On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms
This work offers practitioners a post-processing tool to enhance the accuracy of privacy-preserving data analysis using existing LDP mechanisms, though it is incremental as it compares methods rather than introducing new ones.
This paper investigates whether Iterative Bayesian Update (IBU) improves utility over Matrix Inversion (MI) for estimating discrete distributions from data obfuscated with Locally Differentially Private (LDP) mechanisms, finding that IBU provides better utility, especially in high privacy regimes, without additional privacy cost.
This paper investigates the utility gain of using Iterative Bayesian Update (IBU) for private discrete distribution estimation using data obfuscated with Locally Differentially Private (LDP) mechanisms. We compare the performance of IBU to Matrix Inversion (MI), a standard estimation technique, for seven LDP mechanisms designed for one-time data collection and for other seven LDP mechanisms designed for multiple data collections (e.g., RAPPOR). To broaden the scope of our study, we also varied the utility metric, the number of users n, the domain size k, and the privacy parameter ε, using both synthetic and real-world data. Our results suggest that IBU can be a useful post-processing tool for improving the utility of LDP mechanisms in different scenarios without any additional privacy cost. For instance, our experiments show that IBU can provide better utility than MI, especially in high privacy regimes (i.e., when ε is small). Our paper provides insights for practitioners to use IBU in conjunction with existing LDP mechanisms for more accurate and privacy-preserving data analysis. Finally, we implemented IBU for all fourteen LDP mechanisms into the state-of-the-art multi-freq-ldpy Python package (https://pypi.org/project/multi-freq-ldpy/) and open-sourced all our code used for the experiments as tutorials.