This paper examines the security vulnerabilities and threats associated with AI-based healthcare systems, and analyses privacy-preserving techniques for protecting data during processing as well as post-quantum cryptographic algorithms for secure communication and authentication.
Kyriaki Tsantikidou, Nicolas Sklavos· ABH· 0 citations
AI-driven services continuously infer “models-of-users” to personalize content, automate decisions, and optimize interaction. Yet, most user models remain opaque, fragmented across platforms, and hard to correct, transfer, or revoke. As a result, personalisation in AI-driven services is commonly experienced as a trade-...