Building a Collaborative Phone Blacklisting System with Local Differential Privacy
This addresses privacy concerns in spam-blocking apps for smartphone users, offering a practical solution with formal guarantees, though it is incremental in applying LDP to this domain.
The paper tackles the problem of spam phone calls by proposing a collaborative phone blacklisting system that uses local differential privacy (LDP) to protect user data, showing it can learn an effective blacklist with a reasonable privacy budget while maintaining utility.
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.