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Riffle
An Efficient Communication System With Strong Anonymity

Abstract

Existing anonymity systems sacrifice anonymity for efficient communication or vice-versa. Onion-routing achieves low latency, high bandwidth, and scalable anonymous communication, but is susceptible to traffic analysis attacks. Designs based on DC-Nets, on the other hand, protect the users against traffic analysis attacks, but sacrifice bandwidth. Verifiable mixnets maintain strong anonymity with low bandwidth overhead, but suffer from high computation overhead instead.

In this paper, we present Riffle, a bandwidth and computation efficient communication system with strong anonymity. Riffle consists of a small set of anonymity servers and a large number of users, and guarantees anonymity among all honest clients as long as there exists at least one honest server. Riffle uses a new hybrid verifiable shuffle technique and private information retrieval for bandwidth- and computation-efficient anonymous communication. Our evaluation of Riffle in file sharing and microblogging applications shows that Riffle can achieve a bandwidth of over 100KB/s per user in an anonymity set of 200 users in the case of file sharing, and handle over 100,000 users with less than 10 second latency in the case of microblogging.

Open access
UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Abstract

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present UnLynx, a new decentralized system for efficient privacy-preserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. UnLynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, UnLynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. UnLynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that UnLynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.

Open access