Optimal error of query sets under the differentially-private matrix mechanism

Understand

A common goal of privacy research is to release synthetic data that satisfies a formal privacy guarantee and can be used by an analyst in place of the original data.

• To achieve reasonable accuracy, a synthetic data set must be tuned to support a specified set of queries accurately, sacrificing fidelity for other queries.
• This work considers methods for producing synthetic data under differential privacy and investigates what makes a set of queries "easy" or "hard" to answer.
• We consider answering sets of linear counting queries using the matrix mechanism, a recent differentially-private mechanism that can reduce error by adding complex correlated noise adapted to a specified workload.