Cloud Data Loss Prevention (Cloud DLP) 現已併入機密資料保護。API 名稱維持不變：Cloud Data Loss Prevention API (DLP API)。如要瞭解構成 Sensitive Data Protection 的服務，請參閱「Sensitive Data Protection 總覽」。

本頁面由 Cloud Translation API 翻譯而成。

計算資料集的 k-anonymity

K-anonymity 是指資料集的一個屬性，表示資料集記錄的重新識別性。如果資料集中每個人的準識別項與資料集中至少其他 k- 1 個人相同，則這個資料集就符合 k-anonymous。

您可以根據資料集的一或多個資料欄或欄位，計算 k-anonymity 值。本主題將示範如何使用 Sensitive Data Protection，計算資料集的 k-anonymity 值。如要進一步瞭解 k-anonymity 或一般風險分析，請先參閱風險分析概念主題，然後再繼續閱讀本文。

事前準備

繼續操作前，請務必先完成下列事項：

登入您的 Google 帳戶。
在 Google Cloud 控制台的專案選擇器頁面中，選取或建立 Google Cloud 專案。

前往專案選取器

請確認您已為 Google Cloud 專案啟用計費功能。瞭解如何確認專案已啟用計費功能。
啟用 Sensitive Data Protection。

啟用 Sensitive Data Protection

選取要分析的 BigQuery 資料集。Sensitive Data Protection 會掃描 BigQuery 資料表，計算 k 匿名性指標。
判斷資料集中的識別項 (如適用) 和至少一個準識別項。詳情請參閱「風險分析術語與技術」。

計算 k-anonymity

每當執行風險分析工作時，Sensitive Data Protection 都會執行風險分析。您必須先建立工作，方法是使用Google Cloud 控制台、傳送 DLP API 要求，或使用 Sensitive Data Protection 用戶端程式庫。

控制台

前往 Google Cloud 控制台的「建立風險分析」頁面。

前往「建立風險分析」
在「選擇輸入資料」部分，輸入含有資料表的專案 ID、資料表的資料集 ID 和資料表名稱，指定要掃描的 BigQuery 資料表。
在「要計算的隱私權指標」下方，選取「k-匿名」。
在「Job ID」(工作 ID) 區段中，您可以選擇為工作提供自訂 ID，並選取資源位置，讓 Sensitive Data Protection 處理資料。完成後，按一下「繼續」。
在「定義欄位」部分，指定 k 匿名風險工作的識別項和準識別項。Sensitive Data Protection 會存取您在上一個步驟中指定的 BigQuery 資料表的中繼資料，並嘗試填入欄位清單。
1. 選取適當的核取方塊，將欄位指定為識別項 (ID) 或準識別項 (QI)。您必須選取 0 或 1 個識別項，以及至少 1 個準識別項。
2. 如果「機密資料防護」無法填入欄位，請按一下「輸入欄位名稱」，手動輸入一或多個欄位，並將每個欄位設為識別項或準識別項。完成後，按一下「繼續」。
在「新增動作」部分，您可以新增選用的動作，在風險工作完成時執行。可用的選項如下：

重要事項： 請注意，執行風險分析工作可能會將所選準識別項的引號值保留在風險分析工作結果中。這些引號值用於建構直方圖和輸出表格。
- 儲存至 BigQuery：將風險分析掃描的結果儲存至 BigQuery 表格。
- 發布至 Pub/Sub：將通知發布至 Pub/Sub 主題。
  
  注意： 如果 Pub/Sub 主題有設定或權限問題，Sensitive Data Protection 最多會重試傳送 Pub/Sub 通知兩週。兩週後，系統會捨棄通知。
- 透過電子郵件通知：透過電子郵件傳送結果。完成後，按一下「建立」。

k-anonymity 風險分析作業會立即啟動。

C#

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Google.Cloud.PubSub.V1;
using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
using static Google.Cloud.Dlp.V2.Action.Types;
using static Google.Cloud.Dlp.V2.PrivacyMetric.Types;

public class RiskAnalysisCreateKAnonymity
{
    public static AnalyzeDataSourceRiskDetails.Types.KAnonymityResult KAnonymity(
        string callingProjectId,
        string tableProjectId,
        string datasetId,
        string tableId,
        string topicId,
        string subscriptionId,
        IEnumerable<FieldId> quasiIds)
    {
        var dlp = DlpServiceClient.Create();

        // Construct + submit the job
        var KAnonymityConfig = new KAnonymityConfig
        {
            QuasiIds = { quasiIds }
        };

        var config = new RiskAnalysisJobConfig
        {
            PrivacyMetric = new PrivacyMetric
            {
                KAnonymityConfig = KAnonymityConfig
            },
            SourceTable = new BigQueryTable
            {
                ProjectId = tableProjectId,
                DatasetId = datasetId,
                TableId = tableId
            },
            Actions =
            {
                new Google.Cloud.Dlp.V2.Action
                {
                    PubSub = new PublishToPubSub
                    {
                        Topic = $"projects/{callingProjectId}/topics/{topicId}"
                    }
                }
            }
        };

        var submittedJob = dlp.CreateDlpJob(
            new CreateDlpJobRequest
            {
                ParentAsProjectName = new ProjectName(callingProjectId),
                RiskJob = config
            });

        // Listen to pub/sub for the job
        var subscriptionName = new SubscriptionName(callingProjectId, subscriptionId);
        var subscriber = SubscriberClient.CreateAsync(
            subscriptionName).Result;

        // SimpleSubscriber runs your message handle function on multiple
        // threads to maximize throughput.
        var done = new ManualResetEventSlim(false);
        subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
        {
            if (message.Attributes["DlpJobName"] == submittedJob.Name)
            {
                Thread.Sleep(500); // Wait for DLP API results to become consistent
                done.Set();
                return Task.FromResult(SubscriberClient.Reply.Ack);
            }
            else
            {
                return Task.FromResult(SubscriberClient.Reply.Nack);
            }
        });

        done.Wait(TimeSpan.FromMinutes(10)); // 10 minute timeout; may not work for large jobs
        subscriber.StopAsync(CancellationToken.None).Wait();

        // Process results
        var resultJob = dlp.GetDlpJob(new GetDlpJobRequest
        {
            DlpJobName = DlpJobName.Parse(submittedJob.Name)
        });

        var result = resultJob.RiskDetails.KAnonymityResult;

        for (var bucketIdx = 0; bucketIdx < result.EquivalenceClassHistogramBuckets.Count; bucketIdx++)
        {
            var bucket = result.EquivalenceClassHistogramBuckets[bucketIdx];
            Console.WriteLine($"Bucket {bucketIdx}");
            Console.WriteLine($"  Bucket size range: [{bucket.EquivalenceClassSizeLowerBound}, {bucket.EquivalenceClassSizeUpperBound}].");
            Console.WriteLine($"  {bucket.BucketSize} unique value(s) total.");

            foreach (var bucketValue in bucket.BucketValues)
            {
                // 'UnpackValue(x)' is a prettier version of 'x.toString()'
                Console.WriteLine($"    Quasi-ID values: [{String.Join(',', bucketValue.QuasiIdsValues.Select(x => UnpackValue(x)))}]");
                Console.WriteLine($"    Class size: {bucketValue.EquivalenceClassSize}");
            }
        }

        return result;
    }

    public static string UnpackValue(Value protoValue)
    {
        var jsonValue = JsonConvert.DeserializeObject<Dictionary<string, object>>(protoValue.ToString());
        return jsonValue.Values.ElementAt(0).ToString();
    }
}

Go

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"strings"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
	"cloud.google.com/go/pubsub"
)

// riskKAnonymity computes the risk of the given columns using K Anonymity.
func riskKAnonymity(w io.Writer, projectID, dataProject, pubSubTopic, pubSubSub, datasetID, tableID string, columnNames ...string) error {
	// projectID := "my-project-id"
	// dataProject := "bigquery-public-data"
	// pubSubTopic := "dlp-risk-sample-topic"
	// pubSubSub := "dlp-risk-sample-sub"
	// datasetID := "nhtsa_traffic_fatalities"
	// tableID := "accident_2015"
	// columnNames := "state_number" "county"
	ctx := context.Background()
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("dlp.NewClient: %w", err)
	}

