Conditional Analysis for Key-Value Data with Local Differential Privacy

Abstract

Local differential privacy (LDP) has been deemed as the de facto measure for privacy-preserving distributed data collection and analysis. Recently, researchers have extended LDP to the basic data type in NoSQL systems: the key-value data, and show its feasibilities in mean estimation and frequency estimation. In this paper, we develop a set of new perturbation mechanisms for key-value data collection and analysis under the strong model of local differential privacy. Since many modern machine learning tasks rely on the availability of conditional probability or the marginal statistics, we then propose the conditional frequency estimation method for key analysis and the conditional mean estimation for value analysis in key-value data. The released statistics with conditions can further be used in learning tasks. Extensive experiments of frequency and mean estimation on both synthetic and real-world datasets validate the effectiveness and accuracy of the proposed key-value perturbation mechanisms against the state-of-art competitors.

References in corpus (5)

• Extremal Mechanisms for Local Differential Privacy
• Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy
• Practical Locally Private Heavy Hitters
• Comparing Population Means under Local Differential Privacy: with Significance and Power
• Locally Differentially Private Data Collection and Analysis

Cited by in corpus (4)

• A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
• Local Differential Privacy and Its Applications: A Comprehensive Survey
• PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility
• Selective MPC: Distributed Computation of Differentially Private Key-Value Statistics