Compliance Generation for Privacy Documents under GDPR: A Roadmap for Implementing Automation and Machine Learning

• URL: http://arxiv.org/abs/2012.12718v1
• Date: Wed, 23 Dec 2020 14:46:51 GMT
• Title: Compliance Generation for Privacy Documents under GDPR: A Roadmap for Implementing Automation and Machine Learning
• Authors: David Restrepo Amariles, Aurore Cl\'ement Troussel, Rajaa El Hamdani
• Abstract summary: Privatech project focuses on corporations and law firms as agents of compliance. Data processors must implement accountability measures to assess and document compliance. We provide a roadmap for compliance assessment and generation by identifying compliance issues.
• Score: 2.1485350418225244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most prominent research today addresses compliance with data protection laws through consumer-centric and public-regulatory approaches. We shift this perspective with the Privatech project to focus on corporations and law firms as agents of compliance. To comply with data protection laws, data processors must implement accountability measures to assess and document compliance in relation to both privacy documents and privacy practices. In this paper, we survey, on the one hand, current research on GDPR automation, and on the other hand, the operational challenges corporations face to comply with GDPR, and that may benefit from new forms of automation. We attempt to bridge the gap. We provide a roadmap for compliance assessment and generation by identifying compliance issues, breaking them down into tasks that can be addressed through machine learning and automation, and providing notes about related developments in the Privatech project.

Related papers

• Advancing Android Privacy Assessments with Automation [5.863391019411233]
  This paper motivates the need for an automated approach that enhances understanding of data protection in Android apps. We propose Assessor View, a tool designed to bridge the knowledge gap between these parties facilitating more effective privacy assessments of Android applications.
  arXiv  Detail & Related papers  (2024-09-10T14:56:51Z)
• RegNLP in Action: Facilitating Compliance Through Automated Information Retrieval and Answer Generation [51.998738311700095]
  Regulatory documents, characterized by their length, complexity and frequent updates, are challenging to interpret. RegNLP is a multidisciplinary subfield aimed at simplifying access to and interpretation of regulatory rules and obligations. ObliQA dataset contains 27,869 questions derived from the Abu Dhabi Global Markets (ADGM) financial regulation document collection.
  arXiv  Detail & Related papers  (2024-09-09T14:44:19Z)
• How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review [15.15468770348023]
  We evaluate large language models' performance in privacy-related tasks such as privacy information extraction (PIE), legal and regulatory key point detection (KPD), and question answering (QA) Through an empirical assessment, we investigate the capacity of several prominent LLMs, including BERT, GPT-3.5, GPT-4, and custom models, in executing privacy compliance checks and technical privacy reviews. While LLMs show promise in automating privacy reviews and identifying regulatory discrepancies, significant gaps persist in their ability to fully comply with evolving legal standards.
  arXiv  Detail & Related papers  (2024-09-04T01:51:37Z)
• A BERT-based Empirical Study of Privacy Policies' Compliance with GDPR [9.676166100354282]
  This study aims to address challenge of compliance analysis between privacy policies for 5G networks. We manually collected privacy policies from almost 70 different MNOs and we utilized an automated BERT-based model for classification. In addition, we present first empirical evidence on the readability of privacy policies for 5G network. we adopted incorporates various established readability metrics.
  arXiv  Detail & Related papers  (2024-07-09T11:47:52Z)
• Demystifying Legalese: An Automated Approach for Summarizing and Analyzing Overlaps in Privacy Policies and Terms of Service [0.6240153531166704]
  Our work seeks to alleviate this issue by developing language models that provide automated, accessible summaries and scores for such documents. We compared transformer-based and conventional models during training on our dataset, and RoBERTa performed better overall with a remarkable 0.74 F1-score.
  arXiv  Detail & Related papers  (2024-04-17T19:53:59Z)
• Towards an Enforceable GDPR Specification [49.1574468325115]
  Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's. One emerging technique to realize PbD is enforcement (RE) We present a set of requirements and an iterative methodology for creating formal specifications of legal provisions.
  arXiv  Detail & Related papers  (2024-02-27T09:38:51Z)
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
  We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
  arXiv  Detail & Related papers  (2023-04-21T09:03:18Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
  We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems. We highlight opportunities and challenges in AI systems using trusted execution environments. We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
  arXiv  Detail & Related papers  (2020-08-10T23:05:55Z)
• GDPR: When the Right to Access Personal Data Becomes a Threat [63.732639864601914]
  We examine more than 300 data controllers performing for each of them a request to access personal data. We find that 50.4% of the data controllers that handled the request, have flaws in the procedure of identifying the users. With the undesired and surprising result that, in its present deployment, has actually decreased the privacy of the users of web services.
  arXiv  Detail & Related papers  (2020-05-04T22:01:46Z)
• Machine Understandable Policies and GDPR Compliance Checking [9.032680855473986]
  Towards SPECIAL H2020 project aims to provide a set of tools that can be used by data controllers that automatically check if personal data sharing complies with obligations set forth with obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with regulatory obligations set forth with
  arXiv  Detail & Related papers  (2020-01-24T09:41:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.