	// Create a PubSub Client used to listen for when the inspect job finishes.
	pubsubClient, err := pubsub.NewClient(ctx, projectID)
	if err != nil {
		return err
	}
	defer pubsubClient.Close()

	// Create a PubSub subscription we can use to listen for messages.
	// Create the Topic if it doesn't exist.
	t := pubsubClient.Topic(pubSubTopic)
	topicExists, err := t.Exists(ctx)
	if err != nil {
		return err
	}
	if !topicExists {
		if t, err = pubsubClient.CreateTopic(ctx, pubSubTopic); err != nil {
			return err
		}
	}

	// Create the Subscription if it doesn't exist.
	s := pubsubClient.Subscription(pubSubSub)
	subExists, err := s.Exists(ctx)
	if err != nil {
		return err
	}
	if !subExists {
		if s, err = pubsubClient.CreateSubscription(ctx, pubSubSub, pubsub.SubscriptionConfig{Topic: t}); err != nil {
			return err
		}
	}

	// topic is the PubSub topic string where messages should be sent.
	topic := "projects/" + projectID + "/topics/" + pubSubTopic

	// Build the QuasiID slice.
	var q []*dlppb.FieldId
	for _, c := range columnNames {
		q = append(q, &dlppb.FieldId{Name: c})
	}

	// Create a configured request.
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_RiskJob{
			RiskJob: &dlppb.RiskAnalysisJobConfig{
				// PrivacyMetric configures what to compute.
				PrivacyMetric: &dlppb.PrivacyMetric{
					Type: &dlppb.PrivacyMetric_KAnonymityConfig_{
						KAnonymityConfig: &dlppb.PrivacyMetric_KAnonymityConfig{
							QuasiIds: q,
						},
					},
				},
				// SourceTable describes where to find the data.
				SourceTable: &dlppb.BigQueryTable{
					ProjectId: dataProject,
					DatasetId: datasetID,
					TableId:   tableID,
				},
				// Send a message to PubSub using Actions.
				Actions: []*dlppb.Action{
					{
						Action: &dlppb.Action_PubSub{
							PubSub: &dlppb.Action_PublishToPubSub{
								Topic: topic,
							},
						},
					},
				},
			},
		},
	}
	// Create the risk job.
	j, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDlpJob: %w", err)
	}
	fmt.Fprintf(w, "Created job: %v\n", j.GetName())

	// Wait for the risk job to finish by waiting for a PubSub message.
	// This only waits for 10 minutes. For long jobs, consider using a truly
	// asynchronous execution model such as Cloud Functions.
	ctx, cancel := context.WithTimeout(ctx, 10*time.Minute)
	defer cancel()
	err = s.Receive(ctx, func(ctx context.Context, msg *pubsub.Message) {
		// If this is the wrong job, do not process the result.
		if msg.Attributes["DlpJobName"] != j.GetName() {
			msg.Nack()
			return
		}
		msg.Ack()
		time.Sleep(500 * time.Millisecond)
		j, err := client.GetDlpJob(ctx, &dlppb.GetDlpJobRequest{
			Name: j.GetName(),
		})
		if err != nil {
			fmt.Fprintf(w, "GetDlpJob: %v", err)
			return
		}
		h := j.GetRiskDetails().GetKAnonymityResult().GetEquivalenceClassHistogramBuckets()
		for i, b := range h {
			fmt.Fprintf(w, "Histogram bucket %v\n", i)
			fmt.Fprintf(w, "  Size range: [%v,%v]\n", b.GetEquivalenceClassSizeLowerBound(), b.GetEquivalenceClassSizeUpperBound())
			fmt.Fprintf(w, "  %v unique values total\n", b.GetBucketSize())
			for _, v := range b.GetBucketValues() {
				var qvs []string
				for _, qv := range v.GetQuasiIdsValues() {
					qvs = append(qvs, qv.String())
				}
				fmt.Fprintf(w, "    QuasiID values: %s\n", strings.Join(qvs, ", "))
				fmt.Fprintf(w, "    Class size: %v\n", v.GetEquivalenceClassSize())
			}
		}
		// Stop listening for more messages.
		cancel()
	})
	if err != nil {
		return fmt.Errorf("Receive: %w", err)
	}
	return nil
}

Java

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.pubsub.v1.AckReplyConsumer;
import com.google.cloud.pubsub.v1.MessageReceiver;
import com.google.cloud.pubsub.v1.Subscriber;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.Action.PublishToPubSub;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult.KAnonymityEquivalenceClass;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult.KAnonymityHistogramBucket;
import com.google.privacy.dlp.v2.BigQueryTable;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.PrivacyMetric;
import com.google.privacy.dlp.v2.PrivacyMetric.KAnonymityConfig;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
import com.google.privacy.dlp.v2.Value;
import com.google.pubsub.v1.ProjectSubscriptionName;
import com.google.pubsub.v1.ProjectTopicName;
import com.google.pubsub.v1.PubsubMessage;
import java.io.IOException;
import java.util.Arrays;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import java.util.stream.Collectors;

@SuppressWarnings("checkstyle:AbbreviationAsWordInName")
class RiskAnalysisKAnonymity {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String datasetId = "your-bigquery-dataset-id";
    String tableId = "your-bigquery-table-id";
    String topicId = "pub-sub-topic";
    String subscriptionId = "pub-sub-subscription";
    calculateKAnonymity(projectId, datasetId, tableId, topicId, subscriptionId);
  }

  public static void calculateKAnonymity(
      String projectId, String datasetId, String tableId, String topicId, String subscriptionId)
      throws ExecutionException, InterruptedException, IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlpServiceClient = DlpServiceClient.create()) {

      // Specify the BigQuery table to analyze
      BigQueryTable bigQueryTable =
          BigQueryTable.newBuilder()
              .setProjectId(projectId)
              .setDatasetId(datasetId)
              .setTableId(tableId)
              .build();

      // These values represent the column names of quasi-identifiers to analyze
      List<String> quasiIds = Arrays.asList("Age", "Mystery");

      // Configure the privacy metric for the job
      List<FieldId> quasiIdFields =
          quasiIds.stream()
              .map(columnName -> FieldId.newBuilder().setName(columnName).build())
              .collect(Collectors.toList());
      KAnonymityConfig kanonymityConfig =
          KAnonymityConfig.newBuilder().addAllQuasiIds(quasiIdFields).build();
      PrivacyMetric privacyMetric =
          PrivacyMetric.newBuilder().setKAnonymityConfig(kanonymityConfig).build();

      // Create action to publish job status notifications over Google Cloud Pub/Sub
      ProjectTopicName topicName = ProjectTopicName.of(projectId, topicId);
      PublishToPubSub publishToPubSub =
          PublishToPubSub.newBuilder().setTopic(topicName.toString()).build();
      Action action = Action.newBuilder().setPubSub(publishToPubSub).build();

      // Configure the risk analysis job to perform
      RiskAnalysisJobConfig riskAnalysisJobConfig =
          RiskAnalysisJobConfig.newBuilder()
              .setSourceTable(bigQueryTable)
              .setPrivacyMetric(privacyMetric)
              .addActions(action)
              .build();

      // Build the request to be sent by the client
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setRiskJob(riskAnalysisJobConfig)
              .build();

      // Send the request to the API using the client
      DlpJob dlpJob = dlpServiceClient.createDlpJob(createDlpJobRequest);

      // Set up a Pub/Sub subscriber to listen on the job completion status
      final SettableApiFuture<Boolean> done = SettableApiFuture.create();

      ProjectSubscriptionName subscriptionName =
          ProjectSubscriptionName.of(projectId, subscriptionId);

