Concept
Secure multi-party computation
Related publications (112)
Anonymous Tokens with Stronger Metadata Bit Hiding from Algebraic MACs
Serge Vaudenay, Fatma Betül Durak
On the one hand, the web needs to be secured from malicious activities such as bots or DoS attacks; on the other hand, such needs ideally should not justify services tracking people's activities on the web. Anonymous tokens provide a nice tradeoff between ...
2023Enforcing Privacy in Distributed Learning With Performance Guarantees
Ali H. Sayed, Stefan Vlaski, Elsa Rizk
We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...
Piscataway2023Authenticated private information retrieval
Bryan Alexander Ford, Simone Maria Stefano Colombo, Henry Nathaniel Corrigan-Gibbs, Kirill Nikitin
This paper introduces protocols for authenticated private information retrieval. These schemes enable a client to fetch a record from a remote database server such that (a) the server does not learn which record the client reads, and (b) the client either ...
Berkeley2023Efficient protocols for oblivious linear function evaluation from ring-LWE
An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function f (x) = ax + b, where a sender inputs the field elements a, b, and a receiver inputs x and learns f (x). OLE can be used to build secret-shared multiplicatio ...
IOS PRESS2022Differentially Private Multi-Agent Constraint Optimization
Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle
Several optimization scenarios involve multiple agents that desire to protect the privacy of their preferences. There are distributed algorithms for constraint optimization that provide improved privacy protection through secure multiparty computation. How ...
ACM2022Blockchain-based Practical Multi-agent Secure Comparison and its Application in Auctions
Boi Faltings, Sujit Prakash Gujar, Sankarshan Damle
AI applications find widespread use in a variety of domains. For further acceptance, mostly when multiple agents interact with the system, we must aim to preserve the privacy of participants information in such applications. Towards this, the Yao’s Million ...
ACM2022Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets
Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis, Jeffrey Chen, David Jules Froelicher
Sharing data across multiple institutions for genome-wide association studies (GWAS) would enable discovery of novel genetic variants linked to health and disease. However, existing regulations on genomic data sharing and the sheer size of the data limit t ...
2022Why the search for a privacy-preserving data sharing mechanism is failing
Carmela González Troncoso, Theresa Stadler
The rapidly growing demand to share data more openly creates a need for secure and privacy-preserving sharing technologies. However, there are multiple challenges associated with the development of a universal privacy-preserving data sharing mechanism, and ...
SPRINGERNATURE2022Optimal Symmetric Ratcheting for Secure Communication
Serge Vaudenay, Andrea Felice Caforio, Daniel Patrick Collins, Hailun Yan
To mitigate state exposure threats to long-lived instant messaging sessions, ratcheting was introduced, which is used in practice in protocols like Signal. However, existing ratcheting protocols generally come with a high cost. Recently, Caforio et al. pro ...
OXFORD UNIV PRESS2022Multiparty Homomorphic Encryption from Ring-Learning-with-Errors
Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Christian Vincent Mouchet
We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-honest model with dishonest majority that is based on multiparty homomorphic encryption (MHE). To support our solution, we introduce a multiparty version of the Brakerski-Fa ...
2021