ReBound: Reuse-Aware Privacy For Interactive Decision Support

arXiv:2607.13441 · cs.CR, cs.DB · Submitted 2026-07-15 · Read on arXiv

Nada Lahjouji, Shufan Zhang, Xi He, Sharad Mehrotra

cs.CR, cs.DB

Submitted: 2026-07-15

Comments: 5 pages, 3 figures, 3 tables. Accepted at the Theory and Practice of Differential Privacy workshop (TPDP 2026)

License: http://creativecommons.org/licenses/by/4.0/

The gist: 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

Terminology

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.

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