Local Private Hypothesis Testing: Chi-Square Tests
This work addresses the need for practical privacy-preserving statistical testing in applications like healthcare or surveys, but it is incremental as it adapts existing chi-square tests from the curator model to the local model.
The paper tackles the problem of designing private hypothesis tests in the local differential privacy model, where individual data is perturbed before sharing, and it analyzes locally private chi-square tests for goodness of fit and independence testing, achieving results with concrete privacy and utility guarantees.
The local model for differential privacy is emerging as the reference model for practical applications collecting and sharing sensitive information while satisfying strong privacy guarantees. In the local model, there is no trusted entity which is allowed to have each individual's raw data as is assumed in the traditional curator model for differential privacy. So, individuals' data are usually perturbed before sharing them. We explore the design of private hypothesis tests in the local model, where each data entry is perturbed to ensure the privacy of each participant. Specifically, we analyze locally private chi-square tests for goodness of fit and independence testing, which have been studied in the traditional, curator model for differential privacy.