Memeriksa teks terstruktur untuk data sensitif

Perlindungan Data Sensitif dapat mendeteksi dan mengklasifikasikan data sensitif dalam konten terstruktur seperti CSV. Dengan memeriksa atau menghapus identitas sebagai tabel, struktur dan kolom memberikan petunjuk tambahan kepada Perlindungan Data Sensitif yang dapat memungkinkannya memberikan hasil yang lebih baik untuk beberapa kasus penggunaan.

Memeriksa tabel

Contoh kode di bawah menunjukkan cara memeriksa tabel data untuk konten sensitif. Tabel mendukung berbagai jenis.

Penting: Kode di halaman ini mengharuskan Anda menyiapkan klien Perlindungan Data Sensitif terlebih dahulu. Untuk mengetahui informasi selengkapnya tentang cara menginstal dan membuat klien Perlindungan Data Sensitif, lihat Library klien Perlindungan Data Sensitif. (Mengirim JSON ke endpoint REST Perlindungan Data Sensitif tidak memerlukan library klien.)

C#

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Perlindungan Data Sensitif, lihat library klien Perlindungan Data Sensitif.

Untuk melakukan autentikasi ke Perlindungan Data Sensitif, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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

public class InspectTable
{
    public static InspectContentResponse InspectTableData(
        string projectId,
        Table tableToInspect = null,
        IEnumerable<InfoType> infoTypes = null)
    {
        // Instantiate a client.
        var dlp = DlpServiceClient.Create();

        // Construct the table if null.
        if (tableToInspect == null)
        {
            var row1 = new Value[]
            {
                new Value { StringValue = "John Doe" },
                new Value { StringValue = "[phone]" }
            };
            var row2 = new Value[]
            {
                new Value { StringValue = "Mark Twain" },
                new Value { StringValue = "[phone]" }
            };

            tableToInspect = new Table
            {
                Headers =
                {
                    new FieldId { Name = "Name" }, new FieldId { Name = "Phone" }
                },
                Rows =
                {
                    new Table.Types.Row { Values = { row1 } },
                    new Table.Types.Row { Values = { row2 } }
                }
            };
        }

        // Set content item.
        var contentItem = new ContentItem { Table = tableToInspect };

        // Construct inspect config.
        var inspectConfig = new InspectConfig
        {
            InfoTypes =
            {
                infoTypes ?? new InfoType[] { new InfoType { Name = "PHONE_NUMBER" } }
            },
            IncludeQuote = true,
        };

        // Construct a request.
        var request = new InspectContentRequest
        {
            ParentAsLocationName = new LocationName(projectId, "global"),
            InspectConfig = inspectConfig,
            Item = contentItem,
        };

        // Call the API.
        var response = dlp.InspectContent(request);

        // Inspect the results.
        var resultFindings = response.Result.Findings;

        Console.WriteLine($"Findings: {resultFindings.Count}");

        foreach (var f in resultFindings)
        {
            Console.WriteLine("Quote: " + f.Quote);
            Console.WriteLine("Info type: " + f.InfoType.Name);
            Console.WriteLine("Likelihood: " + f.Likelihood);
        }

        return response;
    }
}

Go

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Perlindungan Data Sensitif, lihat library klien Perlindungan Data Sensitif.

Untuk melakukan autentikasi ke Perlindungan Data Sensitif, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

import (
	"context"
	"fmt"
	"io"

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

// inspectTable inspects a table for sensitive content
func inspectTable(w io.Writer, projectID string) error {
	// projectID := "your-project-id"

	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()

	// create a default table
	tableToInspect := &dlppb.Table{
		Headers: []*dlppb.FieldId{
			{Name: "name"},
			{Name: "phone"},
		},
		Rows: []*dlppb.Table_Row{
			{
				Values: []*dlppb.Value{
					{
						Type: &dlppb.Value_StringValue{
							StringValue: "John Doe",
						},
					},
					{
						Type: &dlppb.Value_StringValue{
							StringValue: "[phone]",
						},
					},
				},
			},
		},
	}

	// Specify the table to be inspected.
	contentItem := &dlppb.ContentItem{
		DataItem: &dlppb.ContentItem_Table{
			Table: tableToInspect,
		},
	}

	// Specify the type of info the inspection will look for.
	// See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
	infoTypes := []*dlppb.InfoType{
		{Name: "PHONE_NUMBER"},
	}

