SoK: Privacy-Enhancing Technologies in Artificial Intelligence

• URL: http://arxiv.org/abs/2506.14576v1
• Date: Tue, 17 Jun 2025 14:32:01 GMT
• Title: SoK: Privacy-Enhancing Technologies in Artificial Intelligence
• Authors: Nouha Oualha,
• Abstract summary: Privacy-enhancing technologies (PETs) have emerged as a suite of digital tools that enable data collection and processing while preserving privacy.<n>This paper explores the current landscape of data privacy in the context of AI, reviews the integration of PETs within AI systems, and assesses both their achievements and the challenges that remain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As artificial intelligence (AI) continues to permeate various sectors, safeguarding personal and sensitive data has become increasingly crucial. To address these concerns, privacy-enhancing technologies (PETs) have emerged as a suite of digital tools that enable data collection and processing while preserving privacy. This paper explores the current landscape of data privacy in the context of AI, reviews the integration of PETs within AI systems, and assesses both their achievements and the challenges that remain.

Related papers

• Rethinking Data Protection in the (Generative) Artificial Intelligence Era [115.71019708491386]
  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.
  arXiv  Detail & Related papers  (2025-07-03T02:45:51Z)
• Privacy Preservation in Gen AI Applications [0.0]
  Large Language Models (LLMs) may unintentionally absorb and reveal Personally Identifiable Information (PII) from user interactions.<n>Deep neural networks' intricacy makes it difficult to track down or stop the inadvertent storing and release of private information.<n>This study tackles these issues by detecting Generative AI weaknesses through attacks such as data extraction, model inversion, and membership inference.<n>It ensures privacy without sacrificing functionality by using methods to identify, alter, or remove PII before to dealing with LLMs.
  arXiv  Detail & Related papers  (2025-04-12T06:19:37Z)
• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs.<n>Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.<n>Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• A Narrative Review of Identity, Data, and Location Privacy Techniques in Edge Computing and Mobile Crowdsourcing [2.5944208050492183]
  This review focuses on the need for privacy protection in mobile crowdsourcing and edge computing. We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy. This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers.
  arXiv  Detail & Related papers  (2024-01-20T19:32:56Z)
• Privacy-Preserving Data in IoT-based Cloud Systems: A Comprehensive Survey with AI Integration [0.0]
  This survey paper meticulously explores the landscape of privacy issues in the dynamic intersection of IoT and cloud systems. The comprehensive literature review synthesizes existing research, illuminating key challenges and discerning emerging trends in privacy preserving techniques.
  arXiv  Detail & Related papers  (2024-01-01T15:48:39Z)
• Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective [28.968233485060654]
  We discuss the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.
  arXiv  Detail & Related papers  (2023-11-30T05:03:08Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Security and Privacy on Generative Data in AIGC: A Survey [17.456578314457612]
  We review the security and privacy on generative data in AIGC. We reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance.
  arXiv  Detail & Related papers  (2023-09-18T02:35:24Z)
• Elephants and Algorithms: A Review of the Current and Future Role of AI in Elephant Monitoring [47.24825031148412]
  Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behavior and conservation strategies. Using elephants, a crucial species in Africa's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked.
  arXiv  Detail & Related papers  (2023-06-23T22:35:51Z)
• Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply Privacy- and Authenticity-Enhancing Technologies [67.34625604583208]
  Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) are considered to engage in Sovereign Data Exchange (SDE) PETs and AETs are technically complex, which impedes their adoption. This study empirically constructs a challenge-oriented technology mapping.
  arXiv  Detail & Related papers  (2022-06-20T08:16:42Z)
• More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence [62.3133247463974]
  We show that differential privacy can do more than just privacy preservation in AI. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI.
  arXiv  Detail & Related papers  (2020-08-05T03:07:36Z)

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.