A Model-Based Filter to Improve Local Differential Privacy

Local differential privacy has been gaining popularity in both academic and industrial settings as an effective mechanism to enable the computation of summary statistics by service providers while providing privacy guarantees to end users. In the proposed mechanisms of local differential privacy, the introduction of noise in the user data is key to preserve privacy, but can greatly limit the estimation power on the provider side. In this paper we propose to include pre-filters based on machine learning models to help discard observations that may be too noisy to add value to the estimation process. We test our approach on both synthetic and real datasets, and identify average reductions of up to 31% in the mean squared error.