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This paper introduces ReBound, a novel framework that enhances differentially private decision support by allowing the reuse of cached results from previous queries, thereby reducing the privacy cost associated with new queries. By integrating a cache graph structure and a negotiation mechanism, ReBound enables analysts to interactively refine queries while maintaining formal utility guarantees and addressing privacy constraints. The key finding demonstrates that ReBound can significantly lower the privacy overhead without sacrificing the accuracy of the decision support process, making it a valuable tool for analysts dealing with complex data queries.
ReBound slashes privacy costs for interactive decision support by intelligently reusing past query results, enabling analysts to refine their insights without compromising data integrity.
Differentially private decision support frameworks answer complex aggregate threshold queries with formal bounds on false negative and false positive rates, but treat each query independently with no memory of past results. In practice, analysts work interactively, issuing sequences of related queries that refine bounds, adjust thresholds, or derive new functions from previous ones. We propose ReBound, a framework that reuses cached results from previous queries to answer new queries at reduced or zero additional privacy cost while maintaining formal utility guarantees. ReBound introduces a reuse framework for multiple refinement types, a cache graph structure for efficient lookup of reusable results, and a negotiation mechanism for when requested bounds cannot be met within budget.