k-图与 k-匿名性非常相似，不同的只是，前者假设攻击者很可能不知道数据集中的人员。如果数据集相对较小，或者泛化属性所涉及的工作量太大，请使用 k-图。
就像 k-匿名性一样，k-图要求您确定数据库的哪些列是准标识符。这样做是为了说明攻击者最有可能使用哪些数据来重标识主体。此外，计算 k-图值需要重标识化数据集：一个较大的表，用于与原始数据集中的行进行比较。
本主题演示了如何使用敏感数据保护计算数据集的 k-map 值。如需从整体上详细了解 k-图或风险分析，请参阅风险分析概念主题，然后再继续。
准备工作
在继续操作之前，请确保您已完成以下步骤：
- 登录您的 Google 账号。
- 在 Google Cloud 控制台的项目选择器页面上，选择或创建 Google Cloud 项目。 转到项目选择器
- 确保您的 Google Cloud 项目已启用结算功能。 了解如何确认您的项目已启用结算功能。
- 启用敏感数据保护。 启用敏感数据保护
- 选择要分析的 BigQuery 数据集。敏感数据保护通过扫描 BigQuery 表来估算 k-map 指标。
- 确定您要用于攻击数据集建模的数据集类型。如需了解详情，请参阅
KMapEstimationConfig对象的参考页面以及风险分析术语和技术。
计算 k-图估算值
您可以使用敏感数据保护来估算 k-map 值，敏感数据保护使用统计模型来估算重标识数据集。这与其他明确知道攻击数据集的风险分析方法不同。根据数据类型，敏感数据保护会使用公开提供的数据集（例如美国人口普查数据集）或自定义统计模型（例如，您指定的一个或多个 BigQuery 表），或者根据输入数据集中值的分布进行推断。如需了解详情，请参阅 KMapEstimationConfig 对象的参考页面。
如需使用敏感数据保护计算 k-map 估算值，请先配置风险作业。请向 projects.dlpJobs 资源发送一个请求，其中 PROJECT_ID 表示项目标识符：
https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs
该请求包含一个由以下项组成的 RiskAnalysisJobConfig 对象：
PrivacyMetric对象。您可以在此处指定包含以下内容的KMapEstimationConfig对象以表明您要计算 k-图：quasiIds[]：必填。被视为要扫描的准标识符的字段（TaggedField对象），用于计算 k-图。任何两列的标记都不能相同。这些标记可为下列任一项：- infoType：这会使敏感数据保护使用相关的公开数据集作为人口统计模型，包括美国邮政编码、区域代码、年龄和性别。
- 自定义 infoType：自定义标记，用于指示包含有关此列的可能值的统计信息的辅助表（
AuxiliaryTable对象）。 inferred标记：如果未指示语义标记，请指定inferred。敏感数据保护会根据输入数据中值的分布来推断统计模型。
regionCode：敏感数据保护要在统计建模中使用的 ISO 3166-1 alpha-2 区域代码。如果未使用区域专属 infoType（如美国邮政编码）或区域代码标记列，则此值是必需的。auxiliaryTables[]：用于分析的辅助表（AuxiliaryTable对象）。用于标记准标识符列的每个自定义标记（来自quasiIds[]）都必须恰好出现在一个辅助表的一列中。
BigQueryTable对象。通过包括以下所有项指定要扫描的 BigQuery 表格：projectId：表格所属项目的 ID。datasetId：表格的数据集 ID。tableId：表格的名称。
由一个或多个
Action对象组成的对象集，这些对象表示在作业完成时要按给定顺序运行的操作。每个Action对象都可以包含以下操作之一：SaveFindings对象：将风险分析扫描的结果保存到 BigQuery 表格中。PublishToPubSub对象：向 Pub/Sub 主题发布通知。
PublishSummaryToCscc对象：将结果摘要保存到 Security Command Center。PublishFindingsToCloudDataCatalog对象：将结果保存到 Data Catalog。JobNotificationEmails对象：向您发送电子邮件，其中包含结果。PublishToStackdriver对象：将结果保存到 Google Cloud 的运维套件。
代码示例
下面是多种语言的示例代码，其中演示了如何使用敏感数据保护计算 k-map 值。
Go
如需了解如何安装和使用用于敏感数据保护的客户端库，请参阅敏感数据保护客户端库。
如需向敏感数据保护服务进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。
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"
"github.com/golang/protobuf/ptypes/empty"
)
// riskKMap runs K Map on the given data.
