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Privacy-preserving data analysis has become prevailing in recent years. Inthis paper, we propose a distributed group differentially private majority votemechanism for the sign selection problem in a distributed setup. To achievethis, we apply the iterative peeling to the stability function and use theexponential mechanism to recover the signs. As applications, we study theprivate sign selection for mean estimation and linear regression problems indistributed systems. Our method recovers the support and signs with the optimalsignal-to-noise ratio as in the non-private scenario, which is better thancontemporary works of private variable selections. Moreover, the sign selectionconsistency is justified with theoretical guarantees. Simulation studies areconducted to demonstrate the effectiveness of our proposed method.

Majority Vote for Distributed Differentially Private Sign Selection