This paper formalizes two specification-induced risks in the learnware paradigm and shows that the resulting DP guarantees control both disclosure risk and amplification risk, and analyzes the induced privacy–utility trade-off to guide effective learn-ware identification while preserving privacy.
This work introduces minimal information disclosure (MID), which designs and quantifies the information content of verifier-facing evidence itself and measures collateral leakage with conditional mutual information.
This SoK model federated learning as an append-only transcript of submissions, admissions, aggregation, and finalization events, and formalize verifiability as a collection of integrity claims issued by clients and the aggregator, and checked by different verifier classes.
Andrea Rizzini, Marco Esposito, Tommaso Gagliardoni et al.· Proceedings on Privacy Enhan...· 0 citations
A layered system model and an analysis pipeline that uses prior history as the temporal baseline, applies cumulative pruning and cross-proof propagation to each proof's Commitment Set, and recursively traces the survivors through historical hidden-state transitions to derive the final transaction-level Anonymity Set Si...
Hanze Guo, Stefanos Chaliasos, Yebo Feng et al.· 0 citations
This work formalizes rigorous cryptographic security notions tailored to CMC frameworks, introduces a generic protocol template, and proves that it satisfies these requirements, which offer both cautionary evidence about existing approaches and constructive guidance for designing secure, privacy-preserving ML auditing...
Carter Luck, Olive Franzese-McLaughlin, Elisaweta Masserova et al.· 0 citations
It is proved that the three most prominent variants of Privacy Pass are anamorphic, which makes it insecure in a model where user's device or client application is working against them and can also be used to achieve a private metadata-like functionality.
Mirosław Kutyłowski, Oliwer Sobolewski· Proceedings on Privacy Enhan...· 0 citations
This work expands the understanding of attacks in this setting by investigating a broader class of functionalities, namely: Private Set Union, PSU-Cardinality, and Meta’s multi-key private matching (MKPM) functionality, and investigates possible mitigations for deploying such systems.
Andrea Raguso, Francesca Falzon, Tianxin Tang et al.· Proceedings on Privacy Enhan...· 0 citations
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