BlockRR: A Unified Framework of RR-type Algorithms for Label Differential Privacy

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 ≤ 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 ≥ 4.0), all methods reduce BlockRR to standard RR without additional performance loss.