Towards Post-mortem Data Management Principles for Generative AI

• URL: http://arxiv.org/abs/2509.07375v2
• Date: Wed, 10 Sep 2025 19:31:25 GMT
• Title: Towards Post-mortem Data Management Principles for Generative AI
• Authors: Elina Van Kempen, Ismat Jarin, Chloe Georgiou,
• Abstract summary: Foundation models, large language models (LLMs), and agentic AI systems rely heavily on vast corpora of user data.<n>The use of such data for training has raised persistent concerns around ownership, copyright, and potential harms.<n>We propose three post-mortem data management principles to guide the protection of deceased individuals data rights.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models, large language models (LLMs), and agentic AI systems rely heavily on vast corpora of user data. The use of such data for training has raised persistent concerns around ownership, copyright, and potential harms. In this work, we explore a related but less examined dimension: the ownership rights of data belonging to deceased individuals. We examine the current landscape of post-mortem data management and privacy rights as defined by the privacy policies of major technology companies and regulations such as the EU AI Act. Based on this analysis, we propose three post-mortem data management principles to guide the protection of deceased individuals data rights. Finally, we discuss directions for future work and offer recommendations for policymakers and privacy practitioners on deploying these principles alongside technological solutions to operationalize and audit them in practice.

Related papers

• Data Sharing, Privacy and Security Considerations in the Energy Sector: A Review from Technical Landscape to Regulatory Specifications [49.567747749614924]
  Decarbonization, decentralization and digitalization are the three key elements driving the twin energy transition.<n>This paper conducts a comprehensive review of the data-related issues for the energy system by integrating both technical and regulatory dimensions.<n>We classify the issues into three categories: (i) data-sharing among energy end users and stakeholders (ii) privacy of end users, and (iii) cyber security.
  arXiv  Detail & Related papers  (2025-03-05T14:23:56Z)
• How to Manage My Data? With Machine--Interpretable GDPR Rights! [0.0]
  The EU is a landmark regulation that introduced several rights for individuals to obtain information and control how their personal data is being processed.<n>There are gaps in the effective use of rights due to each organisation developing custom methods for rights declaration and management.<n>We present a specification for exercising and managing rights in a machine-interpretable format based on semantic web standards.
  arXiv  Detail & Related papers  (2024-12-19T23:09:17Z)
• Towards Data Governance of Frontier AI Models [0.0]
  We look at how data can enable new governance capacities for frontier AI models.<n>Data is non-rival, often non-excludable, easily replicable, and increasingly synthesizable.<n>We propose a set of policy mechanisms targeting key actors along the data supply chain.
  arXiv  Detail & Related papers  (2024-12-05T02:37:51Z)
• Assessing Privacy Policies with AI: Ethical, Legal, and Technical Challenges [6.916147439085307]
  Large Language Models (LLMs) can be used to assess privacy policies for users automatically. We explore the challenges of this approach in three pillars, namely technical feasibility, ethical implications, and legal compatibility. Our findings aim to identify potential for future research, and to foster a discussion on the use of LLM technologies.
  arXiv  Detail & Related papers  (2024-10-10T21:36:35Z)
• 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)
• Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
  Security measures such as anonymization are needed to protect individuals' privacy. Within our study, we conducted expert interviews to gain insights into practices in the field. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
  arXiv  Detail & Related papers  (2024-07-04T08:29:27Z)
• SoK: The Gap Between Data Rights Ideals and Reality [42.769107967436945]
  Do rights-based privacy laws effectively empower individuals over their data?<n>This paper scrutinizes these approaches by reviewing empirical studies, news articles, and blog posts.
  arXiv  Detail & Related papers  (2023-12-03T21:52:51Z)
• 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)
• Second layer data governance for permissioned blockchains: the privacy management challenge [58.720142291102135]
  In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data is crucial to avoid the massive infection and decrease the number of deaths. In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts.
  arXiv  Detail & Related papers  (2020-10-22T13:19:38Z)
• 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.