Full Program
Summary:
Local differential privacy (LDP) has become a prominent notion for privacy-preserving data collection. While numerous LDP protocols and post-processing (PP) methods have been developed, selecting an optimal combination under different privacy budgets and datasets remains a challenge. Moreover, the lack of a comprehensive and extensible LDP benchmarking toolkit raises difficulties. To address these concerns, this paper presents LDP3, an open-source, extensible, and multi-threaded toolkit for LDP researchers and practitioners. LDP3 contains implementations of several LDP protocols, PP methods, and utility metrics in a modular and extensible design. Its modular design enables developers to conveniently integrate new protocols and PP methods. Furthermore, its multi-threaded nature enables significant reductions in execution times via parallelization. Experimental evaluations demonstrate that: (i) using LDP3 to select a good protocol and post-processing method substantially improves utility compared to a bad or random choice, and (ii) the multi-threaded design brings substantial benefits in terms of efficiency.Author(s):
Berkay Kemal Balioglu
Koç University
Turkey
Alireza Khodaie
Koç University
Turkey
M. Emre Gursoy
Koç University
Turkey