CIPHER: Benchmarking Cross-record Inference over Privacy-Hardened Evidence Records

Abstract:Reasoning over privacy-constrained records requires combining structured attributes with evidence from free-text narratives. We introduce CIPHER (Cross-record Inference over Privacy-Hardened Evidence Records), a benchmark of expert-validated questions from consumer-finance, clinical, and law-enforcement records. The questions cover common tabular operations and include executable SQL supervision. We evaluate retrieval, prompting, table-specialist, and hybrid symbolic-neural systems under native redaction and surrogate-based evidence restoration. All system families exhibit substantial failures even when supporting records are provided. Most errors arise from incorrect record selection and predicate interpretation rather than arithmetic execution. Privacy transformations have non-uniform effects, sometimes obscuring necessary evidence and sometimes reducing distraction. CIPHER provides a reproducible testbed for diagnosing these failures and assessing how transformations of sensitive text affect reasoning over hybrid records.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2609.07022 [cs.CR]

(or arXiv:2609.07022v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.07022 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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