A Differentially Private Quadrature Amplitude Modulation Mechanism for Federated Analytics

This is the authors' abstract. We don't add key points for this paper.

Wireless federated analytics face two critical challenges: data privacy and communication efficiency, since the local data may contain sensitive information and the users may be equipped with limited communication capability. Existing methods often adopt a direct combination of privacy-preservation schemes and compression mechanisms but overlook the privacy amplification effect from errors introduced in compression and wireless communication. With such consideration, a Differentially Private Quadrature Amplitude Modulation (DP-QAM) scheme, which leverages privacy amplification from both compression and noisy wireless channels, is proposed. The privacy guarantee is established in terms of the emergingf-DP, and the trade-off between privacy, communication cost, and accuracy in terms of mean square error (MSE) is characterized in the fundamental use cases of distributed mean estimation and frequency estimation, which outperforms the state-of-the-art methods. Moreover, the advantage of the proposed method over the classic Gaussian mechanism is further demonstrated from a rate-distortion perspective. Finally, extensive simulation results validate the effectiveness of the proposed mechanism.