TUPLE-Bench: Evaluating and Mitigating Privacy-Utility Trade-offs in Typed Decision Models
TUPLE-Bench quantifies how prediction probabilities in typed decision models can leak information beyond intended outputs — a privacy-by-design concern for GDPR-regulated AI deployments.
Summary written by editorial AI · Source link below
arXiv:2610.23668v1 Announce Type: new Abstract: Typed decision models often return more than a selected category. They also expose probabilities for each candidate and, sometimes, a separate confidence score. These fields support downstream decisions but may reveal information beyond a recipient's authorization. We introduce TUPLE-Bench (Typed decision models' Utility and Privacy Leakage Evaluation Benchmark), a benchmark of paired synthetic cases in which recipients may learn a coarse operatio
Editorial Analysis
Enterprises deploying ML models that expose confidence scores should assess whether these outputs violate data-minimisation principles under GDPR.
Audit ML model outputs for unnecessary information leakage, particularly probability distributions shared with downstream consumers.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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