  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.
