Conditional Analysis for Key-Value Data with Local Differential Privacy
This work addresses privacy-preserving data analysis for NoSQL systems, offering incremental improvements by extending existing LDP methods to handle conditional statistics for machine learning tasks.
The paper tackled the problem of estimating conditional frequencies and means for key-value data under local differential privacy, proposing new perturbation mechanisms that demonstrated effectiveness and accuracy in experiments against state-of-the-art competitors.
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.