CoP: Coordinated Perturbation for Controlled Disclosure Under Local Differential Privacy

Abstract

Collecting multidimensional user data is essential for personalized services, yet it poses significant privacy risks. While privacy regulations like the GDPR and CPRA advocate for data minimization, attribute correlations can inadvertently amplify unintentional information disclosure, leading to correlation-induced information leakage (CIL). Although data collectors often possess rich prior knowledge of these correlations, existing Local Differential Privacy (LDP) mechanisms are inadequate for effectively leveraging this information to reduce CIL. In this paper, we propose CoP, a coordinated perturbation mechanism designed to mitigate CIL in multidimensional data collection while preserving utility. Unlike traditional LDP approaches, CoP explicitly incorporates prior distribution knowledge to coordinate the perturbation process across attributes. By optimizing the perturbation strategy based on known correlations, CoP achieves a better privacy-utility trade-off. Extensive evaluations across both synthetic and real-world datasets demonstrate that CoP significantly outperforms state-of-the-art LDP mechanisms in reducing disclosure while preserving analytical accuracy.