Federated Data Analytics with Differentially Private Density Estimation Model

Federated data analytics, aimed at extracting in-sights from decentralized private data while preserving privacy, is crucial for organizations holding sensitive data. Existing approaches, such as output perturbation that adds noise to query results based on differential privacy, often suffer from degraded accuracy due to cumulative privacy budget consumption. In this paper, we introduce ADAPT, a novel framework that addresses this problem by training a privacy-preserving density model over decentralized data. Unlike traditional methods, ADAPT avoids accessing raw data when answering queries, thereby avoiding additional privacy leakage. We tackle the technical challenges raised by privacy-preserving federated data analytics, including parameter misalignment and distribution discrepancy, through innovative techniques of pre-alignment of network parameters and fine-tuning towards accurate data distributions. Directly using the density model, ADAPT accurately infers the results of a wide range of analytical queries. Extensive experiments demonstrate that ADAPT outperforms existing methods in terms of accuracy. Notably, for answering 8,000 analytical queries, ADAPT reduces the median relative error from over 103 to less than 6%. Moreover, it achieves high accuracy comparable to centralized differential privacy training, demonstrating its effectiveness in practical federated data analytics scenarios.