	// Construct the Inspect request to be sent by the client.
	req := &dlppb.InspectContentRequest{
		Parent: fmt.Sprintf("projects/%s/locations/global", projectID),
		Item:   contentItem,
		InspectConfig: &dlppb.InspectConfig{
			InfoTypes:    infoTypes,
			IncludeQuote: true,
		},
	}

	// Send the request.
	resp, err := client.InspectContent(ctx, req)
	if err != nil {
		return err
	}

	// Print the results.
	fmt.Fprintf(w, "Findings: %v\n", len(resp.Result.Findings))
	for _, v := range resp.GetResult().Findings {
		fmt.Fprintf(w, "Quote: %v\n", v.GetQuote())
		fmt.Fprintf(w, "Infotype Name: %v\n", v.GetInfoType().GetName())
		fmt.Fprintf(w, "Likelihood: %v\n", v.GetLikelihood())
	}
	return nil

}

Java

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Perlindungan Data Sensitif, lihat library klien Perlindungan Data Sensitif.

Untuk melakukan autentikasi ke Perlindungan Data Sensitif, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

import com.google.cloud.dlp.v2.DlpServiceClient;
import com.google.privacy.dlp.v2.ContentItem;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.Finding;
import com.google.privacy.dlp.v2.InfoType;
import com.google.privacy.dlp.v2.InspectConfig;
import com.google.privacy.dlp.v2.InspectContentRequest;
import com.google.privacy.dlp.v2.InspectContentResponse;
import com.google.privacy.dlp.v2.LocationName;
import com.google.privacy.dlp.v2.Table;
import com.google.privacy.dlp.v2.Table.Row;
import com.google.privacy.dlp.v2.Value;

public class InspectTable {

  public static void main(String[] args) throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    Table tableToInspect =
        Table.newBuilder()
            .addHeaders(FieldId.newBuilder().setName("name").build())
            .addHeaders(FieldId.newBuilder().setName("phone").build())
            .addRows(
                Row.newBuilder()
                    .addValues(Value.newBuilder().setStringValue("John Doe").build())
                    .addValues(Value.newBuilder().setStringValue("[phone]").build()))
            .build();

    inspectTable(projectId, tableToInspect);
  }

  // Inspects the provided text.
  public static void inspectTable(String projectId, Table tableToInspect) {
    // 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 dlp = DlpServiceClient.create()) {
      // Specify the table to be inspected.
      ContentItem item = ContentItem.newBuilder().setTable(tableToInspect).build();

      // Specify the type of info the inspection will look for.
      // See https://cloud.google.com/dlp/docs/infotypes-reference for complete list of info types
      InfoType infoType = InfoType.newBuilder().setName("PHONE_NUMBER").build();

      // Construct the configuration for the Inspect request.
      InspectConfig config =
          InspectConfig.newBuilder().addInfoTypes(infoType).setIncludeQuote(true).build();

      // Construct the Inspect request to be sent by the client.
      InspectContentRequest request =
          InspectContentRequest.newBuilder()
              .setParent(LocationName.of(projectId, "global").toString())
              .setItem(item)
              .setInspectConfig(config)
              .build();

      // Use the client to send the API request.
      InspectContentResponse response = dlp.inspectContent(request);

      // Parse the response and process results
      System.out.println("Findings: " + response.getResult().getFindingsCount());
      for (Finding f : response.getResult().getFindingsList()) {
        System.out.println("\tQuote: " + f.getQuote());
        System.out.println("\tInfo type: " + f.getInfoType().getName());
        System.out.println("\tLikelihood: " + f.getLikelihood());
      }
    } catch (Exception e) {
      System.out.println("Error during inspectString: \n" + e.toString());
    }
  }
}

Node.js

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Perlindungan Data Sensitif, lihat library klien Perlindungan Data Sensitif.

Untuk melakukan autentikasi ke Perlindungan Data Sensitif, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

// 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 = 'my-project';

// The infoTypes of information to match
const infoTypes = [{name: 'PHONE_NUMBER'}];

// Table data
const tableData = {
  headers: [{name: 'name'}, {name: 'phone'}],
  rows: [
    {
      values: [{stringValue: 'John Doe'}, {stringValue: '[phone]'}],
    },
  ],
};

async function inspectTable() {
  // Specify the table to be inspected.
  const item = {
    table: tableData,
  };

  // Construct the configuration for the Inspect request.
  const inspectConfig = {
    infoTypes: infoTypes,
    includeQuote: true,
  };

  // Construct the Inspect request to be sent by the client.
  const request = {
    parent: `projects/${projectId}/locations/global`,
    inspectConfig: inspectConfig,
    item: item,
  };

  // Use the client to send the API request.
  const [response] = await dlp.inspectContent(request);

  // Print findings.
  const findings = response.result.findings;
  if (findings.length > 0) {
    console.log(`Findings: ${findings.length}\n`);
    findings.forEach(finding => {
      console.log(`InfoType: ${finding.infoType.name}`);
      console.log(`\tQuote: ${finding.quote}`);
      console.log(`\tLikelihood: ${finding.likelihood} \n`);
    });
  } else {
    console.log('No findings.');
  }
}
inspectTable();

PHP

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Perlindungan Data Sensitif, lihat library klien Perlindungan Data Sensitif.