      MessageReceiver messageHandler =
          (PubsubMessage pubsubMessage, AckReplyConsumer ackReplyConsumer) -> {
            handleMessage(dlpJob, done, pubsubMessage, ackReplyConsumer);
          };
      Subscriber subscriber = Subscriber.newBuilder(subscriptionName, messageHandler).build();
      subscriber.startAsync();

      // Wait for job completion semi-synchronously
      // For long jobs, consider using a truly asynchronous execution model such as Cloud Functions
      try {
        done.get(15, TimeUnit.MINUTES);
      } catch (TimeoutException e) {
        System.out.println("Job was not completed after 15 minutes.");
        return;
      } finally {
        subscriber.stopAsync();
        subscriber.awaitTerminated();
      }

      // Build a request to get the completed job
      GetDlpJobRequest getDlpJobRequest =
          GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();

      // Retrieve completed job status
      DlpJob completedJob = dlpServiceClient.getDlpJob(getDlpJobRequest);
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());

      // Get the result and parse through and process the information
      KAnonymityResult kanonymityResult = completedJob.getRiskDetails().getKAnonymityResult();
      List<KAnonymityHistogramBucket> histogramBucketList =
          kanonymityResult.getEquivalenceClassHistogramBucketsList();
      for (KAnonymityHistogramBucket result : histogramBucketList) {
        System.out.printf(
            "Bucket size range: [%d, %d]\n",
            result.getEquivalenceClassSizeLowerBound(), result.getEquivalenceClassSizeUpperBound());

        for (KAnonymityEquivalenceClass bucket : result.getBucketValuesList()) {
          List<String> quasiIdValues =
              bucket.getQuasiIdsValuesList().stream()
                  .map(Value::toString)
                  .collect(Collectors.toList());

          System.out.println("\tQuasi-ID values: " + String.join(", ", quasiIdValues));
          System.out.println("\tClass size: " + bucket.getEquivalenceClassSize());
        }
      }
    }
  }

  // handleMessage injects the job and settableFuture into the message reciever interface
  private static void handleMessage(
      DlpJob job,
      SettableApiFuture<Boolean> done,
      PubsubMessage pubsubMessage,
      AckReplyConsumer ackReplyConsumer) {
    String messageAttribute = pubsubMessage.getAttributesMap().get("DlpJobName");
    if (job.getName().equals(messageAttribute)) {
      done.set(true);
      ackReplyConsumer.ack();
    } else {
      ackReplyConsumer.nack();
    }
  }
}

Node.js

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

// Import the Google Cloud client libraries
const DLP = require('@google-cloud/dlp');
const {PubSub} = require('@google-cloud/pubsub');

// Instantiates clients
const dlp = new DLP.DlpServiceClient();
const pubsub = new PubSub();

// The project ID to run the API call under
// const projectId = 'my-project';

// The project ID the table is stored under
// This may or (for public datasets) may not equal the calling project ID
// const tableProjectId = 'my-project';

// The ID of the dataset to inspect, e.g. 'my_dataset'
// const datasetId = 'my_dataset';

// The ID of the table to inspect, e.g. 'my_table'
// const tableId = 'my_table';

// The name of the Pub/Sub topic to notify once the job completes
// TODO(developer): create a Pub/Sub topic to use for this
// const topicId = 'MY-PUBSUB-TOPIC'

// The name of the Pub/Sub subscription to use when listening for job
// completion notifications
// TODO(developer): create a Pub/Sub subscription to use for this
// const subscriptionId = 'MY-PUBSUB-SUBSCRIPTION'

// A set of columns that form a composite key ('quasi-identifiers')
// const quasiIds = [{ name: 'age' }, { name: 'city' }];
async function kAnonymityAnalysis() {
  const sourceTable = {
    projectId: tableProjectId,
    datasetId: datasetId,
    tableId: tableId,
  };
  // Construct request for creating a risk analysis job

  const request = {
    parent: `projects/${projectId}/locations/global`,
    riskJob: {
      privacyMetric: {
        kAnonymityConfig: {
          quasiIds: quasiIds,
        },
      },
      sourceTable: sourceTable,
      actions: [
        {
          pubSub: {
            topic: `projects/${projectId}/topics/${topicId}`,
          },
        },
      ],
    },
  };

  // Create helper function for unpacking values
  const getValue = obj => obj[Object.keys(obj)[0]];

  // Run risk analysis job
  const [topicResponse] = await pubsub.topic(topicId).get();
  const subscription = await topicResponse.subscription(subscriptionId);
  const [jobsResponse] = await dlp.createDlpJob(request);
  const jobName = jobsResponse.name;
  console.log(`Job created. Job name: ${jobName}`);
  // Watch the Pub/Sub topic until the DLP job finishes
  await new Promise((resolve, reject) => {
    const messageHandler = message => {
      if (message.attributes && message.attributes.DlpJobName === jobName) {
        message.ack();
        subscription.removeListener('message', messageHandler);
        subscription.removeListener('error', errorHandler);
        resolve(jobName);
      } else {
        message.nack();
      }
    };

    const errorHandler = err => {
      subscription.removeListener('message', messageHandler);
      subscription.removeListener('error', errorHandler);
      reject(err);
    };

    subscription.on('message', messageHandler);
    subscription.on('error', errorHandler);
  });
  setTimeout(() => {
    console.log(' Waiting for DLP job to fully complete');
  }, 500);
  const [job] = await dlp.getDlpJob({name: jobName});
  const histogramBuckets =
    job.riskDetails.kAnonymityResult.equivalenceClassHistogramBuckets;

  histogramBuckets.forEach((histogramBucket, histogramBucketIdx) => {
    console.log(`Bucket ${histogramBucketIdx}:`);
    console.log(
      `  Bucket size range: [${histogramBucket.equivalenceClassSizeLowerBound}, ${histogramBucket.equivalenceClassSizeUpperBound}]`
    );

    histogramBucket.bucketValues.forEach(valueBucket => {
      const quasiIdValues = valueBucket.quasiIdsValues
        .map(getValue)
        .join(', ');
      console.log(`  Quasi-ID values: {${quasiIdValues}}`);
      console.log(`  Class size: ${valueBucket.equivalenceClassSize}`);
    });
  });
}
await kAnonymityAnalysis();

PHP

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

use Google\Cloud\Dlp\V2\RiskAnalysisJobConfig;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\PrivacyMetric\KAnonymityConfig;
use Google\Cloud\PubSub\PubSubClient;

/**
 * Computes the k-anonymity of a column set in a Google BigQuery table.
 *
 * @param string    $callingProjectId  The project ID to run the API call under
 * @param string    $dataProjectId     The project ID containing the target Datastore
 * @param string    $topicId           The name of the Pub/Sub topic to notify once the job completes
 * @param string    $subscriptionId    The name of the Pub/Sub subscription to use when listening for job
 * @param string    $datasetId         The ID of the dataset to inspect
 * @param string    $tableId           The ID of the table to inspect
 * @param string[]  $quasiIdNames      Array columns that form a composite key (quasi-identifiers)
 */
function k_anonymity(
    string $callingProjectId,
    string $dataProjectId,
    string $topicId,
    string $subscriptionId,
    string $datasetId,
    string $tableId,
    array $quasiIdNames
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();
    $pubsub = new PubSubClient();
    $topic = $pubsub->topic($topicId);

    // Construct risk analysis config
    $quasiIds = array_map(
        function ($id) {
            return (new FieldId())->setName($id);
        },
        $quasiIdNames
    );

    $statsConfig = (new KAnonymityConfig())
        ->setQuasiIds($quasiIds);

    $privacyMetric = (new PrivacyMetric())
        ->setKAnonymityConfig($statsConfig);

    // Construct items to be analyzed
    $bigqueryTable = (new BigQueryTable())
        ->setProjectId($dataProjectId)
        ->setDatasetId($datasetId)
        ->setTableId($tableId);

    // Construct the action to run when job completes
    $pubSubAction = (new PublishToPubSub())
        ->setTopic($topic->name());

    $action = (new Action())
        ->setPubSub($pubSubAction);

