Conditional Analysis for Key-Value Data with Local Differential Privacy

Understand

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.