Inference under Information Constraints III: Local Privacy Constraints
This work addresses privacy-preserving statistical testing for distributed data, which is incremental as it builds on existing local privacy frameworks.
The paper tackles the problem of performing goodness-of-fit and independence testing on discrete distributions with data distributed across multiple users under local differential privacy constraints, showing that shared randomness reduces sample complexity and proposing sample-optimal, communication-efficient protocols.
We study goodness-of-fit and independence testing of discrete distributions in a setting where samples are distributed across multiple users. The users wish to preserve the privacy of their data while enabling a central server to perform the tests. Under the notion of local differential privacy, we propose simple, sample-optimal, and communication-efficient protocols for these two questions in the noninteractive setting, where in addition users may or may not share a common random seed. In particular, we show that the availability of shared (public) randomness greatly reduces the sample complexity. Underlying our public-coin protocols are privacy-preserving mappings which, when applied to the samples, minimally contract the distance between their respective probability distributions.