    // Construct risk analysis job config to run
    $riskJob = (new RiskAnalysisJobConfig())
        ->setPrivacyMetric($privacyMetric)
        ->setSourceTable($bigqueryTable)
        ->setActions([$action]);

    // Listen for job notifications via an existing topic/subscription.
    $subscription = $topic->subscription($subscriptionId);

    // Submit request
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setRiskJob($riskJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    // Poll Pub/Sub using exponential backoff until job finishes
    // Consider using an asynchronous execution model such as Cloud Functions
    $attempt = 1;
    $startTime = time();
    do {
        foreach ($subscription->pull() as $message) {
            if (
                isset($message->attributes()['DlpJobName']) &&
                $message->attributes()['DlpJobName'] === $job->getName()
            ) {
                $subscription->acknowledge($message);
                // Get the updated job. Loop to avoid race condition with DLP API.
                do {
                    $getDlpJobRequest = (new GetDlpJobRequest())
                        ->setName($job->getName());
                    $job = $dlp->getDlpJob($getDlpJobRequest);
                } while ($job->getState() == JobState::RUNNING);
                break 2; // break from parent do while
            }
        }
        print('Waiting for job to complete' . PHP_EOL);
        // Exponential backoff with max delay of 60 seconds
        sleep(min(60, pow(2, ++$attempt)));
    } while (time() - $startTime < 600); // 10 minute timeout

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $histBuckets = $job->getRiskDetails()->getKAnonymityResult()->getEquivalenceClassHistogramBuckets();

            foreach ($histBuckets as $bucketIndex => $histBucket) {
                // Print bucket stats
                printf('Bucket %s:' . PHP_EOL, $bucketIndex);
                printf(
                    '  Bucket size range: [%s, %s]' . PHP_EOL,
                    $histBucket->getEquivalenceClassSizeLowerBound(),
                    $histBucket->getEquivalenceClassSizeUpperBound()
                );

                // Print bucket values
                foreach ($histBucket->getBucketValues() as $percent => $valueBucket) {
                    // Pretty-print quasi-ID values
                    print('  Quasi-ID values:' . PHP_EOL);
                    foreach ($valueBucket->getQuasiIdsValues() as $index => $value) {
                        print('    ' . $value->serializeToJsonString() . PHP_EOL);
                    }
                    printf(
                        '  Class size: %s' . PHP_EOL,
                        $valueBucket->getEquivalenceClassSize()
                    );
                }
            }

            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            print('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            print('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

Python

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


import concurrent.futures

from typing import List

import google.cloud.dlp
from google.cloud.dlp_v2 import types
import google.cloud.pubsub


def k_anonymity_analysis(
    project: str,
    table_project_id: str,
    dataset_id: str,
    table_id: str,
    topic_id: str,
    subscription_id: str,
    quasi_ids: List[str],
    timeout: int = 300,
) -> None:
    """Uses the Data Loss Prevention API to compute the k-anonymity of a
        column set in a Google BigQuery table.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        table_project_id: The Google Cloud project id where the BigQuery table
            is stored.
        dataset_id: The id of the dataset to inspect.
        table_id: The id of the table to inspect.
        topic_id: The name of the Pub/Sub topic to notify once the job
            completes.
        subscription_id: The name of the Pub/Sub subscription to use when
            listening for job completion notifications.
        quasi_ids: A set of columns that form a composite key.
        timeout: The number of seconds to wait for a response from the API.

    Returns:
        None; the response from the API is printed to the terminal.
    """

    # Create helper function for unpacking values
    def get_values(obj: types.Value) -> int:
        return int(obj.integer_value)

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Convert the project id into a full resource id.
    topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
    parent = f"projects/{project}/locations/global"

    # Location info of the BigQuery table.
    source_table = {
        "project_id": table_project_id,
        "dataset_id": dataset_id,
        "table_id": table_id,
    }

    # Convert quasi id list to Protobuf type
    def map_fields(field: str) -> dict:
        return {"name": field}

    quasi_ids = map(map_fields, quasi_ids)

    # Tell the API where to send a notification when the job is complete.
    actions = [{"pub_sub": {"topic": topic}}]

    # Configure risk analysis job
    # Give the name of the numeric column to compute risk metrics for
    risk_job = {
        "privacy_metric": {"k_anonymity_config": {"quasi_ids": quasi_ids}},
        "source_table": source_table,
        "actions": actions,
    }

    # Call API to start risk analysis job
    operation = dlp.create_dlp_job(request={"parent": parent, "risk_job": risk_job})

    def callback(message: google.cloud.pubsub_v1.subscriber.message.Message) -> None:
        if message.attributes["DlpJobName"] == operation.name:
            # This is the message we're looking for, so acknowledge it.
            message.ack()

            # Now that the job is done, fetch the results and print them.
            job = dlp.get_dlp_job(request={"name": operation.name})
            print(f"Job name: {job.name}")
            histogram_buckets = (
                job.risk_details.k_anonymity_result.equivalence_class_histogram_buckets
            )
            # Print bucket stats
            for i, bucket in enumerate(histogram_buckets):
                print(f"Bucket {i}:")
                if bucket.equivalence_class_size_lower_bound:
                    print(
                        "   Bucket size range: [{}, {}]".format(
                            bucket.equivalence_class_size_lower_bound,
                            bucket.equivalence_class_size_upper_bound,
                        )
                    )
                    for value_bucket in bucket.bucket_values:
                        print(
                            "   Quasi-ID values: {}".format(
                                map(get_values, value_bucket.quasi_ids_values)
                            )
                        )
                        print(
                            "   Class size: {}".format(
                                value_bucket.equivalence_class_size
                            )
                        )
            subscription.set_result(None)
        else:
            # This is not the message we're looking for.
            message.drop()

    # Create a Pub/Sub client and find the subscription. The subscription is
    # expected to already be listening to the topic.
    subscriber = google.cloud.pubsub.SubscriberClient()
    subscription_path = subscriber.subscription_path(project, subscription_id)
    subscription = subscriber.subscribe(subscription_path, callback)

    try:
        subscription.result(timeout=timeout)
    except concurrent.futures.TimeoutError:
        print(
            "No event received before the timeout. Please verify that the "
            "subscription provided is subscribed to the topic provided."
        )
        subscription.close()

REST

如要執行新的風險分析工作來計算 k-anonymity，請將要求傳送至 projects.dlpJobs 資源，其中 PROJECT_ID 表示您的專案 ID：

https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs

要求會包含由以下項目組成的 RiskAnalysisJobConfig 物件：

A PrivacyMetric 物件。您可以在這裡包含 KAnonymityConfig 物件，指定要計算 k-anonymity。
BigQueryTable 物件。包含以下所有項目以指定要掃描的 BigQuery 表格：
- projectId：包含表格的專案 ID。
- datasetId：資料表的資料集 ID。
- tableId：資料表名稱。
一或多個 Action 物件的組合，代表完成工作時要按照指定順序執行的動作。每個 Action 物件都可包含以下其中一個動作：
- SaveFindings 物件：將風險分析掃描的結果儲存至 BigQuery 表格。
- PublishToPubSub object: 將通知發布至 Pub/Sub 主題。
  
  注意： 如果 Pub/Sub 主題有設定或權限問題，Sensitive Data Protection 最多會重試傳送 Pub/Sub 通知兩週。兩週後，系統會捨棄通知。
- JobNotificationEmails 物件：傳送內含結果的電子郵件給你。
您可在 KAnonymityConfig 物件中指定以下項目：

