SYSTEMATIC REVIEW article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
This article is part of the Research TopicAI-Driven Architectures and Algorithms for Secure and Scalable Big Data SystemsView all 9 articles
Both Ends of Artificial Intelligence Impacting Privacy: A Review of Violation and Protection
Provisionally accepted- Ariel University, Ari'el, Israel
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The intersection of Artificial Intelligence (AI) and privacy presents both significant challenges and opportunities. As AI systems become increasingly embedded in many aspects of our lives, including healthcare, finance, and social networks, and introduce significant concerns regarding privacy issues – the need for effective privacy-preserving mechanisms also grows. This review systematically analyzes 94 research papers in the field of AI and privacy. To model this complex issue, we categorized privacy in AI through a multi-dimensional approach that includes technological domains' privacy actions, privacy-preserving strategies, and AI-privacy interaction directions. A novel technique based on a Graph Database (Neo4J) which is available to the reader was employed to facilitate visualization of the complex relations between the reviewed objects. Moreover, the Graph, which is actually the review, can be queried and updated with future publications. Key findings indicate that AI can be both a potential threat to privacy, for example due to inference risks and data exploitation, as well as a tool for enhancing privacy through techniques such as federated learning and differential privacy. The study highlights regulatory, ethical, and technical challenges, emphasizing the need for interdisciplinary collaboration.
Keywords: analysis, Artificial intelligence (AI), Graph Database (GDB), Machine Learning (ML), Privacy, review
Received: 15 Aug 2025; Accepted: 28 Jan 2026.
Copyright: © 2026 Voloch and Hirschprung. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Nadav Voloch
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