{"id":"W2003383194","doi":"10.1145/1804669.1804684","title":"k-jump strategy for preserving privacy in micro-data disclosure","year":2010,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jump; Adversarial system; Computer science; Data mining; Data modeling; Information privacy; Algorithm; 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":[0.007330936,0.0007719425,0.001593329,0.00118164,0.001629201,0.003001141,0.003055333,0.002692695,0.001787135],"category_scores_gemma":[0.02530555,0.000484498,0.001329536,0.001134435,0.004064769,0.007316528,0.004044209,0.003858161,0.0005172865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293868,"about_ca_system_score_gemma":0.002532782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004755457,"about_ca_topic_score_gemma":0.0003381607,"domain_scores_codex":[0.9930785,0.002754646,0.0004862021,0.001265863,0.001746163,0.0006685253],"domain_scores_gemma":[0.9739833,0.01590662,0.001816673,0.006373309,0.001191707,0.000728366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001420882,0.0003456292,0.003664743,0.0003925515,0.0002411965,0.0004706823,0.001180126,0.1471109,0.01769454,0.6828801,0.003131204,0.1414673],"study_design_scores_gemma":[0.0001065718,0.0002586772,0.0003887065,0.00004160811,0.00005615817,0.0004426367,0.0001427216,0.5848277,0.01444029,0.3971958,0.002031204,0.00006795373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06901565,0.0002880672,0.9265195,0.000737131,0.00004886587,0.0001852854,0.00009400254,0.0005115508,0.00259998],"genre_scores_gemma":[0.8309309,0.0002612565,0.165555,0.0003093976,0.00006387247,0.0002368922,0.0001198331,0.00009093983,0.00243182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007330936,"threshold_uncertainty_score":0.0387702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07385878438443395,"score_gpt":0.3274053301727948,"score_spread":0.2535465457883608,"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."}}