Duke University

The work introduces Half-Moon Cookie, a three-party framework in which a sending client performs a privacy-preserving approximate (metric-space) check of an item against a server's proprietary blocklist; on success the server stores a hiding and binding token in an allowlist, and a receiving client can later confirm via a much faster implicit check that the item still passes, mitigating TOCTOU attacks without revealing client inputs or the blocklist; the authors instantiate it for Hamming-distance blocklists and apply it to similarity-based malware detection, showing reusable garbled circuits cut embedding communication by over two orders of magnitude and that the implicit check needs only 7.2e-3 MB and 0.19 s on a 100 kB input.

The work introduces Half-Moon Cookie, a three-party framework in which a sending client performs a privacy-preserving approximate (metric-space) check of an item against a server's proprietary blocklist; on success the server stores a hiding and binding token in an allowlist, and a receiving client can later confirm via a much faster implicit check that the item still passes, mitigating TOCTOU attacks without revealing client inputs or the blocklist; the authors instantiate it for Hamming-distance blocklists and apply it to similarity-based malware detection, showing reusable garbled circuits cut embedding communication by over two orders of magnitude and that the implicit check needs only 7.2e-3 MB and 0.19 s on a 100 kB input.

EviGen proposes a three-layer framework that first retrieves and ranks evidence from a patient's longitudinal EHR by predictive contribution rather than textual relevance using learnable queries trained on outcome labels, then has an LLM generate a citation-grounded clinical rationale within that evidence scaffold, and finally applies a process-supervised verifier to check each reasoning step and flag unreliable claims; across MIMIC-IV one-year mortality, autism, and ADHD tasks it improves prediction performance and rationale faithfulness over full-context and RAG baselines and is preferred by most reviewers in a clinical pilot.

EviGen proposes a three-layer framework that first retrieves and ranks evidence from a patient's longitudinal EHR by predictive contribution rather than textual relevance using learnable queries trained on outcome labels, then has an LLM generate a citation-grounded clinical rationale within that evidence scaffold, and finally applies a process-supervised verifier to check each reasoning step and flag unreliable claims; across MIMIC-IV one-year mortality, autism, and ADHD tasks it improves prediction performance and rationale faithfulness over full-context and RAG baselines and is preferred by most reviewers in a clinical pilot.