Utility-Improved Key-Value Data Collection with Local Differential Privacy for Mobile Devices

Release Date:2023-01-28 Author:TONG Ze, DENG Bowen, ZHENG Lele, ZHANG Tao Click:

Abstract: The structure of key-value data is a typical data structure generated by mobile devices. The collection and analysis of the data from mobile devices are critical for service providers to improve service quality. Nevertheless, collecting raw data, which may contain various personal information, would lead to serious personal privacy leaks. Local differential privacy (LDP) has been proposed to protect privacy on the device side so that the server cannot obtain the raw data. However, existing mechanisms assume that all keys are equally sensitive, which cannot produce high-precision statistical results. A utility-improved data collection framework with LDP for key-value formed mobile data is proposed to solve this issue. More specifically, we divide the key-value data into sensitive and non-sensitive parts and only provide an LDP-equivalent privacy guarantee for sensitive keys and all values. We instantiate our framework by using a utility-improved key value-unary encoding (UKV-UE) mechanism based on unary encoding, with which our framework can work effectively for a large key domain. We then validate our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets. Finally, some possible future research directions are envisioned.

Keywords: key-value data; local differential privacy; mobile devices; privacy-preserving data collectio

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