{"id":"W4365998865","doi":"10.1002/spe.3209","title":"A blockchain‐based privacy‐preserving advertising attribution architecture: Requirements, design, and a prototype implementation","year":2023,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; Peng Cheng Laboratory; Tencent","keywords":"Attribution; Computer science; Architecture; Blockchain; Computer security; Intersection (aeronautics); Set (abstract data type); Internet privacy; Advertising; Business; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0006418632,0.0001386983,0.0001149818,0.0001441008,0.0005285289,0.0001500153,0.0003704169,0.00008389699,0.000006486444],"category_scores_gemma":[0.0004358034,0.0001372018,0.00001806688,0.0007946494,0.0001054043,0.0003939252,0.0004259787,0.000173189,0.000006494795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003166344,"about_ca_system_score_gemma":0.00006640366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000758815,"about_ca_topic_score_gemma":0.000005560388,"domain_scores_codex":[0.9985983,0.0001321768,0.0002149003,0.0005090077,0.0002288734,0.000316705],"domain_scores_gemma":[0.9988574,0.0003611558,0.000141523,0.0004512497,0.000108276,0.00008037058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002070317,0.000231558,0.009020752,0.0001913375,0.00006609577,0.00008086501,0.05075294,0.0006623815,0.01515741,0.02430994,0.0006175573,0.8987021],"study_design_scores_gemma":[0.006068219,0.002692406,0.0288094,0.0004984902,0.0001674359,0.0006760008,0.01536398,0.5409457,0.1347481,0.09415986,0.1727631,0.003107234],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.182238,0.0002365079,0.8116337,0.00457951,0.00003594758,0.0007439257,0.000001597696,0.0005250586,0.000005750147],"genre_scores_gemma":[0.7589242,0.00006424381,0.2398177,0.0004984661,0.00001498648,0.0006600437,0.000005335719,0.000008273305,0.00000674078],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8955949,"threshold_uncertainty_score":0.5594929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03236558568730539,"score_gpt":0.3337952147060385,"score_spread":0.3014296290187331,"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."}}