E-commerce companies are actively considering the use of delivery drones for customer
fulfillment, leading to growing concerns around citizen privacy. Drones are equipped with …

Secure multi-party computation (MPC) has recently become prominent as a concept to
enable multiple parties to perform privacy-preserving machine learning without leaking …

Well-trained neural network models are deployed on edge servers to provide valuable
inference services for clients. To protect the client data privacy, a promising way is to exploit …

Garbled Circuit (GC) is a basic technique for practical secure computation. GC handles
Boolean circuits; it consumes significant network bandwidth to transmit encoded gate truth …

Neural networks, as one of the most significant techniques in artificial intelligence, play a
crucial role in various tasks associated with intelligent connected vehicles (ICVs), including …

This paper introduces Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to
third parties that they are within a specified geographical region while not disclosing their …

Private convolutional neural network (CNN) inference based on secure two-party
computation (2PC) suffers from high communication and latency overhead, especially from …

As large language models (LLMs) continue to gain popularity, concerns about user privacy
are amplified, given that the data submitted by users for inference may contain sensitive …