{"id":"W2119997159","doi":"10.1109/rfid.2009.4911184","title":"Preserving RFID data privacy","year":2009,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Computer science; Data publishing; Computer security; Timestamp; Data anonymization; Radio-frequency identification; Information privacy; Identification (biology); Encryption; Anonymity; Publishing","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005101303,0.0001596403,0.0001547432,0.0001218565,0.0001176878,0.0003469888,0.1659425,0.00009731357,0.00006638114],"category_scores_gemma":[0.01982776,0.0001396108,0.00002441353,0.0006166659,0.00004142729,0.003177241,0.3287743,0.000239268,0.0001621086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003327106,"about_ca_system_score_gemma":0.00005643442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003008041,"about_ca_topic_score_gemma":0.000004624917,"domain_scores_codex":[0.9980209,0.00003948248,0.0002439971,0.0008766162,0.0003684801,0.0004505661],"domain_scores_gemma":[0.9558979,0.0001085414,0.00007171168,0.04379975,0.00004552176,0.00007654692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00000120485,0.0000456899,0.0002212695,0.000004033618,0.000006761236,0.00001722531,0.00001673341,0.000001330725,0.0006382744,0.01728297,0.8854969,0.0962676],"study_design_scores_gemma":[0.0001988539,0.00006230915,0.003755987,0.00002154125,0.000003383203,0.00002435386,0.000007564038,0.3476438,0.003886821,0.578201,0.06590161,0.0002927923],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002412654,0.0002306137,0.8186427,0.1506352,0.0002601558,0.0001877558,0.00001457635,0.003579749,0.02403652],"genre_scores_gemma":[0.1478532,0.00005002453,0.8504646,0.001244809,0.00005237577,0.000002734837,0.00003041339,0.000006915596,0.0002948974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8195953,"threshold_uncertainty_score":0.9884287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07597651806588417,"score_gpt":0.3169329534026148,"score_spread":0.2409564353367306,"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."}}