func riskKMap(w io.Writer, projectID, dataProject, pubSubTopic, pubSubSub, datasetID, tableID, region string, columnNames ...string) error {
// projectID := "my-project-id"
// dataProject := "bigquery-public-data"
// pubSubTopic := "dlp-risk-sample-topic"
// pubSubSub := "dlp-risk-sample-sub"
// datasetID := "san_francisco"
// tableID := "bikeshare_trips"
// region := "US"
// columnNames := "zip_code"
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.PrivacyMetric_KMapEstimationConfig_TaggedField
for _, c := range columnNames {
q = append(q, &dlppb.PrivacyMetric_KMapEstimationConfig_TaggedField{
Field: &dlppb.FieldId{
Name: c,
},
Tag: &dlppb.PrivacyMetric_KMapEstimationConfig_TaggedField_Inferred{
Inferred: &empty.Empty{},
},
})
}
// 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_KMapEstimationConfig_{
KMapEstimationConfig: &dlppb.PrivacyMetric_KMapEstimationConfig{
QuasiIds: q,
RegionCode: region,
},
},
},
// 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().GetKMapEstimationResult().GetKMapEstimationHistogram()
for i, b := range h {
fmt.Fprintf(w, "Histogram bucket %v\n", i)
fmt.Fprintf(w, " Anonymity range: [%v,%v]\n", b.GetMaxAnonymity(), b.GetMaxAnonymity())
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, " Estimated anonymity: %v\n", v.GetEstimatedAnonymity())
}
}
// Stop listening for more messages.
cancel()
})
if err != nil {
return fmt.Errorf("Recieve: %w", err)
}
return nil
}
Java
如需了解如何安装和使用用于敏感数据保护的客户端库，请参阅敏感数据保护客户端库。
如需向敏感数据保护服务进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。
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.KMapEstimationResult;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KMapEstimationResult.KMapEstimationHistogramBucket;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.KMapEstimationResult.KMapEstimationQuasiIdValues;
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.InfoType;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.PrivacyMetric;
import com.google.privacy.dlp.v2.PrivacyMetric.KMapEstimationConfig;
import com.google.privacy.dlp.v2.PrivacyMetric.KMapEstimationConfig.TaggedField;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
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.ArrayList;
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 RiskAnalysisKMap {
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";
calculateKMap(projectId, datasetId, tableId, topicId, subscriptionId);
}
public static void calculateKMap(
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", "Gender");
// These values represent the info types corresponding to the quasi-identifiers above
List<String> infoTypeNames = Arrays.asList("AGE", "GENDER");
// Tag each of the quasiId column names with its corresponding infoType
List<InfoType> infoTypes =
infoTypeNames.stream()
.map(it -> InfoType.newBuilder().setName(it).build())
.collect(Collectors.toList());
if (quasiIds.size() != infoTypes.size()) {
throw new IllegalArgumentException("The numbers of quasi-IDs and infoTypes must be equal!");
}
List<TaggedField> taggedFields = new ArrayList<TaggedField>();
for (int i = 0; i < quasiIds.size(); i++) {
TaggedField taggedField =
TaggedField.newBuilder()
.setField(FieldId.newBuilder().setName(quasiIds.get(i)).build())
.setInfoType(infoTypes.get(i))
.build();
taggedFields.add(taggedField);
}
// The k-map distribution region can be specified by any ISO-3166-1 region code.
