Inspecter du texte structuré pour identifier les données sensibles

Cloud Data Loss Prevention peut détecter et classer les données sensibles au sein de contenus structurés tels que des fichiers CSV. En inspectant ou en anonymisant une table, la structure et les colonnes fournissent à Cloud DLP des indices supplémentaires qui peuvent l'aider à fournir de meilleurs résultats pour certains cas d'utilisation.

Inspecter une table

Les exemples de code ci-dessous montrent comment procéder pour identifier les contenus sensibles dans une table de données. Une grande variété de types sont compatibles avec les tables.

Important : Pour exécuter ce code, vous devez d'abord configurer un client Cloud DLP. Pour plus d'informations sur l'installation et la création d'un client Cloud DLP, consultez la page Bibliothèques clientes Cloud DLP. (L'envoi de code JSON à des points de terminaison REST Cloud DLP ne nécessite pas de bibliothèque cliente.)

Protocole

Pour découvrir comment utiliser le format JSON avec l'API DLP, consultez le démarrage rapide JSON.

Entrée 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
  }
}

Sortie 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"
     }
    ]
  }
}

Java

Pour savoir comment installer et utiliser la bibliothèque cliente pour Cloud DLP, consultez Bibliothèques clientes Cloud DLP.

Pour vous authentifier auprès de Cloud DLP, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.

Afficher sur GitHub Commentaires

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());
    }
  }
}

Python

Pour savoir comment installer et utiliser la bibliothèque cliente pour Cloud DLP, consultez Bibliothèques clientes Cloud DLP.

Pour vous authentifier auprès de Cloud DLP, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.

Afficher sur GitHub Commentaires

from typing import List, Optional  # noqa: E402, I100

import google.cloud.dlp  # noqa: F811, E402

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.")

Texte et texte structuré

Un texte structurant peut aider à fournir un contexte. La même requête que celle de l'exemple précédent, si elle était inspectée sous forme de chaîne (comme simplement "John Doe, [phone]"), fournirait des résultats moins précis. En effet, Cloud DLP dispose de moins d'indices contextuels sur le but du numéro. Dans la mesure du possible, envisagez d'analyser les chaînes dans un objet de table pour obtenir des résultats d'analyse plus précis.