Majority Vote for Distributed Differentially Private Sign Selection
This work addresses privacy-preserving data analysis for distributed setups, offering improved performance in applications like mean estimation and linear regression, though it is incremental as it builds on existing differential privacy techniques.
The paper tackles the sign selection problem in distributed systems by proposing a distributed group differentially private Majority Vote mechanism, achieving optimal signal-to-noise ratio comparable to non-private scenarios and outperforming existing private variable selection methods.
Privacy-preserving data analysis has become more prevalent in recent years. In this study, we propose a distributed group differentially private Majority Vote mechanism, for the sign selection problem in a distributed setup. To achieve this, we apply the iterative peeling to the stability function and use the exponential mechanism to recover the signs. For enhanced applicability, we study the private sign selection for mean estimation and linear regression problems, in distributed systems. Our method recovers the support and signs with the optimal signal-to-noise ratio as in the non-private scenario, which is better than contemporary works of private variable selections. Moreover, the sign selection consistency is justified by theoretical guarantees. Simulation studies are conducted to demonstrate the effectiveness of the proposed method.