{"id":"W4388925643","doi":"10.1145/3605758.3623496","title":"Privacy through Diffusion: A White-listing Approach to Sensor Data Anonymization","year":2023,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obfuscation; Computer science; Inference; Generative model; Information privacy; Data modeling; Differential privacy; Listing (finance); Data mining; Generative grammar; Machine learning; Artificial intelligence; Computer security; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01043352,0.0008297461,0.001314292,0.001477686,0.001358776,0.003112111,0.002688692,0.002522073,0.00167862],"category_scores_gemma":[0.02698749,0.0006862597,0.00183019,0.00204729,0.004045907,0.007997253,0.005716717,0.00382873,0.0004509546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001781682,"about_ca_system_score_gemma":0.001656707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001318114,"about_ca_topic_score_gemma":0.00126097,"domain_scores_codex":[0.991319,0.004167163,0.0004374943,0.001418293,0.002155374,0.0005026038],"domain_scores_gemma":[0.9732807,0.01251533,0.001843213,0.01072882,0.00125705,0.0003750261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004458546,0.0001397865,0.001983607,0.0001583438,0.0001201154,0.0005572592,0.00114495,0.3146847,0.009502556,0.5618306,0.002754978,0.1066773],"study_design_scores_gemma":[0.00002658783,0.00005889752,0.0002078151,0.00004480669,0.00002428508,0.0002381113,0.00008531759,0.6864038,0.007274888,0.3015093,0.004090375,0.00003590355],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007666243,0.0001461222,0.9903138,0.0005110419,0.00002368435,0.00004654032,0.00007267282,0.0002741012,0.0009456711],"genre_scores_gemma":[0.6558311,0.000637192,0.336198,0.0007229948,0.000158308,0.0002731668,0.0005249933,0.000262623,0.005391655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01043352,"threshold_uncertainty_score":0.05517834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1218550595434212,"score_gpt":0.3212802001654009,"score_spread":0.1994251406219797,"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."}}