String regionCode = "US";
// Configure the privacy metric for the job
KMapEstimationConfig kmapConfig =
KMapEstimationConfig.newBuilder()
.addAllQuasiIds(taggedFields)
.setRegionCode(regionCode)
.build();
PrivacyMetric privacyMetric =
PrivacyMetric.newBuilder().setKMapEstimationConfig(kmapConfig).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
KMapEstimationResult kmapResult = completedJob.getRiskDetails().getKMapEstimationResult();
for (KMapEstimationHistogramBucket result : kmapResult.getKMapEstimationHistogramList()) {
System.out.printf(
"\tAnonymity range: [%d, %d]\n", result.getMinAnonymity(), result.getMaxAnonymity());
System.out.printf("\tSize: %d\n", result.getBucketSize());
for (KMapEstimationQuasiIdValues valueBucket : result.getBucketValuesList()) {
List<String> quasiIdValues =
valueBucket.getQuasiIdsValuesList().stream()
.map(
value -> {
String s = value.toString();
return s.substring(s.indexOf(':') + 1).trim();
})
.collect(Collectors.toList());
System.out.printf("\tValues: {%s}\n", String.join(", ", quasiIdValues));
System.out.printf(
"\tEstimated k-map anonymity: %d\n", valueBucket.getEstimatedAnonymity());
}
}
}
}
// 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
如需了解如何安装和使用用于敏感数据保护的客户端库，请参阅敏感数据保护客户端库。
如需向敏感数据保护服务进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。
// 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'
// The ISO 3166-1 region code that the data is representative of
// Can be omitted if using a region-specific infoType (such as US_ZIP_5)
// const regionCode = 'USA';
// A set of columns that form a composite key ('quasi-identifiers'), and
// optionally their reidentification distributions
// const quasiIds = [{ field: { name: 'age' }, infoType: { name: 'AGE' }}];
async function kMapEstimationAnalysis() {
const sourceTable = {
projectId: tableProjectId,
datasetId: datasetId,
tableId: tableId,
};
// Construct request for creating a risk analysis job
const request = {
parent: `projects/${projectId}/locations/global`,
riskJob: {
privacyMetric: {
kMapEstimationConfig: {
quasiIds: quasiIds,
regionCode: regionCode,
},
},
sourceTable: sourceTable,
actions: [
{
pubSub: {
topic: `projects/${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.kMapEstimationResult.kMapEstimationHistogram;
histogramBuckets.forEach((histogramBucket, histogramBucketIdx) => {
console.log(`Bucket ${histogramBucketIdx}:`);
console.log(
` Anonymity range: [${histogramBucket.minAnonymity}, ${histogramBucket.maxAnonymity}]`
);
console.log(` Size: ${histogramBucket.bucketSize}`);
histogramBucket.bucketValues.forEach(valueBucket => {
const values = valueBucket.quasiIdsValues.map(value => getValue(value));
console.log(` Values: ${values.join(' ')}`);
console.log(
` Estimated k-map anonymity: ${valueBucket.estimatedAnonymity}`
);
});
});
}
await kMapEstimationAnalysis();PHP
如需了解如何安装和使用用于敏感数据保护的客户端库，请参阅敏感数据保护客户端库。
如需向敏感数据保护服务进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。
use Exception;
use Google\Cloud\Dlp\V2\Action;
use Google\Cloud\Dlp\V2\Action\PublishToPubSub;
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\FieldId;
use Google\Cloud\Dlp\V2\GetDlpJobRequest;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\PrivacyMetric\KMapEstimationConfig;
use Google\Cloud\Dlp\V2\PrivacyMetric\KMapEstimationConfig\TaggedField;
use Google\Cloud\Dlp\V2\RiskAnalysisJobConfig;
use Google\Cloud\PubSub\PubSubClient;
/**
* Computes the k-map risk estimation 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 $regionCode The ISO 3166-1 region code that the data is representative of
* @param string[] $quasiIdNames Array columns that form a composite key (quasi-identifiers)
* @param string[] $infoTypes Array of infoTypes corresponding to the chosen quasi-identifiers
*/
function k_map(
string $callingProjectId,
string $dataProjectId,
string $topicId,
string $subscriptionId,
string $datasetId,
string $tableId,
string $regionCode,
array $quasiIdNames,
array $infoTypes
): void {
// Instantiate a client.
