Vuvuzela: scalable private messaging resistant to traffic analysis
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Author(s)
van den Hooff, Jelle
Lazar, David
Zeldovich, Nickolai
Zaharia, Matei A.
Date Issued
October 2015
Journal
Proceedings of the 25th Symposium on Operating Systems Principles (SOSP '15)
Publisher
Association for Computing Machinery (ACM)
Citation
Jelle van den Hooff, David Lazar, Matei Zaharia, and Nickolai Zeldovich. 2015. Vuvuzela: scalable private messaging resistant to traffic analysis. In Proceedings of the 25th Symposium on Operating Systems Principles (SOSP '15). ACM, New York, NY, USA, 137-152.
Version
Final published version
Abstract
Private messaging over the Internet has proven challenging to implement, because even if message data is encrypted, it is difficult to hide metadata about who is communicating in the face of traffic analysis. Systems that offer strong privacy guarantees, such as Dissent [36], scale to only several thousand clients, because they use techniques with superlinear cost in the number of clients (e.g., each client broadcasts their message to all other clients). On the other hand, scalable systems, such as Tor, do not protect against traffic analysis, making them ineffective in an era of pervasive network monitoring.
Vuvuzela is a new scalable messaging system that offers strong privacy guarantees, hiding both message data and metadata. Vuvuzela is secure against adversaries that observe and tamper with all network traffic, and that control all nodes except for one server. Vuvuzela's key insight is to minimize the number of variables observable by an attacker, and to use differential privacy techniques to add noise to all observable variables in a way that provably hides information about which users are communicating. Vuvuzela has a linear cost in the number of clients, and experiments show that it can achieve a throughput of 68,000 messages per second for 1 million users with a 37-second end-to-end latency on commodity servers.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/2815400.2815417
