{"paper":{"title":"Privatization of Synthetic Gaze: Attenuating State Signatures in Diffusion-Generated Eye Movements","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Kamrul Hasan, Oleg V. Komogortsev","submitted_at":"2026-01-28T21:24:50Z","abstract_excerpt":"The recent success of deep learning (DL) has enabled the generation of high-quality synthetic data, advancing the development of data-driven biometric applications. Among various biometric modalities, eye movement sequences have emerged as a promising behavioral biometric. However, gaze data also raises privacy concerns because it may encode individuals' internal states, such as fatigue, emotional load, and stress. Ideally, synthetic gaze data should preserve the signal quality of real recordings, including identity features, while removing or attenuating privacy-sensitive, state-related attri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.21057","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.21057/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}