ReBound: Reuse-Aware Privacy For Interactive Decision Support

2026-07-15

AI Summary

This work addresses the inefficiency in existing differentially private decision support systems, which often disregard historical query results when processing interactive query sequences, leading to unnecessary privacy budget expenditure. To mitigate this issue, the paper proposes a framework that supports reuse across diverse query types by constructing a cache graph structure for efficient retrieval of reusable results and incorporating a negotiation mechanism to handle insufficient privacy budgets. Under formal utility guarantees that strictly bound false positive and false negative rates, the approach significantly reduces cumulative privacy costs, with certain queries incurring zero additional privacy expense. Key contributions include a scalable query reuse mechanism, an efficient graph-based indexing structure, and a budget negotiation strategy compatible with differential privacy constraints.

Abstract

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.

Problem

• differential privacy
• interactive querying
• query reuse
• decision support
• privacy budget

Innovation

• differential privacy
• query reuse
• interactive decision support
• cache graph
• privacy budget negotiation