{"id":"W7155386224","doi":"10.1109/isope67098.2025.00025","title":"Privacy Analysis of Consumer Hardware Devices – Strategies, Pitfalls and Opportunities to Build Privacy Respecting Hardware","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Glycemic Index Laboratories","funders":"","keywords":"Information privacy; Key (lock); Patient privacy; Confidentiality; Consumer privacy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009924688,0.0007555382,0.001505702,0.003403731,0.0005762293,0.001205461,0.002110828,0.0002978429,0.000257293],"category_scores_gemma":[0.0006813671,0.0007839858,0.0003841608,0.004407454,0.0004106465,0.002398225,0.003031956,0.0005110803,0.000006181523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002561563,"about_ca_system_score_gemma":0.001134938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007337827,"about_ca_topic_score_gemma":0.0006492226,"domain_scores_codex":[0.9946105,0.0003527965,0.001707519,0.001739527,0.0007459976,0.0008436691],"domain_scores_gemma":[0.9944519,0.0007465176,0.000780266,0.00223707,0.00137639,0.0004077988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003537673,0.0005887231,0.01994591,0.002015221,0.008821884,0.000189816,0.01633994,0.002574197,0.005991976,0.2952918,0.00553396,0.6423528],"study_design_scores_gemma":[0.002493888,0.002151187,0.05414499,0.004176338,0.007777377,0.00007052327,0.02825607,0.06985379,0.1712355,0.02466923,0.629429,0.005742074],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04067033,0.003422821,0.942525,0.002347596,0.0004079057,0.001079567,0.00008848347,0.0008600821,0.008598264],"genre_scores_gemma":[0.8712308,0.001560881,0.120575,0.001504071,0.0000382313,0.0000829245,0.00001063804,0.0000360884,0.00496133],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8305604,"threshold_uncertainty_score":0.9998314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04552139189851701,"score_gpt":0.3267406081304847,"score_spread":0.2812192162319677,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}