Personalized Local Differential Privacy Frequency Estimation Mechanisms Based on Partitioning the Domain of Real Attribute Values

Existing multi-domain personalized local differential privacy (MDPLDP) mechanisms, which extend attribute domains by introducing fake values, often fail to provide adequate personalized privacy protection and limit utility in frequency estimation. To address these limitations, the authors propose two novel MDPLDP mechanisms that construct multiple domains by partitioning real attribute values, support cross-domain aggregation, and flexibly accommodate diverse privacy requirements and budgets. The methods further extend to multi-dimensional frequency estimation, catering to complex user privacy preferences. Theoretical analysis and experimental results demonstrate that our mechanisms achieve substantially lower estimation error and communication overhead, while delivering over 20% average utility improvement compared to state-of-the-art methods in both single- and multi-dimensional settings.