重要事項： 請注意，執行風險分析工作可能會將所選準 ID 的部分「引號」值保留在風險分析工作結果中。這些引號值用於建構直方圖和輸出資料表。
- quasiIds[]：一或多個準 ID (FieldId 物件)，用於掃描及計算 k-anonymity。當您指定多個準識別項時，系統會將這些準識別項視為單一複合式金鑰。不支援結構和重複資料類型，但可支援巢狀欄位 (只要它們本身並非結構或某個重複欄位內含的巢狀結構)。
- entityId：選用 ID 值，設定這個項目時，即表示應針對 k-anonymity 計算，將對應到每個獨立 entityId 的所有資料列分在一組。entityId 通常會是表示不重複使用者的資料欄，例如客戶 ID 或使用者 ID。當 entityId 出現在多個具有不同準識別項值的資料列時，這些資料列會加以彙整形成一個多重集合，用來當做這個實體的準識別項。如要進一步瞭解實體 ID，請參閱風險分析概念主題中的「實體 ID 及計算 k-anonymity」一節。

只要您將要求傳送至 DLP API，系統就會啟動風險分析工作。

列出已完成的風險分析工作

您可以查看目前專案中已執行的風險分析工作清單。

控制台

如要在Google Cloud 控制台中列出正在執行和先前執行的風險分析工作，請執行下列操作：

在 Google Cloud 控制台中，開啟 Sensitive Data Protection。

前往 Sensitive Data Protection
按一下頁面頂端的「工作和工作觸發條件」分頁標籤。
按一下「風險工作」分頁標籤。

系統會顯示有風險的工作清單。

通訊協定

如要列出正在執行和先前執行的風險分析工作，請將 GET 要求傳送至projects.dlpJobs 資源。新增工作類型篩選器 (?type=RISK_ANALYSIS_JOB) 可將回應範圍縮小至僅限風險分析工作。

https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs?type=RISK_ANALYSIS_JOB

您收到的回應包含所有目前和先前風險分析工作的 JSON 表示法。

查看 k-anonymity 工作結果

Google Cloud 控制台中的 Sensitive Data Protection 內建已完成 k 匿名化工作的視覺化功能。按照上一節的指示操作後，從風險分析工作清單中，選取要查看結果的工作。假設工作已順利執行，則「風險分析詳細資料」頁面頂端會顯示如下內容：

頁面頂端會顯示 k 匿名風險作業的相關資訊，包括作業 ID，以及「容器」下方的資源位置。

如要查看 k-anonymity 計算結果，請按一下「K-anonymity」分頁標籤。如要查看風險分析工作的設定，請按一下「設定」分頁標籤。

「K-anonymity」分頁會先列出實體 ID (如有) 和用於計算 k-anonymity 的準識別碼。

風險圖表

「重新識別風險」圖表會在 y 軸上，繪製出為達成 x 軸上的 k-anonymity 值，獨特資料列和獨特準識別項組合可能損失的資料百分比。圖表的顏色也會顯示潛在風險。藍色越深代表風險越高，藍色越淺代表風險越低。

k-anonymity 值越高，表示重新識別的風險越低。不過，如要達到更高的 k-anonymity 值，您需要移除更高百分比的總列數和更高比例的準 ID 組合，這可能會降低資料的實用性。如要查看特定 k-anonymity 值對應的可能資料遺失百分比，請將游標懸停在圖表上。如螢幕截圖所示，圖表上會顯示工具提示。

如要查看特定 k-anonymity 值的詳細資料，請按一下對應的資料點。圖表下方會顯示詳細說明，頁面下方則會顯示範例資料表。

風險樣本資料表

風險分析工作結果頁面的第二個元件是資料表樣本。並顯示特定目標 k-anonymity 值的準識別項組合。

表格的第一欄會列出 k-匿名值。按一下 k-anonymity 值，即可查看為達到該值而需要捨棄的對應樣本資料。

第二欄會顯示不重複資料列和準 ID 組合的潛在資料遺失情形，以及至少有 k 筆記錄的群組數量和記錄總數。

最後一欄會顯示共用準 ID 組合的群組樣本，以及該組合的記錄數。

使用 REST 擷取工作詳細資料

如要使用 REST API 擷取 k-anonymity 風險分析工作的結果，請將下列 GET 要求傳送至 projects.dlpJobs 資源。將 PROJECT_ID 替換為專案 ID，並將 JOB_ID 替換為要取得結果的工作 ID。工作 ID 會在您啟動工作時傳回，您也可以列出所有工作來擷取 ID。

GET https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs/JOB_ID

要求會傳回包含工作例項的 JSON 物件。分析結果位於 "riskDetails" 鍵中，以 AnalyzeDataSourceRiskDetails 物件的形式呈現。詳情請參閱 DlpJob 資源的 API 參考資料。

程式碼範例：使用實體 ID 計算 k-anonymity

這個範例會建立風險分析工作，計算實體 ID 的 k-匿名性。

如要進一步瞭解實體 ID，請參閱「實體 ID 及計算 k-anonymity」一節。

C#

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


using System;
using System.Collections.Generic;
using System.Linq;
using Google.Api.Gax.ResourceNames;
using Google.Cloud.Dlp.V2;
using Newtonsoft.Json;

public class CalculateKAnonymityOnDataset
{
    public static DlpJob CalculateKAnonymitty(
        string projectId,
        string datasetId,
        string sourceTableId,
        string outputTableId)
    {
        // Construct the dlp client.
        var dlp = DlpServiceClient.Create();

        // Construct the k-anonymity config by setting the EntityId as user_id column
        // and two quasi-identifiers columns.
        var kAnonymity = new PrivacyMetric.Types.KAnonymityConfig
        {
            EntityId = new EntityId
            {
                Field = new FieldId { Name = "Name" }
            },
            QuasiIds =
            {
                new FieldId { Name = "Age" },
                new FieldId { Name = "Mystery" }
            }
        };

        // Construct risk analysis job config by providing the source table, privacy metric
        // and action to save the findings to a BigQuery table.
        var riskJob = new RiskAnalysisJobConfig
        {
            SourceTable = new BigQueryTable
            {
                ProjectId = projectId,
                DatasetId = datasetId,
                TableId = sourceTableId,
            },
            PrivacyMetric = new PrivacyMetric
            {
                KAnonymityConfig = kAnonymity,
            },
            Actions =
            {
                new Google.Cloud.Dlp.V2.Action
                {
                    SaveFindings = new Google.Cloud.Dlp.V2.Action.Types.SaveFindings
                    {
                        OutputConfig = new OutputStorageConfig
                        {
                            Table = new BigQueryTable
                            {
                                ProjectId = projectId,
                                DatasetId = datasetId,
                                TableId = outputTableId
                            }
                        }
                    }
                }
            }
        };

        // Construct the request by providing RiskJob object created above.
        var request = new CreateDlpJobRequest
        {
            ParentAsLocationName = new LocationName(projectId, "global"),
            RiskJob = riskJob
        };

        // Send the job request.
        DlpJob response = dlp.CreateDlpJob(request);

        Console.WriteLine($"Job created successfully. Job name: ${response.Name}");

        return response;
    }
}

Go

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"strings"
	"time"

	dlp "cloud.google.com/go/dlp/apiv2"
	"cloud.google.com/go/dlp/apiv2/dlppb"
)

// Uses the Data Loss Prevention API to compute the k-anonymity of a
// column set in a Google BigQuery table.
func calculateKAnonymityWithEntityId(w io.Writer, projectID, datasetId, tableId string, columnNames ...string) error {
	// projectID := "your-project-id"
	// datasetId := "your-bigquery-dataset-id"
	// tableId := "your-bigquery-table-id"
	// columnNames := "age" "job_title"

	ctx := context.Background()

	// Initialize a client once and reuse it to send multiple requests. Clients
	// are safe to use across goroutines. When the client is no longer needed,
	// call the Close method to cleanup its resources.
	client, err := dlp.NewClient(ctx)
	if err != nil {
		return err
	}

	// Closing the client safely cleans up background resources.
	defer client.Close()

	// Specify the BigQuery table to analyze
	bigQueryTable := &dlppb.BigQueryTable{
		ProjectId: "bigquery-public-data",
		DatasetId: "samples",
		TableId:   "wikipedia",
	}

