Metric-based local differential privacy for statistical applications
This addresses utility limitations in LDP for domain-specific data like location or energy consumption, offering a more efficient solution for statistical applications.
The paper tackles the problem of high noise requirements in local differential privacy (LDP) for statistical applications by proposing a variant suitable for metric spaces, such as location or energy consumption data, and shows it provides much better utility for the same level of privacy.
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.