124 research outputs found
Building a Collaborative Phone Blacklisting System with Local Differential Privacy
Spam phone calls have been rapidly growing from nuisance to an increasingly
effective scam delivery tool. To counter this increasingly successful attack
vector, a number of commercial smartphone apps that promise to block spam phone
calls have appeared on app stores, and are now used by hundreds of thousands or
even millions of users. However, following a business model similar to some
online social network services, these apps often collect call records or other
potentially sensitive information from users' phones with little or no formal
privacy guarantees.
In this paper, we study whether it is possible to build a practical
collaborative phone blacklisting system that makes use of local differential
privacy (LDP) mechanisms to provide clear privacy guarantees. We analyze the
challenges and trade-offs related to using LDP, evaluate our LDP-based system
on real-world user-reported call records collected by the FTC, and show that it
is possible to learn a phone blacklist using a reasonable overall privacy
budget and at the same time preserve users' privacy while maintaining utility
for the learned blacklist.Comment: 15 pages, 10 figures, 7 algorithm
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