	// Configure the privacy metric for the job
	// Build the QuasiID slice.
	var q []*dlppb.FieldId
	for _, c := range columnNames {
		q = append(q, &dlppb.FieldId{Name: c})
	}

	entityId := &dlppb.EntityId{
		Field: &dlppb.FieldId{
			Name: "id",
		},
	}

	kAnonymityConfig := &dlppb.PrivacyMetric_KAnonymityConfig{
		QuasiIds: q,
		EntityId: entityId,
	}

	privacyMetric := &dlppb.PrivacyMetric{
		Type: &dlppb.PrivacyMetric_KAnonymityConfig_{
			KAnonymityConfig: kAnonymityConfig,
		},
	}

	// Specify the bigquery table to store the findings.
	// The "test_results" table in the given BigQuery dataset will be created if it doesn't
	// already exist.
	outputbigQueryTable := &dlppb.BigQueryTable{
		ProjectId: projectID,
		DatasetId: datasetId,
		TableId:   tableId,
	}

	// Create action to publish job status notifications to BigQuery table.
	outputStorageConfig := &dlppb.OutputStorageConfig{
		Type: &dlppb.OutputStorageConfig_Table{
			Table: outputbigQueryTable,
		},
	}

	findings := &dlppb.Action_SaveFindings{
		OutputConfig: outputStorageConfig,
	}

	action := &dlppb.Action{
		Action: &dlppb.Action_SaveFindings_{
			SaveFindings: findings,
		},
	}

	// Configure the risk analysis job to perform
	riskAnalysisJobConfig := &dlppb.RiskAnalysisJobConfig{
		PrivacyMetric: privacyMetric,
		SourceTable:   bigQueryTable,
		Actions: []*dlppb.Action{
			action,
		},
	}

	// Build the request to be sent by the client
	req := &dlppb.CreateDlpJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Job: &dlppb.CreateDlpJobRequest_RiskJob{
			RiskJob: riskAnalysisJobConfig,
		},
	}

	// Send the request to the API using the client
	dlpJob, err := client.CreateDlpJob(ctx, req)
	if err != nil {
		return err
	}
	fmt.Fprintf(w, "Created job: %v\n", dlpJob.GetName())

	// Build a request to get the completed job
	getDlpJobReq := &dlppb.GetDlpJobRequest{
		Name: dlpJob.Name,
	}

	timeout := 15 * time.Minute
	startTime := time.Now()

	var completedJob *dlppb.DlpJob

	// Wait for job completion
	for time.Since(startTime) <= timeout {
		completedJob, err = client.GetDlpJob(ctx, getDlpJobReq)
		if err != nil {
			return err
		}

		if completedJob.GetState() == dlppb.DlpJob_DONE {
			break
		}

		time.Sleep(30 * time.Second)

	}

	if completedJob.GetState() != dlppb.DlpJob_DONE {
		fmt.Println("Job did not complete within 15 minutes.")
	}

	// Retrieve completed job status
	fmt.Fprintf(w, "Job status: %v", completedJob.State)
	fmt.Fprintf(w, "Job name: %v", dlpJob.Name)

	// Get the result and parse through and process the information
	kanonymityResult := completedJob.GetRiskDetails().GetKAnonymityResult()

	for _, result := range kanonymityResult.GetEquivalenceClassHistogramBuckets() {
		fmt.Fprintf(w, "Bucket size range: [%d, %d]\n", result.GetEquivalenceClassSizeLowerBound(), result.GetEquivalenceClassSizeLowerBound())

		for _, bucket := range result.GetBucketValues() {
			quasiIdValues := []string{}
			for _, v := range bucket.GetQuasiIdsValues() {
				quasiIdValues = append(quasiIdValues, v.GetStringValue())
			}
			fmt.Fprintf(w, "\tQuasi-ID values: %s", strings.Join(quasiIdValues, ","))
			fmt.Fprintf(w, "\tClass size: %d", bucket.EquivalenceClassSize)
		}
	}

	return nil

}

Java

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.privacy.dlp.v2.Action;
import com.google.privacy.dlp.v2.Action.SaveFindings;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult.KAnonymityEquivalenceClass;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KAnonymityResult.KAnonymityHistogramBucket;
import com.google.privacy.dlp.v2.BigQueryTable;
import com.google.privacy.dlp.v2.CreateDlpJobRequest;
import com.google.privacy.dlp.v2.DlpJob;
import com.google.privacy.dlp.v2.EntityId;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.GetDlpJobRequest;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.OutputStorageConfig;
import com.google.privacy.dlp.v2.PrivacyMetric;
import com.google.privacy.dlp.v2.PrivacyMetric.KAnonymityConfig;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
import com.google.privacy.dlp.v2.Value;
import java.io.IOException;
import java.time.Duration;
import java.util.Arrays;
import java.util.List;
import java.util.concurrent.TimeUnit;
import java.util.stream.Collectors;

@SuppressWarnings("checkstyle:AbbreviationAsWordInName")
public class RiskAnalysisKAnonymityWithEntityId {

  public static void main(String[] args) throws IOException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    // The Google Cloud project id to use as a parent resource.
    String projectId = "your-project-id";
    // The BigQuery dataset id to be used and the reference table name to be inspected.
    String datasetId = "your-bigquery-dataset-id";
    String tableId = "your-bigquery-table-id";
    calculateKAnonymityWithEntityId(projectId, datasetId, tableId);
  }

  // Uses the Data Loss Prevention API to compute the k-anonymity of a column set in a Google
  // BigQuery table.
  public static void calculateKAnonymityWithEntityId(
      String projectId, String datasetId, String tableId) throws IOException, InterruptedException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (DlpServiceClient dlpServiceClient = DlpServiceClient.create()) {

      // Specify the BigQuery table to analyze
      BigQueryTable bigQueryTable =
          BigQueryTable.newBuilder()
              .setProjectId(projectId)
              .setDatasetId(datasetId)
              .setTableId(tableId)
              .build();

      // These values represent the column names of quasi-identifiers to analyze
      List<String> quasiIds = Arrays.asList("Age", "Mystery");

      // Create a list of FieldId objects based on the provided list of column names.
      List<FieldId> quasiIdFields =
          quasiIds.stream()
              .map(columnName -> FieldId.newBuilder().setName(columnName).build())
              .collect(Collectors.toList());

      // Specify the unique identifier in the source table for the k-anonymity analysis.
      FieldId uniqueIdField = FieldId.newBuilder().setName("Name").build();
      EntityId entityId = EntityId.newBuilder().setField(uniqueIdField).build();
      KAnonymityConfig kanonymityConfig = KAnonymityConfig.newBuilder()
              .addAllQuasiIds(quasiIdFields)
              .setEntityId(entityId)
              .build();

      // Configure the privacy metric to compute for re-identification risk analysis.
      PrivacyMetric privacyMetric =
          PrivacyMetric.newBuilder().setKAnonymityConfig(kanonymityConfig).build();

      // Specify the bigquery table to store the findings.
      // The "test_results" table in the given BigQuery dataset will be created if it doesn't
      // already exist.
      BigQueryTable outputbigQueryTable =
          BigQueryTable.newBuilder()
              .setProjectId(projectId)
              .setDatasetId(datasetId)
              .setTableId("test_results")
              .build();

      // Create action to publish job status notifications to BigQuery table.
      OutputStorageConfig outputStorageConfig =
          OutputStorageConfig.newBuilder().setTable(outputbigQueryTable).build();
      SaveFindings findings =
          SaveFindings.newBuilder().setOutputConfig(outputStorageConfig).build();
      Action action = Action.newBuilder().setSaveFindings(findings).build();

      // Configure the risk analysis job to perform
      RiskAnalysisJobConfig riskAnalysisJobConfig =
          RiskAnalysisJobConfig.newBuilder()
              .setSourceTable(bigQueryTable)
              .setPrivacyMetric(privacyMetric)
              .addActions(action)
              .build();

      // Build the request to be sent by the client
      CreateDlpJobRequest createDlpJobRequest =
          CreateDlpJobRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setRiskJob(riskAnalysisJobConfig)
              .build();

