Abstract: The growth of machine learning and advanced information retrieval raises serious privacy concerns when sensitive data is shared with untrusted parties. This work enables similar computations ...

Abstract: The rapid development and wide application of blockchain not only highlight the significance of privacy protection (including anonymity and confidentiality) but also the necessity of ...

These methods include secure multiparty computation and homomorphic encryption ( 46 , 47 ). Reviews in healthcare indicate that cryptographic protection can entail high computational costs. These ...

Belief-Space Quantum-Inspired Reinforcement Learning for Partially Observable Autonomous Cyber Defense in the Internet of Vehicles Anwar Shah , Rohan Farooq , Sajid Anwer , Tallha Akram , Usman Ghous ...

In the long term (2–3 + years), emerging methods such as zero-shot detection and homomorphic encryption are highlighted as future enablers of adaptive, privacy-preserving threat intelligence—though ...

Scaling Deep-Learning Inference with Chiplet-based Architecture and Photonic Interconnects Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices Designing a ...