SoK: Technical Implementation and Human Impact of Internet Privacy Regulations

• URL: http://arxiv.org/abs/2312.15383v1
• Date: Sun, 24 Dec 2023 01:48:07 GMT
• Title: SoK: Technical Implementation and Human Impact of Internet Privacy Regulations
• Authors: Eleanor Birrell, Jay Rodolitz, Angel Ding, Jenna Lee, Emily McReynolds, Jevan Hutson, Ada Lerner
• Abstract summary: We analyze a set of Internet privacy and data protection regulations drawn from around the world. We develop a taxonomy of rights granted and obligations imposed by these laws. We then leverage this taxonomy to systematize 270 technical research papers.
• Score: 2.797211052758564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Growing recognition of the potential for exploitation of personal data and of the shortcomings of prior privacy regimes has led to the passage of a multitude of new online privacy regulations. Some of these laws -- notably the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) -- have been the focus of large bodies of research by the computer science community, while others have received less attention. In this work, we analyze a set of Internet privacy and data protection regulations drawn from around the world -- both those that have frequently been studied by computer scientists and those that have not -- and develop a taxonomy of rights granted and obligations imposed by these laws. We then leverage this taxonomy to systematize 270 technical research papers published in computer science venues that investigate the impact of these laws and explore how technical solutions can complement legal protections. Finally, we analyze the results in this space through an interdisciplinary lens and make recommendations for future work at the intersection of computer science and legal privacy.

Related papers

• You Still See Me: How Data Protection Supports the Architecture of AI Surveillance [5.989015605760986]
  We show how privacy-preserving techniques in the development of AI systems can support surveillance infrastructure under the guise of regulatory permissibility. We propose technology and policy strategies to evaluate privacy-preserving techniques in light of the protections they actually confer.
  arXiv  Detail & Related papers  (2024-02-09T18:39:29Z)
• SoK: The Gap Between Data Rights Ideals and Reality [46.14715472341707]
  Do rights-based privacy laws effectively empower individuals over their data? This paper scrutinizes these approaches by reviewing empirical studies, news articles, and blog posts.
  arXiv  Detail & Related papers  (2023-12-03T21:52:51Z)
• Biometric Technologies and the Law: Developing a Taxonomy for Guiding Policymakers [0.0]
  This study proposes a taxonomy of biometric technologies that can aid in their effective deployment and supervision. The resulting taxonomy can enhance the understanding of biometric technologies and facilitate the development of regulation that prioritises privacy and personal data protection.
  arXiv  Detail & Related papers  (2023-10-27T10:23:46Z)
• Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment [100.1798289103163]
  We present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP) Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier" This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions.
  arXiv  Detail & Related papers  (2023-04-14T05:29:18Z)
• Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset [46.156169284961045]
  We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material. First, we gather and make available the Pile of Law, a 256GB dataset of open-source English-language legal and administrative data. Second, we distill the legal norms that governments have developed to constrain the inclusion of toxic or private content into actionable lessons. Third, we show how the Pile of Law offers researchers the opportunity to learn such filtering rules directly from the data.
  arXiv  Detail & Related papers  (2022-07-01T06:25:15Z)
• Creation and Analysis of an International Corpus of Privacy Laws [7.45571096955396]
  We introduce the Government Privacy Instructions Corpus, or GPI Corpus, of 1,043 privacy laws, regulations, and guidelines, covering 182 jurisdictions. We examine the temporal distribution of when GPIs were created and illustrate the dramatic increase in privacy legislation over the past 50 years. Our exploration also demonstrates that most privacy laws respectively address relatively few personal data types, showing that comprehensive privacy legislation remains rare.
  arXiv  Detail & Related papers  (2022-06-28T17:36:12Z)
• 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)
• A vision for global privacy bridges: Technical and legal measures for international data markets [77.34726150561087]
  Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil" An open conflict is arising between business demands for data and a desire for privacy. We propose and test a vision of a personal information market with privacy.
  arXiv  Detail & Related papers  (2020-05-13T13:55:50Z)
• Beyond privacy regulations: an ethical approach to data usage in transportation [64.86110095869176]
  We describe how Federated Machine Learning can be applied to the transportation sector. We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
  arXiv  Detail & Related papers  (2020-04-01T15:10:12Z)

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.