Differential Privacy for Class-based Data: A Practical Gaussian Mechanism

TL;DR

This paper introduces a practical Gaussian mechanism for differential privacy that protects class membership information in data, ensuring privacy while maintaining accuracy and efficiency, demonstrated through real-world case studies.

Contribution

The paper proposes a novel output perturbation mechanism for class-based data that is both privacy-preserving and computationally efficient, outperforming existing Gaussian noise methods.

Findings

Outperforms baseline Gaussian noise mechanisms

Maintains high accuracy in power consumption forecasting

Effective in real-world AMI data applications

Abstract

In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be protected. The proposed method is an output perturbation mechanism that adds noise to the release of query response such that the analyst is unable to infer the underlying class-label. The proposed DP method is capable of not only protecting the privacy of class-based data but also meets quality metrics of accuracy and is computationally efficient and practical. We illustrate the efficacy of the proposed method empirically while outperforming the baseline additive Gaussian noise mechanism. We also examine a real-world application and apply the proposed DP method to the autoregression and moving average (ARMA) forecasting method, protecting the privacy of the underlying data source. Case studies on the real-world advanced metering infrastructure (AMI) measurements of household power consumption validate the excellent performance of the proposed DP method while also satisfying the accuracy of forecasted power consumption measurements.