      // Send the request to the API using the client
      DlpJob dlpJob = dlpServiceClient.createDlpJob(createDlpJobRequest);

      // Build a request to get the completed job
      GetDlpJobRequest getDlpJobRequest =
          GetDlpJobRequest.newBuilder().setName(dlpJob.getName()).build();

      DlpJob completedJob = null;
      // Wait for job completion
      try {
        Duration timeout = Duration.ofMinutes(15);
        long startTime = System.currentTimeMillis();
        do {
          completedJob = dlpServiceClient.getDlpJob(getDlpJobRequest);
          TimeUnit.SECONDS.sleep(30);
        } while (completedJob.getState() != DlpJob.JobState.DONE
            && System.currentTimeMillis() - startTime <= timeout.toMillis());
      } catch (InterruptedException e) {
        System.out.println("Job did not complete within 15 minutes.");
      }

      // Retrieve completed job status
      System.out.println("Job status: " + completedJob.getState());
      System.out.println("Job name: " + dlpJob.getName());

      // Get the result and parse through and process the information
      KAnonymityResult kanonymityResult = completedJob.getRiskDetails().getKAnonymityResult();
      for (KAnonymityHistogramBucket result :
          kanonymityResult.getEquivalenceClassHistogramBucketsList()) {
        System.out.printf(
            "Bucket size range: [%d, %d]\n",
            result.getEquivalenceClassSizeLowerBound(), result.getEquivalenceClassSizeUpperBound());

        for (KAnonymityEquivalenceClass bucket : result.getBucketValuesList()) {
          List<String> quasiIdValues =
              bucket.getQuasiIdsValuesList().stream()
                  .map(Value::toString)
                  .collect(Collectors.toList());

          System.out.println("\tQuasi-ID values: " + String.join(", ", quasiIdValues));
          System.out.println("\tClass size: " + bucket.getEquivalenceClassSize());
        }
      }
    }
  }
}

Node.js

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

// Imports the Google Cloud Data Loss Prevention library
const DLP = require('@google-cloud/dlp');

// Instantiates a client
const dlp = new DLP.DlpServiceClient();

// The project ID to run the API call under.
// const projectId = "your-project-id";

// The ID of the dataset to inspect, e.g. 'my_dataset'
// const datasetId = 'my_dataset';

// The ID of the table to inspect, e.g. 'my_table'
// const sourceTableId = 'my_source_table';

// The ID of the table where outputs are stored
// const outputTableId = 'my_output_table';

async function kAnonymityWithEntityIds() {
  // Specify the BigQuery table to analyze.
  const sourceTable = {
    projectId: projectId,
    datasetId: datasetId,
    tableId: sourceTableId,
  };

  // Specify the unique identifier in the source table for the k-anonymity analysis.
  const uniqueIdField = {name: 'Name'};

  // These values represent the column names of quasi-identifiers to analyze
  const quasiIds = [{name: 'Age'}, {name: 'Mystery'}];

  // Configure the privacy metric to compute for re-identification risk analysis.
  const privacyMetric = {
    kAnonymityConfig: {
      entityId: {
        field: uniqueIdField,
      },
      quasiIds: quasiIds,
    },
  };
  // Create action to publish job status notifications to BigQuery table.
  const action = [
    {
      saveFindings: {
        outputConfig: {
          table: {
            projectId: projectId,
            datasetId: datasetId,
            tableId: outputTableId,
          },
        },
      },
    },
  ];

  // Configure the risk analysis job to perform.
  const riskAnalysisJob = {
    sourceTable: sourceTable,
    privacyMetric: privacyMetric,
    actions: action,
  };
  // Combine configurations into a request for the service.
  const createDlpJobRequest = {
    parent: `projects/${projectId}/locations/global`,
    riskJob: riskAnalysisJob,
  };

  // Send the request and receive response from the service
  const [createdDlpJob] = await dlp.createDlpJob(createDlpJobRequest);
  const jobName = createdDlpJob.name;

  // Waiting for a maximum of 15 minutes for the job to get complete.
  let job;
  let numOfAttempts = 30;
  while (numOfAttempts > 0) {
    // Fetch DLP Job status
    [job] = await dlp.getDlpJob({name: jobName});

    // Check if the job has completed.
    if (job.state === 'DONE') {
      break;
    }
    if (job.state === 'FAILED') {
      console.log('Job Failed, Please check the configuration.');
      return;
    }
    // Sleep for a short duration before checking the job status again.
    await new Promise(resolve => {
      setTimeout(() => resolve(), 30000);
    });
    numOfAttempts -= 1;
  }

  // Create helper function for unpacking values
  const getValue = obj => obj[Object.keys(obj)[0]];

  // Print out the results.
  const histogramBuckets =
    job.riskDetails.kAnonymityResult.equivalenceClassHistogramBuckets;

  histogramBuckets.forEach((histogramBucket, histogramBucketIdx) => {
    console.log(`Bucket ${histogramBucketIdx}:`);
    console.log(
      `  Bucket size range: [${histogramBucket.equivalenceClassSizeLowerBound}, ${histogramBucket.equivalenceClassSizeUpperBound}]`
    );

    histogramBucket.bucketValues.forEach(valueBucket => {
      const quasiIdValues = valueBucket.quasiIdsValues
        .map(getValue)
        .join(', ');
      console.log(`  Quasi-ID values: {${quasiIdValues}}`);
      console.log(`  Class size: ${valueBucket.equivalenceClassSize}`);
    });
  });
}
await kAnonymityWithEntityIds();

PHP

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\SaveFindings;
use Google\Cloud\Dlp\V2\BigQueryTable;
use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\CreateDlpJobRequest;
use Google\Cloud\Dlp\V2\DlpJob\JobState;
use Google\Cloud\Dlp\V2\EntityId;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\OutputStorageConfig;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\PrivacyMetric\KAnonymityConfig;
use Google\Cloud\Dlp\V2\RiskAnalysisJobConfig;

/**
 * Computes the k-anonymity of a column set in a Google BigQuery table with entity id.
 *
 * @param string    $callingProjectId  The project ID to run the API call under.
 * @param string    $datasetId         The ID of the dataset to inspect.
 * @param string    $tableId           The ID of the table to inspect.
 * @param string[]  $quasiIdNames      Array columns that form a composite key (quasi-identifiers).
 */

function k_anonymity_with_entity_id(
    // TODO(developer): Replace sample parameters before running the code.
    string $callingProjectId,
    string $datasetId,
    string $tableId,
    array  $quasiIdNames
): void {
    // Instantiate a client.
    $dlp = new DlpServiceClient();

    // Specify the BigQuery table to analyze.
    $bigqueryTable = (new BigQueryTable())
        ->setProjectId($callingProjectId)
        ->setDatasetId($datasetId)
        ->setTableId($tableId);

    // Create a list of FieldId objects based on the provided list of column names.
    $quasiIds = array_map(
        function ($id) {
            return (new FieldId())
                ->setName($id);
        },
        $quasiIdNames
    );

    // Specify the unique identifier in the source table for the k-anonymity analysis.
    $statsConfig = (new KAnonymityConfig())
        ->setEntityId((new EntityId())
            ->setField((new FieldId())
                ->setName('Name')))
        ->setQuasiIds($quasiIds);

    // Configure the privacy metric to compute for re-identification risk analysis.
    $privacyMetric = (new PrivacyMetric())
        ->setKAnonymityConfig($statsConfig);

    // Specify the bigquery table to store the findings.
    // The "test_results" table in the given BigQuery dataset will be created if it doesn't
    // already exist.
    $outBigqueryTable = (new BigQueryTable())
        ->setProjectId($callingProjectId)
        ->setDatasetId($datasetId)
        ->setTableId('test_results');

    $outputStorageConfig = (new OutputStorageConfig())
        ->setTable($outBigqueryTable);

    $findings = (new SaveFindings())
        ->setOutputConfig($outputStorageConfig);

    $action = (new Action())
        ->setSaveFindings($findings);