Untuk melakukan autentikasi ke Perlindungan Data Sensitif, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

use Google\Cloud\Dlp\V2\Client\DlpServiceClient;
use Google\Cloud\Dlp\V2\ContentItem;
use Google\Cloud\Dlp\V2\FieldId;
use Google\Cloud\Dlp\V2\InfoType;
use Google\Cloud\Dlp\V2\InspectConfig;
use Google\Cloud\Dlp\V2\InspectContentRequest;
use Google\Cloud\Dlp\V2\Likelihood;
use Google\Cloud\Dlp\V2\Table;
use Google\Cloud\Dlp\V2\Table\Row;
use Google\Cloud\Dlp\V2\Value;

/**
 * Inspect a table for sensitive content.
 *
 * @param string $projectId         The Google Cloud project id to use as a parent resource.
 */
function inspect_table(string $projectId): void
{
    // Instantiate a client.
    $dlp = new DlpServiceClient();

    $parent = "projects/$projectId/locations/global";

    // Specify the table to be inspected.
    $tableToDeIdentify = (new Table())
        ->setHeaders([
            (new FieldId())
                ->setName('NAME'),
            (new FieldId())
                ->setName('PHONE'),
        ])
        ->setRows([
            (new Row())->setValues([
                (new Value())
                    ->setStringValue('John Doe'),
                (new Value())
                    ->setStringValue('[phone]')
            ])
        ]);

    $item = (new ContentItem())
        ->setTable($tableToDeIdentify);

    // Construct the configuration for the Inspect request.
    $phoneNumber = (new InfoType())
        ->setName('PHONE_NUMBER');
    $inspectConfig = (new InspectConfig())
        ->setInfoTypes([$phoneNumber])
        ->setIncludeQuote(true);

    // Run request.
    $inspectContentRequest = (new InspectContentRequest())
        ->setParent($parent)
        ->setInspectConfig($inspectConfig)
        ->setItem($item);
    $response = $dlp->inspectContent($inspectContentRequest);

    // Print the results.
    $findings = $response->getResult()->getFindings();
    if (count($findings) == 0) {
        printf('No findings.' . PHP_EOL);
    } else {
        printf('Findings:' . PHP_EOL);
        foreach ($findings as $finding) {
            printf('  Quote: %s' . PHP_EOL, $finding->getQuote());
            printf('  Info type: %s' . PHP_EOL, $finding->getInfoType()->getName());
            printf('  Likelihood: %s' . PHP_EOL, Likelihood::name($finding->getLikelihood()));
        }
    }
}

Python

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Perlindungan Data Sensitif, lihat library klien Perlindungan Data Sensitif.

Untuk melakukan autentikasi ke Perlindungan Data Sensitif, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

from typing import List, Optional

import google.cloud.dlp


def inspect_table(
    project: str,
    data: str,
    info_types: List[str],
    custom_dictionaries: List[str] = None,
    custom_regexes: List[str] = None,
    min_likelihood: Optional[str] = None,
    max_findings: Optional[int] = None,
    include_quote: bool = True,
) -> None:
    """Uses the Data Loss Prevention API to analyze strings for protected data.
    Args:
        project: The Google Cloud project id to use as a parent resource.
        data: Json string representing table data.
        info_types: A list of strings representing info types to look for.
            A full list of info type categories can be fetched from the API.
        min_likelihood: A string representing the minimum likelihood threshold
            that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
            'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
        max_findings: The maximum number of findings to report; 0 = no maximum.
        include_quote: Boolean for whether to display a quote of the detected
            information in the results.
    Returns:
        None; the response from the API is printed to the terminal.
    Example:
        data = {
            "header":[
                "email",
                "phone number"
            ],
            "rows":[
                [
                    "[e-mail]",
                    "4232342345"
                ],
                [
                    "[e-mail]",
                    "4253458383"
                ]
            ]
        }

