: Smart healthcare systems integrate the Internet of Things (IoT) and blockchain technologies, cloud infrastructures and advanced data analytic models to enable continuous monitoring, predictive diagnosis and intelligent decision making. However, the distributed nature of these systems, together with blockchain-based infrastructures and third-party processing, introduces significant privacy and security challenges. In parallel, the emergence of quantum computing threatens the long-term security of conventional cryptographic mechanisms. This paper examines the security vulnerabilities and threats associated with AI-based healthcare systems. It analyses privacy-preserving techniques for protecting data during processing as well as post-quantum cryptographic algorithms for secure communication and authentication. Finally, key challenges and future directions are discussed, highlighting the potential of combining homomorphic encryption and post-quantum cryptography for developing secure, resilient and hardware-efficient healthcare systems.
Kyriaki Tsantikidou, Nicolas Sklavos· Proceedings of the Internati...· 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-off between privacy and platform lock-in. Users must stay loyal to platforms if a model of adequate utility is to be built around them. This tension is sharpened by emerging regulatory obligations around transparency and user oversight (e.g., GDPR and the EU AI Act). We propose a full-day EICS 2026 workshop that focuses on engineering cross-compatible personalization: a vision for future interactive systems in which users can make their digital self-representations inspectable, editable, and selectively shareable. The workshop brings together researchers and practitioners from interactive systems engineering, HCI, AI/ML, and security/privacy to (1) map the design and engineering space of user-sovereign personalization; (2) derive reusable artifacts for the EICS community, such as a reference architecture, protocol sketches, and a pattern language of interaction techniques; and (3) seed a community around building interoperable toolchains for AI personalization that is not only accurate, but also controllable, auditable, and context-adaptive.
