【GDPR】支持GDPR合规决策的人工智能框架:第六部分

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6讨论

为了支持意大利PA确保公共文件的GDPR合规性和个人数据的安全，我们制定了INTREPID，这是一个基于人工智能的框架，用于自动检测PA文件中的安全漏洞。作为我们框架的支柱，我们使用了为意大利语处理开发的语言资源，并调整了GDPR的情报。此外，我们定义了一个基于Bag of Word和NER信息的文本数据工程模块，并使用机器学习算法进行分类。最后，我们准备了一个意大利PA文件语料库，用于培训和评估，方法是使用适当的管道来平衡用人工标识符替换任何已识别或可识别信息的需要，以及GDPR检查不适用于匿名信息的事实。对准备好的语料库进行的深入评估强调了INTREPID的有效性以及它所建立的所有组件的设置。

除了INTREPID显示的结果的准确性之外，还需要解决一些限制，以朝着开发有效工具的方向迈出进一步的一步，降低PA文件中安全漏洞的风险。

  • 缺乏解释机制。如今，为了让最终用户接受自动决策过程，解释人工智能系统决策的能力至关重要。这与GDPR对所有决定（包括基于人工智能的决定）的“解释权”的评估一致，这些决定可能会对个人产生重大影响。为此，本研究未来的研究方向可以致力于探索可解释的人工智能机制，通过解释文本中如何发现数据泄露来丰富数据泄露警报。
  • 定位文档中的数据泄露位置。所提出的框架在文件一级执行分类任务。它允许我们识别可能不符合GDPR标准的PA文件，但这是在没有定位文件中的头寸数据泄露的情况下完成的。
  • 数据泄露的多样性。拟议框架的分类模型已经通过与非法披露健康信息有关的数据泄露进行了培训。未来的研究方向可以致力于将分类模型推广到各种数据海滩类别。
  • 多语言支持。拟议的框架是为意大利巴勒斯坦权力机构文件设计的。然而，最近出现了新的多语言模型，并已证明在各种文本分类任务中非常准确（Conneau et al.，2020）。这可以在用于数据泄露检测的多语言系统中进行探索。

7结论

在本文中，我们提出了一个新的基于人工智能的框架，以帮助意大利PA的数据保护工作流程自动化。所提出的框架是根据公共文件的数据保护可以被公式化为二进制文本分类问题的想法设计的。基于这一想法，我们准备了一个由意大利PA各城市在线发布的公共文件标记文本语料库。该语料库包含人类专家标记为符合GDPR或不符合GDPR的文本文件。我们描述了一个人工智能框架，从这个标记的文本语料库中学习文本分类模型，以便学习的模型可以用于预测新的公共文件是否符合GDPR标准。为此，我们选择了SpaCy和Tint这两种能够处理意大利语的NLP工具，并将其调整为GDPR情报。具体来说，我们使用NER工具来处理准备好的文本语料库，并定位几个类别的命名实体。我们介绍了在已识别的命名实体出现时提取的三组NER特征。我们利用这些NER功能丰富了文本文档的传统BoW表示，并训练分类器将文档标记为符合或不符合GDPR标准。我们使用了线性支持向量机、随机森林和XGboost作为分类算法。

我们根据NER的注释预测与领域专家的注释的一致性，以及文本分类模型的准确性对提取的特征组的敏感性，评估了所提出的框架的有效性。特别是，对准备好的文本语料库的评估表明，Tint在该领域的注释预测与领域专家的注释一致性方面优于SpaCy。它还表明，所提出的特征提取阶段工作得相当好，因为它使我们能够训练一个文本分类模型，该模型在检测数据泄露的文档时具有很高的准确性，误报率很低。这一结论可以独立于分类算法得出，尽管通过同时考虑基于BoW和基于NER的特征，使用XGBoost训练分类器获得了最高的精度性能。

到目前为止，据我们所知，这项研究首次尝试结合跨学科能力，以开发一个框架，帮助意大利PA自动化（或半自动化）分析公共文件的GDPR合规性。本研究的下一阶段将通过在文本语料库中包括可能涉及不同类别数据泄露的新文件来扩展对所提出框架的有效性的评估，并使用我们的注释语料库提高NER模型的性能。此外，还需要将该框架扩展到其他类别的个人数据，以及集成XAI技术来解释数据泄露警报，并开发人工智能技术来定位被标记为不符合GDPR标准的文件中的数据泄露位置。最后，我们计划探讨多语言资源在GDPR合规性分析问题中的表现。

代码可用性

根据合理要求，可从通讯作者处获得支持本研究结果的代码和为训练分类算法而提取的数据。

注意事项

  1. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), https://eur-lex.europa.eu/eli/reg/2016/679/oj

  2. Norms contained in the Italian Personal Data Protection Code (Legislative Decree 196/2003) were aligned with provisions introduced by GDPR with the legislative decree n. 101/2018 published in the Official Gazette n. 205 on September 4, 2018.

  3. https://www.dataguidance.com/news/italy-garante-fines-trento-health-authority-150000

  4. https://ec.europa.eu/info/sites/default/files/commission-white-paper-artificial-intelligence-feb2020_en.pd (last access: 2021/10/13)

  5. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206 (last access: 2021/10/13)

  6. We used doccano as the platform for the annotation: https://github.com/doccano/doccano.

  7. Legal references were extracted by the Linkoln tool https://gitlab.com/IGSG/LINKOLN/linkoln.

  8. https://tika.apache.org/

  9. https://github.com/tesseract-ocr/tesseract

  10. https://ufal.mff.cuni.cz/conll2009-st/task-description.html

  11. https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)

  12. https://spacy.io/

  13. https://tika.apache.org/

  14. https://spacy.io/

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Acknowledgements

We acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI (CUP H97G22000210007) under the NRRP MUR program funded by the NextGenerationEU, as well as the PON “Governance e capacità istituzionale” 2014–2020 project “Modelli, Sistemi e Competenze per l’implementazione dell’Ufficio per il Processo/Start UPP” (CUP: H29J22000390006), funded by the Italian Ministry for Universities and Research (MIUR).

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