使用 Cloud DLP 计算 I-多样性。l-多样性是对 k-匿名性的扩展，可针对出现敏感值的每一列测量敏感值的多样性。如果对于具有相同准标识符的每组行，每个敏感属性至少有 l 个不同的值，则数据集具有 l-多样性。
包含此代码示例的文档页面
如需查看上下文中使用的代码示例，请参阅以下文档：
代码示例
C#
如需了解如何安装和使用 Cloud DLP 客户端库，请参阅 Cloud DLP 客户端库。
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 RiskAnalysisCreateLDiversity
{
public static object LDiversity(
string callingProjectId,
string tableProjectId,
string datasetId,
string tableId,
string topicId,
string subscriptionId,
IEnumerable<FieldId> quasiIds,
string sensitiveAttribute)
{
var dlp = DlpServiceClient.Create();
// Construct + submit the job
var ldiversityConfig = new LDiversityConfig
{
SensitiveAttribute = new FieldId { Name = sensitiveAttribute },
QuasiIds = { quasiIds }
};
var config = new RiskAnalysisJobConfig
{
PrivacyMetric = new PrivacyMetric
{
LDiversityConfig = ldiversityConfig
},
SourceTable = new BigQueryTable
{
ProjectId = tableProjectId,
DatasetId = datasetId,
TableId = tableId
},
Actions =
{
new Google.Cloud.Dlp.V2.Action
{
PubSub = new PublishToPubSub
{
Topic = $"projects/{callingProjectId}/topics/{topicId}"
}
}
}
};
var submittedJob = dlp.CreateDlpJob(
new CreateDlpJobRequest
{
ParentAsProjectName = new ProjectName(callingProjectId),
RiskJob = config
});
// Listen to pub/sub for the job
var subscriptionName = new SubscriptionName(callingProjectId, subscriptionId);
var subscriber = SubscriberClient.CreateAsync(subscriptionName).Result;
// SimpleSubscriber runs your message handle function on multiple
// threads to maximize throughput.
var done = new ManualResetEventSlim(false);
subscriber.StartAsync((PubsubMessage message, CancellationToken cancel) =>
{
if (message.Attributes["DlpJobName"] == submittedJob.Name)
{
Thread.Sleep(500); // Wait for DLP API results to become consistent
done.Set();
return Task.FromResult(SubscriberClient.Reply.Ack);
}
else
{
return Task.FromResult(SubscriberClient.Reply.Nack);
}
});
done.Wait(TimeSpan.FromMinutes(10)); // 10 minute timeout; may not work for large jobs
subscriber.StopAsync(CancellationToken.None).Wait();
// Process results
var resultJob = dlp.GetDlpJob(
new GetDlpJobRequest
{
DlpJobName = DlpJobName.Parse(submittedJob.Name)
});
var result = resultJob.RiskDetails.LDiversityResult;
for (var bucketIdx = 0; bucketIdx < result.SensitiveValueFrequencyHistogramBuckets.Count; bucketIdx++)
{
var bucket = result.SensitiveValueFrequencyHistogramBuckets[bucketIdx];
Console.WriteLine($"Bucket {bucketIdx}");
Console.WriteLine($" Bucket size range: [{bucket.SensitiveValueFrequencyLowerBound}, {bucket.SensitiveValueFrequencyUpperBound}].");
Console.WriteLine($" {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}");
foreach (var topValue in bucketValue.TopSensitiveValues)
{
Console.WriteLine($" Sensitive value {UnpackValue(topValue.Value)} occurs {topValue.Count} time(s).");
}
}
}
return result;
}
public static string UnpackValue(Value protoValue)
{
var jsonValue = JsonConvert.DeserializeObject<Dictionary<string, object>>(protoValue.ToString());
return jsonValue.Values.ElementAt(0).ToString();
}
}
Go
如需了解如何安装和使用 Cloud DLP 客户端库，请参阅 Cloud DLP 客户端库。
import (
"context"
"fmt"
"io"
"strings"
"time"
dlp "cloud.google.com/go/dlp/apiv2"
"cloud.google.com/go/pubsub"
dlppb "google.golang.org/genproto/googleapis/privacy/dlp/v2"
)
// riskLDiversity computes the L Diversity of the given columns.
func riskLDiversity(w io.Writer, projectID, dataProject, pubSubTopic, pubSubSub, datasetID, tableID, sensitiveAttribute 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"
// sensitiveAttribute := "city"
// columnNames := "state_number", "county"
ctx := context.Background()
client, err := dlp.NewClient(ctx)
if err != nil {
return fmt.Errorf("dlp.NewClient: %v", err)
}
// Create a PubSub Client used to listen for when the inspect job finishes.
pubsubClient, err := pubsub.NewClient(ctx, projectID)
if err != nil {
return fmt.Errorf("Error creating PubSub client: %v", err)
}
defer pubsubClient.Close()
// Create a PubSub subscription we can use to listen for messages.
