Related papers: VPAS: Publicly Verifiable and Privacy-Preserving A…
The current humanitarian health crisis popularized the debate on data privacy. At the same time, several cities, states, and even countries put the mandatory presentation of health pass to access services into practice. In this article, we…
Privacy data protection in the medical field poses challenges to data sharing, limiting the ability to integrate data across hospitals for training high-precision auxiliary diagnostic models. Traditional centralized training methods are…
Vehicular ad-hoc networks (VANETs) have been proposed to automate transportation industry in order to increase its accuracy, efficiency, throughput, and specially safety. Security plays an Undeniable important role on implementing VANETs in…
We consider the critical problem of distributed learning over data while keeping it private from the computational servers. The state-of-the-art approaches to this problem rely on quantizing the data into a finite field, so that the…
In this paper, we propose FairCrowd, a private, fair, and verifiable framework for aggregate statistics in mobile crowdsensing based on the public blockchain. In specific, mobile users are incentivized to collect and share private data…
The Internet of Things (IoT) has become increasingly popular in people's daily lives. The pervasive IoT devices are encouraged to share data with each other in order to better serve the users. However, users are reluctant to share sensitive…
Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client…
The growing societal reliance on artificial intelligence necessitates robust frameworks for ensuring its security, accountability, and trustworthiness. This thesis addresses the complex interplay between privacy, verifiability, and…
With the emergence of privacy leaks in federated learning, secure aggregation protocols that mainly adopt either homomorphic encryption or threshold secret sharing have been widely developed for federated learning to protect the privacy of…
Privacy-preserving federated averaging is a central approach for protecting client privacy in federated learning. In this paper, we study this problem in an asynchronous communications setting with malicious aggregators. We propose a new…
The amount of personal data collected in our everyday interactions with connected devices offers great opportunities for innovative services fueled by machine learning, as well as raises serious concerns for the privacy of individuals. In…
We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical…
Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. This paper introduces an aggregated zero-knowledge proof and…
Many applications benefit from computations over the data of multiple users while preserving confidentiality. We present a solution where multiple mutually distrusting users' data can be aggregated with an acceptable overhead, while…
The wide deployment of the generative pre-trained transformer (GPT) has raised privacy concerns for both clients and servers. While cryptographic primitives can be employed for secure GPT inference to protect the privacy of both parties,…
The iterative consensus problem requires a set of processes or agents with different initial values, to interact and update their states to eventually converge to a common value. Protocols solving iterative consensus serve as building…
Data and data processing have become an indispensable aspect for our society. Insights drawn from collective data make invaluable contribution to scientific and societal research and business. But there are increasing worries about privacy…
We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multi-party computation (MPC). ORQ protects data against semi-honest or malicious parties and can efficiently evaluate…
Structured P2P overlays provide a framework for building distributed applications that are self-configuring, scalable, and resilient to node failures. Such systems have been successfully adopted in large-scale Internet services such as…
Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significantly among different users. To better utilize sensory data,…