        >> $ python inspect_content.py table \
        '{"header": ["email", "phone number"],
        "rows": [["[e-mail]", "4232342345"],
        ["[e-mail]", "4253458383"]]}'
        >>  Quote: [e-mail]
            Info type: EMAIL_ADDRESS
            Likelihood: 4
            Quote: [e-mail]
            Info type: EMAIL_ADDRESS
            Likelihood: 4
    """

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

    # Prepare info_types by converting the list of strings into a list of
    # dictionaries (protos are also accepted).
    info_types = [{"name": info_type} for info_type in info_types]

    # Prepare custom_info_types by parsing the dictionary word lists and
    # regex patterns.
    if custom_dictionaries is None:
        custom_dictionaries = []
    dictionaries = [
        {
            "info_type": {"name": f"CUSTOM_DICTIONARY_{i}"},
            "dictionary": {"word_list": {"words": custom_dict.split(",")}},
        }
        for i, custom_dict in enumerate(custom_dictionaries)
    ]
    if custom_regexes is None:
        custom_regexes = []
    regexes = [
        {
            "info_type": {"name": f"CUSTOM_REGEX_{i}"},
            "regex": {"pattern": custom_regex},
        }
        for i, custom_regex in enumerate(custom_regexes)
    ]
    custom_info_types = dictionaries + regexes

    # Construct the configuration dictionary. Keys which are None may
    # optionally be omitted entirely.
    inspect_config = {
        "info_types": info_types,
        "custom_info_types": custom_info_types,
        "min_likelihood": min_likelihood,
        "include_quote": include_quote,
        "limits": {"max_findings_per_request": max_findings},
    }

    # Construct the `table`. For more details on the table schema, please see
    # https://cloud.google.com/dlp/docs/reference/rest/v2/ContentItem#Table
    headers = [{"name": val} for val in data["header"]]
    rows = []
    for row in data["rows"]:
        rows.append({"values": [{"string_value": cell_val} for cell_val in row]})

    table = {}
    table["headers"] = headers
    table["rows"] = rows
    item = {"table": table}
    # Convert the project id into a full resource id.
    parent = f"projects/{project}"

    # Call the API.
    response = dlp.inspect_content(
        request={"parent": parent, "inspect_config": inspect_config, "item": item}
    )

    # Print out the results.
    if response.result.findings:
        for finding in response.result.findings:
            try:
                if finding.quote:
                    print(f"Quote: {finding.quote}")
            except AttributeError:
                pass
            print(f"Info type: {finding.info_type.name}")
            print(f"Likelihood: {finding.likelihood}")
    else:
        print("No findings.")

REST

Lihat panduan memulai JSON untuk mengetahui informasi selengkapnya tentang penggunaan DLP API dengan JSON.

Input JSON:

POST https://dlp.googleapis.com/v2/projects/[PROJECT_ID]/content:inspect?key={YOUR_API_KEY}

{
  "item":{
    "table":{
      "headers": [{"name":"name"}, {"name":"phone"}],
      "rows": [{
        "values":[
          {"string_value": "John Doe"},
          {"string_value": "[phone]"}
        ]}
      ],
    }
  },
  "inspectConfig":{
    "infoTypes":[
      {
        "name":"PHONE_NUMBER"
      }
    ],
    "includeQuote":true
  }
}

Output JSON:

{
  "result": {
    "findings": [
     {
      "quote": "[phone]",
      "infoType": {
       "name": "PHONE_NUMBER"
      },
      "likelihood": "VERY_LIKELY",
      "location": {
         "byteRange": {
          "end": "14"
         },
         "codepointRange": {
          "end": "14"
         },
         "contentLocations": [
          {
           "recordLocation": {
              "fieldId": {
               "name": "phone"
              },
              "tableLocation": {
              }
           }
          }
         ]
      },
      "createTime": "2019-03-08T23:55:10.980Z"
     }
    ]
  }
}

Teks versus teks terstruktur

Menyusun teks dapat membantu memberikan konteks. Permintaan yang sama dengan yang ada dalam contoh sebelumnya, jika diperiksa sebagai string—yaitu, hanya "John Doe, [phone]"—akan memberikan temuan yang kurang akurat. Hal ini karena Perlindungan Data Sensitif memiliki lebih sedikit petunjuk kontekstual tentang tujuan angka tersebut. Jika memungkinkan, pertimbangkan untuk mengurai string menjadi objek tabel untuk mendapatkan hasil pemindaian yang paling akurat.