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TSS/SoS Seminar: Spy vs. Spy: Anonymous Messaging over Networks

Event Type
Seminar/Symposium
Sponsor
Information Trust Institute
Location
Coordinated Science Lab Auditorium (B02)
Date
Aug 30, 2016   4:00 pm  
Speaker
Giulia Fanti, University of Illinois at Urbana-Champaign
Views
97

BIOGRAPHY:

Giulia Fanti is a postdoc at the University of Illinois at Urbana-Champaign, studying privacy-preserving technologies under Professor Pramod Viswanath. She previously obtained her Ph.D. and M.S. in EECS from U.C. Berkeley under Professor Kannan Ramchandran, and her B.S. in ECE from Olin College of Engineering in 2010. She is a recipient of the National Science Foundation Graduate Research Fellowship, as well as a Best Paper Award at ACM Sigmetrics 2015 for her work on anonymous rumor spreading, in collaboration with Peter Kairouz, Professor Sewoong Oh and Professor Pramod Viswanath of the University of Illinois at Urbana-Champaign.

ABSTRACT:

Anonymous microblogging platforms, such as Whisper, Yik Yak, and Secret have emerged as important tools for sharing one’s thoughts without fear of judgment by friends, the public, or authorities. These platforms provide anonymity by allowing users to share content (e.g., short messages) with their peers without revealing authorship information to other users. However, recent advances in rumor source detection show that existing messaging protocols, including those used in the mentioned anonymous microblogging applications, leak authorship information when the adversary has global access to metadata. For example, if an adversary can see which users of a messaging service received a particular message, or the timestamps at which a subset of users received a given message, the adversary can infer the message author’s identity with high probability. We introduce a novel anonymous messaging protocol, which we call adaptive diffusion, that is designed to resist such adversaries. We show that adaptive diffusion spreads messages quickly while achieving provably-optimal anonymity guarantees for specific classes of connectivity networks. Simulations on real social network data show that adaptive diffusion effectively hides the location of the source on real-world networks.

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