Local Private Hypothesis Testing: Chi-Square Tests

Abstract

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.

References in corpus (1)

• Revisiting Differentially Private Hypothesis Tests for Categorical Data

Cited by in corpus (7)

• A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
• Local Differential Privacy and Its Applications: A Comprehensive Survey
• Collecting and Analyzing Multidimensional Data with Local Differential Privacy
• Locally Private Gaussian Estimation
• Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
• Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge
• INSPECTRE: Privately Estimating the Unseen