In this paper, we introduce BlockRR, a novel and unified randomized-response mechanism for label differential privacy. This framework generalizes existed RR-type mechanisms as special cases under specific parameter settings, which eliminates the need for separate, case-by-case analysis. Theoretically, we prove that BlockRR satisfies $ε$-label DP. We also design a partition method for BlockRR based on a weight matrix derived from label prior information; the parallel composition principle ensures that the composition of two such mechanisms remains $ε$-label DP. Empirically, we evaluate BlockRR on two variants of CIFAR-10 with varying degrees of class imbalance. Results show that in the high-privacy and moderate-privacy regimes ($ε\leq 3.0$), our propsed method gets a better balance between test accuaracy and the average of per-class accuracy. In the low-privacy regime ($ε\geq 4.0$), all methods reduce BlockRR to standard RR without additional performance loss.

翻译：本文提出BlockRR，一种新颖且统一的随机响应机制，用于实现标签差分隐私。该框架将现有RR型机制推广为特定参数设置下的特例，从而无需进行单独、逐个案例的分析。理论上，我们证明BlockRR满足$ε$-标签差分隐私。我们还基于从标签先验信息导出的权重矩阵，为BlockRR设计了一种分区方法；并行组合原理确保两个此类机制的组合仍保持$ε$-标签差分隐私。实证方面，我们在具有不同程度类别不平衡的两种CIFAR-10变体上评估BlockRR。结果表明，在高隐私和中度隐私区域（$ε≤3.0$），我们提出的方法在测试准确率和类平均准确率之间取得了更好的平衡。在低隐私区域（$ε≥4.0$），所有方法都将BlockRR退化为标准随机响应机制，且无额外的性能损失。