{"id":"W1529158540","doi":"10.1109/icdcs.2015.12","title":"Privacy-Preserving Compressive Sensing for Crowdsensing Based Trajectory Recovery","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Crowdsensing; Trajectory; Obfuscation; Encryption; Homomorphic encryption; Compressed sensing; Cloud computing; Metric (unit); Interpolation (computer graphics); Property (philosophy); Padding; Data mining; Computer vision; Computer security; Algorithm; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008912715,0.0006475451,0.0005866094,0.0003800278,0.0006016071,0.0006058854,0.0009345894,0.0009239611,0.001000696],"category_scores_gemma":[0.004214951,0.000247226,0.0004796565,0.0006249245,0.00117948,0.001511971,0.002024464,0.001210855,0.0003562973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006086653,"about_ca_system_score_gemma":0.001224815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001845792,"about_ca_topic_score_gemma":0.001564841,"domain_scores_codex":[0.9986686,0.0003406177,0.00006492357,0.000215671,0.0005688084,0.0001414769],"domain_scores_gemma":[0.9982916,0.0007979042,0.0002379474,0.0003900519,0.0002077259,0.00007477555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006830834,0.0001488596,0.00264356,0.0004188081,0.00008826563,0.0008136465,0.000520096,0.6638153,0.05862318,0.0700383,0.005215956,0.196991],"study_design_scores_gemma":[0.00002698834,0.0001228918,0.0003957944,0.00002647138,0.00001324637,0.00023881,0.0000950593,0.9601837,0.0149477,0.02069151,0.003228489,0.00002922937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0231931,0.0003260179,0.9727664,0.0005222351,0.00007299327,0.0000611387,0.000134731,0.0004327927,0.002490581],"genre_scores_gemma":[0.8772371,0.000546318,0.119347,0.0003048086,0.00008311344,0.0001302453,0.0002488864,0.00003656641,0.002065995],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001845792,"threshold_uncertainty_score":0.004713535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03509442753615481,"score_gpt":0.2394178686735196,"score_spread":0.2043234411373648,"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."}}