Differential privacy (DP) is a leading standard for protecting individual privacy in data collection and analysis. This paper explores the shuffle model of DP, which balances privacy and utility by allowing users to send messages to a trusted shuffler before reaching an untrusted analyzer anonymously. We focus on efficiently implementing the matrix mechanism in shuffle-DP, where efficiency is defined by the number of messages each user sends. Our contributions include a baseline shuffle-DP mechanism that naively adapts the matrix mechanism, followed by an improved mechanism that reduces message complexity while maintaining error levels comparable to central-DP. We demonstrate the versatility of our approach across common query workloads, such as range queries and data cubes, achieving significant improvements in message efficiency. Experimental results confirm that our method outperforms the baseline solution while closely matching the accuracy of central-DP mechanisms.

Differential privacy (DP) has garnered significant attention from both academia and industry due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of SQLs while maintaining DP has become the holy grail in privacy-preserving query release. In this demonstration, we present DOP-SQL, a DP SQL system that can answer a broad class of queries consisting of the selection, projection, aggregation, join, and group by operators. DOP-SQL has integrated a suite of down-neighborhood optimal DP mechanisms, thus achieving state-of-the-art utility. The current implementation of DOP-SQL is based on PostgreSQL, but its extensible feature allows it to be used in conjunction with any standard SQL engine.