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Data collection under local differential privacy (LDP) has been mostlystudied for homogeneous data. Real-world applications often involve a mixtureof different data types such as key-value pairs, where the frequency of keysand mean of values under each key must be estimated simultaneously. Forkey-value data collection with LDP, it is challenging to achieve a goodutility-privacy tradeoff since the data contains two dimensions and a user maypossess multiple key-value pairs. There is also an inherent correlation betweenkey and values which if not harnessed, will lead to poor utility. In thispaper, we propose a locally differentially private key-value data collectionframework that utilizes correlated perturbations to enhance utility. Weinstantiate our framework by two protocols PCKV-UE (based on Unary Encoding)and PCKV-GRR (based on Generalized Randomized Response), where we design anadvanced Padding-and-Sampling mechanism and an improved mean estimator which isnon-interactive. Due to our correlated key and value perturbation mechanisms,the composed privacy budget is shown to be less than that of independentperturbation of key and value, which enables us to further optimize theperturbation parameters via budget allocation. Experimental results on bothsynthetic and real-world datasets show that our proposed protocols achievebetter utility for both frequency and mean estimations under the same LDPguarantees than state-of-the-art mechanisms.

PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility