Both ends of artificial intelligence impacting privacy: a review of violation and protection
Abstract
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.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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