Differential privacy for counting queries: can Bayes estimation help uncover the true value?
This work addresses privacy-preserving data analysis for database users, but it is incremental as it builds on existing differential privacy methods.
The paper tackles the problem of improving accuracy in differentially private counting queries with a single query, showing that a Bayesian approach can enhance accuracy for the same noise level when database size and positive response probability are known.
Differential privacy is achieved by the introduction of Laplacian noise in the response to a query, establishing a precise trade-off between the level of differential privacy and the accuracy of the database response (via the amount of noise introduced). Multiple queries may improve the accuracy but erode the privacy budget. We examine the case where we submit just a single counting query. We show that even in that case a Bayesian approach may be used to improve the accuracy for the same amount of noise injected, if we know the size of the database and the probability of a positive response to the query.