$dlp = new DlpServiceClient();
$pubsub = new PubSubClient();
$topic = $pubsub->topic($topicId);
// Verify input
if (count($infoTypes) != count($quasiIdNames)) {
throw new Exception('Number of infoTypes and number of quasi-identifiers must be equal!');
}
// Map infoTypes to quasi-ids
$quasiIdObjects = array_map(function ($quasiId, $infoType) {
$quasiIdField = (new FieldId())
->setName($quasiId);
$quasiIdType = (new InfoType())
->setName($infoType);
$quasiIdObject = (new TaggedField())
->setInfoType($quasiIdType)
->setField($quasiIdField);
return $quasiIdObject;
}, $quasiIdNames, $infoTypes);
// Construct analysis config
$statsConfig = (new KMapEstimationConfig())
->setQuasiIds($quasiIdObjects)
->setRegionCode($regionCode);
$privacyMetric = (new PrivacyMetric())
->setKMapEstimationConfig($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 = $job->getRiskDetails()->getKMapEstimationResult()->getKMapEstimationHistogram();
foreach ($histBuckets as $bucketIndex => $histBucket) {
// Print bucket stats
printf('Bucket %s:' . PHP_EOL, $bucketIndex);
printf(
' Anonymity range: [%s, %s]' . PHP_EOL,
$histBucket->getMinAnonymity(),
$histBucket->getMaxAnonymity()
);
printf(' Size: %s' . PHP_EOL, $histBucket->getBucketSize());
// Print bucket values
foreach ($histBucket->getBucketValues() as $percent => $valueBucket) {
printf(
' Estimated k-map anonymity: %s' . PHP_EOL,
$valueBucket->getEstimatedAnonymity()
);
// Pretty-print quasi-ID values
print(' Values: ' . PHP_EOL);
foreach ($valueBucket->getQuasiIdsValues() as $index => $value) {
print(' ' . $value->serializeToJsonString() . PHP_EOL);
}
}
}
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
如需了解如何安装和使用用于敏感数据保护的客户端库，请参阅敏感数据保护客户端库。
如需向敏感数据保护服务进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。
import concurrent.futures
from typing import List
import google.cloud.dlp
from google.cloud.dlp_v2 import types
import google.cloud.pubsub
def k_map_estimate_analysis(
project: str,
table_project_id: str,
dataset_id: str,
table_id: str,
topic_id: str,
subscription_id: str,
quasi_ids: List[str],
info_types: List[str],
region_code: str = "US",
timeout: int = 300,
) -> None:
"""Uses the Data Loss Prevention API to compute the k-map risk estimation
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 and optionally
their re-identification distributions.
info_types: Type of information of the quasi_id in order to provide a
statistical model of population.
region_code: The ISO 3166-1 region code that the data is representative
of. Can be omitted if using a region-specific infoType (such as
US_ZIP_5)
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 full resource ids.
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,
}
# Check that numbers of quasi-ids and info types are equal
if len(quasi_ids) != len(info_types):
raise ValueError(
"""Number of infoTypes and number of quasi-identifiers
must be equal!"""
)
# Convert quasi id list to Protobuf type
def map_fields(quasi_id: str, info_type: str) -> dict:
return {"field": {"name": quasi_id}, "info_type": {"name": info_type}}
quasi_ids = map(map_fields, quasi_ids, info_types)
# 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_map_estimation_config": {
"quasi_ids": quasi_ids,
"region_code": region_code,
}
},
"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_map_estimation_result.k_map_estimation_histogram
)
# Print bucket stats
for i, bucket in enumerate(histogram_buckets):
print(f"Bucket {i}:")
print(
" Anonymity range: [{}, {}]".format(
bucket.min_anonymity, bucket.max_anonymity
)
)
print(f" Size: {bucket.bucket_size}")
for value_bucket in bucket.bucket_values:
print(
" Values: {}".format(
