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.

翻译：本文提出了一种针对来自不同类别的数据的差分隐私（DP）概念。其中，类成员关系是需要保护的隐私信息。所提出的方法是一种输出扰动机制，它在查询响应的发布中添加噪声，使得分析者无法推断出底层的类标签。所提出的DP方法不仅能够保护基于类数据的隐私，还能满足准确性的质量指标，并且计算效率高且实用。我们通过实证方法展示了所提出方法的有效性，同时其性能优于基线加性高斯噪声机制。我们还考察了一个实际应用，将所提出的DP方法应用于自回归移动平均（ARMA）预测方法，以保护底层数据源的隐私。基于真实世界高级计量基础设施（AMI）家庭用电测量数据的案例研究验证了所提出DP方法的优异性能，同时满足了预测用电量测量的准确性。