Riffle

By Albert Kwon, David Lazar, Srini Devadas, Bryan Ford. 2016

86 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, ML Systems & Hardware

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. H. Brendan McMahan
2. Srinath T. V. Setty
3. Jakub Konecný
4. Daniel Ramage
5. Michael Walfish
6. Amir Salman Avestimehr
7. Keith Bonawitz
8. Qiang Yang

In Computer Networks and Communications

1. Michael Walfish
2. Srinath T. V. Setty
3. H. Brendan McMahan
4. Daniel Ramage
5. Lorenzo Alvisi
6. Sarvar Patel
7. Ben Kreuter
8. Elad Hazan

In ML Systems & Hardware

1. H. Brendan McMahan
2. Michael Walfish
3. Daniel Ramage
4. Srinath T. V. Setty

Citations in the corpus

• Distributed Differential Privacy via Shuffling (2018, neutral): “, [22, 5] and citations therein).”
• Scalable and Private Media Consumption with Popcorn (2016, background): “In very recent work, Riffle [70], like Popcorn, targets the case n.”
• Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017, background): “message, the protocol must be restarted from scratch, which can be very expensive [32].”