Building a Collaborative Phone Blacklisting System with Local Differential Privacy

TL;DR

This paper proposes a privacy-preserving collaborative phone blacklisting system using local differential privacy, demonstrating its effectiveness on real-world data while maintaining user privacy.

Contribution

It introduces a practical LDP-based system for phone blacklisting, addressing privacy concerns in collaborative spam call detection.

Findings

The system can learn effective blacklists with reasonable privacy budgets.

It preserves user privacy while maintaining blacklist utility.

Evaluation on FTC data shows promising results.

Abstract

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.