map(get_values, value_bucket.quasi_ids_values)
)
)
print(
" Estimated k-map anonymity: {}".format(
value_bucket.estimated_anonymity
)
)
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()
C#
如需了解如何安装和使用用于敏感数据保护的客户端库，请参阅敏感数据保护客户端库。
如需向敏感数据保护服务进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。
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;
using static Google.Cloud.Dlp.V2.PrivacyMetric.Types.KMapEstimationConfig.Types;
public class RiskAnalysisCreateKMap
{
public static object KMap(
string callingProjectId,
string tableProjectId,
string datasetId,
string tableId,
string topicId,
string subscriptionId,
IEnumerable<FieldId> quasiIds,
IEnumerable<InfoType> infoTypes,
string regionCode)
{
var dlp = DlpServiceClient.Create();
// Construct + submit the job
var kmapEstimationConfig = new KMapEstimationConfig
{
QuasiIds =
{
quasiIds.Zip(
infoTypes,
(Field, InfoType) => new TaggedField
{
Field = Field,
InfoType = InfoType
}
)
},
RegionCode = regionCode
};
var config = new RiskAnalysisJobConfig()
{
PrivacyMetric = new PrivacyMetric
{
KMapEstimationConfig = kmapEstimationConfig
},
SourceTable = new BigQueryTable
{
ProjectId = tableProjectId,
DatasetId = datasetId,
TableId = tableId
},
Actions =
{
new Google.Cloud.Dlp.V2.Action
{
PubSub = new PublishToPubSub
{
Topic = $"projects/{callingProjectId}/topics/{topicId}"
}
}
}
};
var submittedJob = dlp.CreateDlpJob(
new CreateDlpJobRequest
{
ParentAsProjectName = new ProjectName(callingProjectId),
RiskJob = config
});
// Listen to pub/sub for the job
var subscriptionName = new SubscriptionName(
callingProjectId,
subscriptionId);
var subscriber = SubscriberClient.CreateAsync(
subscriptionName).Result;
// SimpleSubscriber runs your message handle function on multiple
// threads to maximize throughput.
var done = new ManualResetEventSlim(false);
subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
{
if (message.Attributes["DlpJobName"] == submittedJob.Name)
{
Thread.Sleep(500); // Wait for DLP API results to become consistent
done.Set();
return Task.FromResult(SubscriberClient.Reply.Ack);
}
else
{
return Task.FromResult(SubscriberClient.Reply.Nack);
}
});
done.Wait(TimeSpan.FromMinutes(10)); // 10 minute timeout; may not work for large jobs
subscriber.StopAsync(CancellationToken.None).Wait();
// Process results
var resultJob = dlp.GetDlpJob(new GetDlpJobRequest
{
DlpJobName = DlpJobName.Parse(submittedJob.Name)
});
var result = resultJob.RiskDetails.KMapEstimationResult;
for (var histogramIdx = 0; histogramIdx < result.KMapEstimationHistogram.Count; histogramIdx++)
{
var histogramValue = result.KMapEstimationHistogram[histogramIdx];
Console.WriteLine($"Bucket {histogramIdx}");
Console.WriteLine($" Anonymity range: [{histogramValue.MinAnonymity}, {histogramValue.MaxAnonymity}].");
Console.WriteLine($" Size: {histogramValue.BucketSize}");
foreach (var datapoint in histogramValue.BucketValues)
{
// 'UnpackValue(x)' is a prettier version of 'x.toString()'
Console.WriteLine($" Values: [{String.Join(',', datapoint.QuasiIdsValues.Select(x => UnpackValue(x)))}]");
Console.WriteLine($" Estimated k-map anonymity: {datapoint.EstimatedAnonymity}");
}
}
return 0;
}
public static string UnpackValue(Value protoValue)
{
var jsonValue = JsonConvert.DeserializeObject<Dictionary<string, object>>(protoValue.ToString());
return jsonValue.Values.ElementAt(0).ToString();
}
}
查看 k-图作业结果
如需使用 REST API 检索 k-图风险分析作业，请将以下 GET 请求发送到 projects.dlpJobs 资源。将 PROJECT_ID 替换为您的项目 ID，并将 JOB_ID 替换为您要获取其结果的作业的标识符。作业 ID 在启动作业时返回，也可通过列出所有作业来检索。
GET https://dlp.googleapis.com/v2/projects/PROJECT_ID/dlpJobs/JOB_ID
该请求会返回包含作业实例的 JSON 对象。分析的结果位于 AnalyzeDataSourceRiskDetails 对象的 "riskDetails" 键中。如需了解详情，请参阅 DlpJob 资源的 API 参考文档。