    // Construct risk analysis job config to run.
    $riskJob = (new RiskAnalysisJobConfig())
        ->setPrivacyMetric($privacyMetric)
        ->setSourceTable($bigqueryTable)
        ->setActions([$action]);

    // Submit request.
    $parent = "projects/$callingProjectId/locations/global";
    $createDlpJobRequest = (new CreateDlpJobRequest())
        ->setParent($parent)
        ->setRiskJob($riskJob);
    $job = $dlp->createDlpJob($createDlpJobRequest);

    $numOfAttempts = 10;
    do {
        printf('Waiting for job to complete' . PHP_EOL);
        sleep(10);
        $getDlpJobRequest = (new GetDlpJobRequest())
            ->setName($job->getName());
        $job = $dlp->getDlpJob($getDlpJobRequest);
        if ($job->getState() == JobState::DONE) {
            break;
        }
        $numOfAttempts--;
    } while ($numOfAttempts > 0);

    // Print finding counts
    printf('Job %s status: %s' . PHP_EOL, $job->getName(), JobState::name($job->getState()));
    switch ($job->getState()) {
        case JobState::DONE:
            $histBuckets = $job->getRiskDetails()->getKAnonymityResult()->getEquivalenceClassHistogramBuckets();

            foreach ($histBuckets as $bucketIndex => $histBucket) {
                // Print bucket stats.
                printf('Bucket %s:' . PHP_EOL, $bucketIndex);
                printf(
                    '  Bucket size range: [%s, %s]' . PHP_EOL,
                    $histBucket->getEquivalenceClassSizeLowerBound(),
                    $histBucket->getEquivalenceClassSizeUpperBound()
                );

                // Print bucket values.
                foreach ($histBucket->getBucketValues() as $percent => $valueBucket) {
                    // Pretty-print quasi-ID values.
                    printf('  Quasi-ID values:' . PHP_EOL);
                    foreach ($valueBucket->getQuasiIdsValues() as $index => $value) {
                        print('    ' . $value->serializeToJsonString() . PHP_EOL);
                    }
                    printf(
                        '  Class size: %s' . PHP_EOL,
                        $valueBucket->getEquivalenceClassSize()
                    );
                }
            }

            break;
        case JobState::FAILED:
            printf('Job %s had errors:' . PHP_EOL, $job->getName());
            $errors = $job->getErrors();
            foreach ($errors as $error) {
                var_dump($error->getDetails());
            }
            break;
        case JobState::PENDING:
            printf('Job has not completed. Consider a longer timeout or an asynchronous execution model' . PHP_EOL);
            break;
        default:
            printf('Unexpected job state. Most likely, the job is either running or has not yet started.');
    }
}

Python

如要瞭解如何安裝及使用 Sensitive Data Protection 的用戶端程式庫，請參閱「Sensitive Data Protection 用戶端程式庫」。

如要向 Sensitive Data Protection 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import time
from typing import List

import google.cloud.dlp_v2
from google.cloud.dlp_v2 import types


def k_anonymity_with_entity_id(
    project: str,
    source_table_project_id: str,
    source_dataset_id: str,
    source_table_id: str,
    entity_id: str,
    quasi_ids: List[str],
    output_table_project_id: str,
    output_dataset_id: str,
    output_table_id: str,
) -> None:
    """Uses the Data Loss Prevention API to compute the k-anonymity using entity_id
        of a column set in a Google BigQuery table.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        source_table_project_id: The Google Cloud project id where the BigQuery table
            is stored.
        source_dataset_id: The id of the dataset to inspect.
        source_table_id: The id of the table to inspect.
        entity_id: The column name of the table that enables accurately determining k-anonymity
         in the common scenario wherein several rows of dataset correspond to the same sensitive
         information.
        quasi_ids: A set of columns that form a composite key.
        output_table_project_id: The Google Cloud project id where the output BigQuery table
            is stored.
        output_dataset_id: The id of the output BigQuery dataset.
        output_table_id: The id of the output BigQuery table.
    """

    # Instantiate a client.
    dlp = google.cloud.dlp_v2.DlpServiceClient()

    # Location info of the source BigQuery table.
    source_table = {
        "project_id": source_table_project_id,
        "dataset_id": source_dataset_id,
        "table_id": source_table_id,
    }

    # Specify the bigquery table to store the findings.
    # The output_table_id in the given BigQuery dataset will be created if it doesn't
    # already exist.
    dest_table = {
        "project_id": output_table_project_id,
        "dataset_id": output_dataset_id,
        "table_id": output_table_id,
    }

    # Convert quasi id list to Protobuf type
    def map_fields(field: str) -> dict:
        return {"name": field}

    #  Configure column names of quasi-identifiers to analyze
    quasi_ids = map(map_fields, quasi_ids)

    # Tell the API where to send a notification when the job is complete.
    actions = [{"save_findings": {"output_config": {"table": dest_table}}}]

    # Configure the privacy metric to compute for re-identification risk analysis.
    # Specify the unique identifier in the source table for the k-anonymity analysis.
    privacy_metric = {
        "k_anonymity_config": {
            "entity_id": {"field": {"name": entity_id}},
            "quasi_ids": quasi_ids,
        }
    }

    # Configure risk analysis job.
    risk_job = {
        "privacy_metric": privacy_metric,
        "source_table": source_table,
        "actions": actions,
    }

    # Convert the project id into a full resource id.
    parent = f"projects/{project}/locations/global"

    # Call API to start risk analysis job.
    response = dlp.create_dlp_job(
        request={
            "parent": parent,
            "risk_job": risk_job,
        }
    )
    job_name = response.name
    print(f"Inspection Job started : {job_name}")

    # Waiting for a maximum of 15 minutes for the job to be completed.
    job = dlp.get_dlp_job(request={"name": job_name})
    no_of_attempts = 30
    while no_of_attempts > 0:
        # Check if the job has completed
        if job.state == google.cloud.dlp_v2.DlpJob.JobState.DONE:
            break
        if job.state == google.cloud.dlp_v2.DlpJob.JobState.FAILED:
            print("Job Failed, Please check the configuration.")
            return

        # Sleep for a short duration before checking the job status again
        time.sleep(30)
        no_of_attempts -= 1

        # Get the DLP job status
        job = dlp.get_dlp_job(request={"name": job_name})

    if job.state != google.cloud.dlp_v2.DlpJob.JobState.DONE:
        print("Job did not complete within 15 minutes.")
        return

    # Create helper function for unpacking values
    def get_values(obj: types.Value) -> str:
        return str(obj.string_value)

    # Print out the results.
    print(f"Job name: {job.name}")
    histogram_buckets = (
        job.risk_details.k_anonymity_result.equivalence_class_histogram_buckets
    )
    # Print bucket stats
    for i, bucket in enumerate(histogram_buckets):
        print(f"Bucket {i}:")
        if bucket.equivalence_class_size_lower_bound:
            print(
                f"Bucket size range: [{bucket.equivalence_class_size_lower_bound}, "
                f"{bucket.equivalence_class_size_upper_bound}]"
            )
            for value_bucket in bucket.bucket_values:
                print(
                    f"Quasi-ID values: {get_values(value_bucket.quasi_ids_values[0])}"
                )
                print(f"Class size: {value_bucket.equivalence_class_size}")
        else:
            print("No findings.")

後續步驟

瞭解如何計算資料集的 l-diversity 值。
瞭解如何計算資料集的 k-map 值。
瞭解如何計算資料集的 δ-presence 值。

計算資料集的 k-anonymity 透過集合功能整理內容 你可以依據偏好儲存及分類內容。

事前準備

計算 k-anonymity

控制台

C#

Go

Java

Node.js

PHP

Python

REST

列出已完成的風險分析工作

控制台

通訊協定

查看 k-anonymity 工作結果

風險圖表

風險樣本資料表

使用 REST 擷取工作詳細資料

程式碼範例：使用實體 ID 計算 k-anonymity

C#

Go

Java

Node.js

PHP

Python

後續步驟

計算資料集的 k-anonymity