Rethinking Data Protection in the (Generative) Artificial Intelligence Era

• URL: http://arxiv.org/abs/2507.03034v1
• Date: Thu, 03 Jul 2025 02:45:51 GMT
• Title: Rethinking Data Protection in the (Generative) Artificial Intelligence Era
• Authors: Yiming Li, Shuo Shao, Yu He, Junfeng Guo, Tianwei Zhang, Zhan Qin, Pin-Yu Chen, Michael Backes, Philip Torr, Dacheng Tao, Kui Ren,
• Abstract summary: We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
• Score: 115.71019708491386
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The (generative) artificial intelligence (AI) era has profoundly reshaped the meaning and value of data. No longer confined to static content, data now permeates every stage of the AI lifecycle from the training samples that shape model parameters to the prompts and outputs that drive real-world model deployment. This shift renders traditional notions of data protection insufficient, while the boundaries of what needs safeguarding remain poorly defined. Failing to safeguard data in AI systems can inflict societal and individual, underscoring the urgent need to clearly delineate the scope of and rigorously enforce data protection. In this perspective, we propose a four-level taxonomy, including non-usability, privacy preservation, traceability, and deletability, that captures the diverse protection needs arising in modern (generative) AI models and systems. Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline, including training datasets, model weights, system prompts, and AI-generated content. We analyze representative technical approaches at each level and reveal regulatory blind spots that leave critical assets exposed. By offering a structured lens to align future AI technologies and governance with trustworthy data practices, we underscore the urgency of rethinking data protection for modern AI techniques and provide timely guidance for developers, researchers, and regulators alike.

Related papers

• Assured Autonomy with Neuro-Symbolic Perception [11.246557832016238]
  Many state-of-the-art AI models deployed in cyber-physical systems (CPS) are pattern-matchers.<n>With limited security guarantees, there are concerns for their reliability in safety-critical and contested domains.<n>We propose a paradigm shift that imbues data-driven perception models with symbolic structure.
  arXiv  Detail & Related papers  (2025-05-27T15:21:06Z)
• Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation [0.0]
  In specialized domains such as healthcare, data acquisition faces significant constraints due to privacy regulations, ethical considerations, and limited availability.<n>The emergence of large-scale prompt-based models presents new opportunities for synthetic data generation without direct access to protected data.<n>We review recent developments in automatic prompt optimization, following PRISMA guidelines.
  arXiv  Detail & Related papers  (2025-02-05T11:13:03Z)
• Technical Report for the Forgotten-by-Design Project: Targeted Obfuscation for Machine Learning [0.03749861135832072]
  This paper explores the concept of the Right to be Forgotten (RTBF) within AI systems, contrasting it with traditional data erasure methods.<n>We introduce Forgotten by Design, a proactive approach to privacy preservation that integrates instance-specific obfuscation techniques.<n>Our experiments on the CIFAR-10 dataset demonstrate that our techniques reduce privacy risks by at least an order of magnitude while maintaining model accuracy.
  arXiv  Detail & Related papers  (2025-01-20T15:07:59Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• The AI Security Pyramid of Pain [0.18820558426635298]
  We introduce the AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats. This framework provides a structured approach to understanding and addressing various levels of AI threats.
  arXiv  Detail & Related papers  (2024-02-16T21:14:11Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• Representative & Fair Synthetic Data [68.8204255655161]
  We present a framework to incorporate fairness constraints into the self-supervised learning process. We generate a representative as well as fair version of the UCI Adult census data set. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
  arXiv  Detail & Related papers  (2021-04-07T09:19:46Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)

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.