s, err := setupPubSub(projectID, pubSubTopic, pubSubSub)
if err != nil {
return fmt.Errorf("setupPubSub: %v", 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_LDiversityConfig_{
LDiversityConfig: &dlppb.PrivacyMetric_LDiversityConfig{
QuasiIds: q,
SensitiveAttribute: &dlppb.FieldId{
Name: sensitiveAttribute,
},
},
},
},
// 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: %v", 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().GetLDiversityResult().GetSensitiveValueFrequencyHistogramBuckets()
for i, b := range h {
fmt.Fprintf(w, "Histogram bucket %v\n", i)
fmt.Fprintf(w, " Size range: [%v,%v]\n", b.GetSensitiveValueFrequencyLowerBound(), b.GetSensitiveValueFrequencyUpperBound())
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())
for _, sv := range v.GetTopSensitiveValues() {
fmt.Fprintf(w, " Sensitive value %v occurs %v times\n", sv.GetValue(), sv.GetCount())
}
}
}
// Stop listening for more messages.
cancel()
})
if err != nil {
return fmt.Errorf("Recieve: %v", err)
}
return nil
}
Java
如需了解如何安装和使用 Cloud DLP 客户端库，请参阅 Cloud DLP 客户端库。
import com.google.api.core.SettableApiFuture;
import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.cloud.dlp.v2.DlpServiceSettings;
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.LDiversityResult;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.LDiversityResult.LDiversityEquivalenceClass;
import com.google.privacy.dlp.v2.AnalyzeDataSourceRiskDetails.LDiversityResult.LDiversityHistogramBucket;
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.LDiversityConfig;
import com.google.privacy.dlp.v2.RiskAnalysisJobConfig;
import com.google.privacy.dlp.v2.Value;
import com.google.privacy.dlp.v2.ValueFrequency;
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;
import org.threeten.bp.Duration;
class RiskAnalysisLDiversity {
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";
calculateLDiversity(projectId, datasetId, tableId, topicId, subscriptionId);
}
public static void calculateLDiversity(
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.
DlpServiceSettings.Builder dlpServiceSettingsBuilder = DlpServiceSettings.newBuilder();
dlpServiceSettingsBuilder
.getDlpJobSettings()
.setRetrySettings(
dlpServiceSettingsBuilder
.getDlpJobSettings()
.getRetrySettings()
.toBuilder()
.setTotalTimeout(Duration.ofSeconds(600))
.build());
try (DlpServiceClient dlpServiceClient =
DlpServiceClient.create(dlpServiceSettingsBuilder.build())) {
// 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");
// This value represents the column name to compare the quasi-identifiers against
String sensitiveAttribute = "Name";
// Configure the privacy metric for the job
FieldId sensitiveAttributeField = FieldId.newBuilder().setName(sensitiveAttribute).build();
List<FieldId> quasiIdFields =
quasiIds.stream()
.map(columnName -> FieldId.newBuilder().setName(columnName).build())
.collect(Collectors.toList());
LDiversityConfig ldiversityConfig =
LDiversityConfig.newBuilder()
.addAllQuasiIds(quasiIdFields)
.setSensitiveAttribute(sensitiveAttributeField)
.build();
PrivacyMetric privacyMetric =
PrivacyMetric.newBuilder().setLDiversityConfig(ldiversityConfig).build();
// Create action to publish job status notifications over Google Cloud Pub/
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());
// Get the result and parse through and process the information
LDiversityResult ldiversityResult = completedJob.getRiskDetails().getLDiversityResult();
List<LDiversityHistogramBucket> histogramBucketList =
ldiversityResult.getSensitiveValueFrequencyHistogramBucketsList();
for (LDiversityHistogramBucket result : histogramBucketList) {
for (LDiversityEquivalenceClass 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());
for (ValueFrequency valueFrequency : bucket.getTopSensitiveValuesList()) {
System.out.printf(
"\t\tSensitive value %s occurs %d time(s).\n",
valueFrequency.getValue().toString(), valueFrequency.getCount());
}
}
}
}
}
// 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 Cloud DLP 客户端库，请参阅 Cloud DLP 客户端库。
// 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 column to measure l-diversity relative to, e.g. 'firstName'
// const sensitiveAttribute = 'name';
// A set of columns that form a composite key ('quasi-identifiers')
// const quasiIds = [{ name: 'age' }, { name: 'city' }];
async function lDiversityAnalysis() {
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: {
lDiversityConfig: {
quasiIds: quasiIds,
sensitiveAttribute: {
name: sensitiveAttribute,
},
},
},
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;
// 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.lDiversityResult.sensitiveValueFrequencyHistogramBuckets;
histogramBuckets.forEach((histogramBucket, histogramBucketIdx) => {
console.log(`Bucket ${histogramBucketIdx}:`);
console.log(
`Bucket size range: [${histogramBucket.sensitiveValueFrequencyLowerBound}, ${histogramBucket.sensitiveValueFrequencyUpperBound}]`
);
histogramBucket.bucketValues.forEach(valueBucket => {
const quasiIdValues = valueBucket.quasiIdsValues
.map(getValue)
.join(', ');
console.log(` Quasi-ID values: {${quasiIdValues}}`);
console.log(` Class size: ${valueBucket.equivalenceClassSize}`);
valueBucket.topSensitiveValues.forEach(valueObj => {
console.log(
` Sensitive value ${getValue(valueObj.value)} occurs ${
valueObj.count
} time(s).`
);
});
});
});
}
lDiversityAnalysis();PHP
如需了解如何安装和使用 Cloud DLP 客户端库，请参阅 Cloud DLP 客户端库。
/**
* Computes the l-diversity of a column set in a Google BigQuery table.
