{"id":"W4401943215","doi":"10.1109/tsc.2024.3451165","title":"An Efficient and Multi-Private Key Secure Aggregation Scheme for Federated Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Scheme (mathematics); Key (lock); Public-key cryptography; Distributed computing; Computer network; Theoretical computer science; Computer security; Encryption","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":[0.002819427,0.0006649899,0.001563096,0.0009229915,0.001634778,0.001673919,0.002436495,0.001644421,0.001636046],"category_scores_gemma":[0.005643626,0.0003637189,0.001195272,0.001586133,0.001252875,0.005886558,0.005105372,0.002157251,0.0005801559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139594,"about_ca_system_score_gemma":0.001943409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008794885,"about_ca_topic_score_gemma":0.0006880685,"domain_scores_codex":[0.9962458,0.000870101,0.000427795,0.0007480024,0.001268753,0.000439557],"domain_scores_gemma":[0.9967492,0.0006160122,0.0003740264,0.001562804,0.0005208465,0.0001771665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001514384,0.0004636564,0.003048577,0.0002792138,0.0002694789,0.0007417034,0.0009697523,0.2702951,0.03141033,0.2643309,0.009283816,0.4173931],"study_design_scores_gemma":[0.00006442685,0.00009842921,0.0002859267,0.00001646119,0.00003354601,0.0003222105,0.00005981831,0.8984591,0.01291392,0.08512452,0.002565865,0.00005580062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02013132,0.000190406,0.9773569,0.0002665153,0.000044624,0.00009711149,0.00008280539,0.000865235,0.000964995],"genre_scores_gemma":[0.8329864,0.000172644,0.1635868,0.0002109029,0.0000484806,0.0001823133,0.000214126,0.00005510388,0.002543218],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002819427,"threshold_uncertainty_score":0.01491076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144168617333403,"score_gpt":0.2711689967980994,"score_spread":0.2567521350647591,"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."}}