{"id":"W3112250879","doi":"10.1109/tpds.2020.3042695","title":"Privacy-Preserving Similarity Search With Efficient Updates in Distributed Key-Value Stores","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Homomorphic encryption; Speedup; Encryption; Cloud computing; Hash function; Nearest neighbor search; Locality-sensitive hashing; Similarity (geometry); Key (lock); Data mining; ElGamal encryption; Leverage (statistics); Theoretical computer science; Distributed computing; Public-key cryptography; Hash table; Computer network; Computer security; Parallel computing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003174962,0.0003037131,0.0003987764,0.0001392412,0.0003437037,0.0003735679,0.0008428679,0.0001359234,0.000009839694],"category_scores_gemma":[0.00001669719,0.000254489,0.00008898923,0.001260985,0.0001132843,0.0003937203,0.00003087045,0.0004897091,0.00001120217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006207119,"about_ca_system_score_gemma":0.0000821236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005620381,"about_ca_topic_score_gemma":0.00009440525,"domain_scores_codex":[0.9975544,0.0002613152,0.0004209322,0.0007521297,0.0005074225,0.0005037896],"domain_scores_gemma":[0.9985952,0.0001789213,0.0000849424,0.0006608046,0.00009843089,0.0003816855],"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":[0.000285851,0.0004508182,0.00156187,0.0001902654,0.00007492805,0.00007538408,0.001594886,0.98891,0.0001669399,0.005870526,0.00056539,0.0002531614],"study_design_scores_gemma":[0.001837495,0.0003398744,0.004737763,0.0001347543,0.00002677293,0.00003042006,0.0007437681,0.9897218,0.0003232181,0.0001349111,0.001492883,0.0004763257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1158094,0.0002595685,0.879872,0.001321162,0.0001483063,0.0004804489,0.001863661,0.0002169803,0.00002851175],"genre_scores_gemma":[0.997615,0.00004946238,0.001955781,0.00009050625,0.0000283779,0.00006231983,0.00018525,0.00001182359,0.000001488819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8818056,"threshold_uncertainty_score":0.9999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0233563898907388,"score_gpt":0.239407286401864,"score_spread":0.2160508965111252,"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."}}