cs.CR · 2026-09-11 · No. 112
Cryptography and Security, 2026-09-11.
4 new papers in cs.CR. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
4 entries-
01
Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology
Noman Sadiq, Mohsen Toorani
cs.CR · cs.LG · eess.SP
Clinical electroencephalography (EEG) data are valuable for healthcare research and for developing artificial intelligence (AI)-based clinical decision-support systems, but EEG recordings and derived features may contain sensitive patient-specific information. This creates privacy risks when data are reused, analyzed, or shared across clinical and research environments. Conventional anonymization methods are often insufficient for...
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02
Deep-Fake CAPTCHA: Mitigating Next-Generation Social Engineering Attacks
Guy Frankovits, Lior Yasur, Fred M. Grabovski, Yisroel Mirsky
cs.CR · cs.AI
This paper presents DF-CAPTCHA, an active defense against real-time deepfake impersonation in voice and video calls. Instead of passively searching for artifacts, DF-CAPTCHA prompts the caller to perform simple challenge-response tasks that are easy for humans but difficult for current real-time deepfake systems to generate convincingly. The framework verifies the response using four criteria: realism, identity consistency, task completion,...
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03
You've Got a BUD in Me: Authenticated Reads from Per-Block Write Logs
Alejandro Ranchal-Pedrosa, Cody Littley, Ben Marsh
cs.CR · cs.DB · cs.DC
Blockchains usually pay for authenticated reads by maintaining a structure that spans the entire state. We show how validators can support historical membership and exclusion proofs by authenticating each block's writes instead. A Block Update Digest (BUD) commits a write log whose predecessor pointers link successive modifications of each key. A SuperBUD summarizes last writes over a window; an exponential hierarchy turns long unchanged...
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04
BenchShield: Formal Model-Backed Instrumentation for Reward Integrity in LLM-Agent Evaluation Infrastructure
Shenghan Zheng, Zonglin Di, Yimin Liu, Kyoung Whan Choe, Jiankai Sun, Heguang Lin, Penghao Jiang, Yifeng He, Xiao...
cs.CR · cs.AI · cs.SE · eess.SY
LM-agent benchmarks increasingly function as interactive evaluation infrastructure. Agents observe state, call tools, modify workspaces, submit artifacts, and receive rewards from outcome procedures. This interactivity makes evaluations vulnerable to reward hacking: an agent improves its measured score by exploiting the reward-relevant trajectory instead of solving the intended task. Existing defenses rely largely on task-specific patches,...
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