*/
use Google\Cloud\Dlp\V2\DlpServiceClient;
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\PrivacyMetric\LDiversityConfig;
use Google\Cloud\Dlp\V2\PrivacyMetric;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\PubSub\PubSubClient;
/** Uncomment and populate these variables in your code */
// $callingProjectId = 'The project ID to run the API call under';
// $dataProjectId = 'The project ID containing the target Datastore';
// $topicId = 'The name of the Pub/Sub topic to notify once the job completes';
// $subscriptionId = 'The name of the Pub/Sub subscription to use when listening for job';
// $datasetId = 'The ID of the dataset to inspect';
// $tableId = 'The ID of the table to inspect';
// $sensitiveAttribute = 'The column to measure l-diversity relative to, e.g. "firstName"';
// $quasiIdNames = ['array columns that form a composite key (quasi-identifiers)'];
// Instantiate a client.
$dlp = new DlpServiceClient([
'projectId' => $callingProjectId,
]);
$pubsub = new PubSubClient([
'projectId' => $callingProjectId,
]);
$topic = $pubsub->topic($topicId);
// Construct risk analysis config
$quasiIds = array_map(
function ($id) {
return (new FieldId())->setName($id);
},
$quasiIdNames
);
$sensitiveField = (new FieldId())
->setName($sensitiveAttribute);
$statsConfig = (new LDiversityConfig())
->setQuasiIds($quasiIds)
->setSensitiveAttribute($sensitiveField);
$privacyMetric = (new PrivacyMetric())
->setLDiversityConfig($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";
$job = $dlp->createDlpJob($parent, [
'riskJob' => $riskJob
]);
// 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 {
$job = $dlp->getDlpJob($job->getName());
} while ($job->getState() == JobState::RUNNING);
break 2; // break from parent do while
}
}
printf('Waiting for job to complete' . PHP_EOL);
// Exponential backoff with max delay of 60 seconds
sleep(min(60, pow(2, ++$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 = $job->getRiskDetails()->getLDiversityResult()->getSensitiveValueFrequencyHistogramBuckets();
foreach ($histBuckets as $bucketIndex => $histBucket) {
// Print bucket stats
printf('Bucket %s:' . PHP_EOL, $bucketIndex);
printf(
' Bucket size range: [%s, %s]' . PHP_EOL,
$histBucket->getSensitiveValueFrequencyLowerBound(),
$histBucket->getSensitiveValueFrequencyUpperBound()
);
// Print bucket values
foreach ($histBucket->getBucketValues() as $percent => $valueBucket) {
printf(
' Class size: %s' . PHP_EOL,
$valueBucket->getEquivalenceClassSize()
);
// Pretty-print quasi-ID values
print(' Quasi-ID values:' . PHP_EOL);
foreach ($valueBucket->getQuasiIdsValues() as $index => $value) {
print(' ' . $value->serializeToJsonString() . PHP_EOL);
}
// Pretty-print sensitive values
$topValues = $valueBucket->getTopSensitiveValues();
foreach ($topValues as $topValue) {
printf(
' Sensitive value %s occurs %s time(s).' . PHP_EOL,
$topValue->getValue()->serializeToJsonString(),
$topValue->getCount()
);
}
}
}
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
如需了解如何安装和使用 Cloud DLP 客户端库，请参阅 Cloud DLP 客户端库。
def l_diversity_analysis(
project,
table_project_id,
dataset_id,
table_id,
topic_id,
subscription_id,
sensitive_attribute,
quasi_ids,
timeout=300,
):
"""Uses the Data Loss Prevention API to compute the l-diversity 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.
sensitive_attribute: The column to measure l-diversity relative to.
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.
"""
import concurrent.futures
# Import the client library.
import google.cloud.dlp
# This sample additionally uses Cloud Pub/Sub to receive results from
# potentially long-running operations.
import google.cloud.pubsub
# Create helper function for unpacking values
def get_values(obj):
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):
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": {
"l_diversity_config": {
"quasi_ids": quasi_ids,
"sensitive_attribute": {"name": sensitive_attribute},
}
},
"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):
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})
histogram_buckets = (
job.risk_details.l_diversity_result.sensitive_value_frequency_histogram_buckets # noqa: E501
)
# Print bucket stats
for i, bucket in enumerate(histogram_buckets):
print("Bucket {}:".format(i))
print(
" Bucket size range: [{}, {}]".format(
bucket.sensitive_value_frequency_lower_bound,
bucket.sensitive_value_frequency_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)
)
for value in value_bucket.top_sensitive_values:
print(
(
" Sensitive value {} occurs {} time(s)".format(
value.value, value.count
)
)
)
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()
后续步骤
如需搜索和过滤其他 Google Cloud 产品的代码示例，请参阅 Google Cloud 示例浏览器。