Local Differential Privacy on Metric Spaces: Optimizing the Trade-Off with Utility

Overview & Synopsis

Local differential privacy (LPD) is a distributed variant of differential privacy (DP) in which the obfuscation of the sensitive information is done at the level of the individual records, and in general it is used to sanitize data that are collected for statistical purposes. LPD has the advantage it does not need to assume a trusted third party. On the other hand LDP in general requires more noise than DP to achieve the same level of protection, with negative consequences on the utility. In practice, utility becomes acceptable only on very large collections of data, and this is the reason why LDP is especially successful among big companies such as Apple and Google, which can count on a huge number of users. In this paper, we propose a variant of LDP suitable for metric spaces, such as location data or energy consumption data, and we show that it provides a much better utility for the same level of privacy.