When differential privacy meets randomized perturbation: A hybrid approach for privacy-preserving recommender system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10177 LNCS, Issue , 2017, Pages 576-591

  Liu, Xiao ^a   Liu, An ^a,b   Zhang, Xiangliang ^b   Li, Zhixu ^a   Liu, Guanfeng ^a   Zhao, Lei ^a   Zhou, Xiaofang ^c  

^a SOOCHOW UNIVERSITY   (China)

^b KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^c UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Differential privacy; Privacy preserving; Randomized perturbation; Recommender systems

Indexed keywords

DATA PRIVACY; DATABASE SYSTEMS; SENSITIVITY ANALYSIS;

DIFFERENTIAL PRIVACIES; HYBRID APPROACH; PRIVACY PRESERVING; PRIVACY PROTECTION; PRIVACY RISKS; RANDOMIZED PERTURBATION; REAL-WORLD DATASETS; RECOMMENDATION ACCURACY;

RECOMMENDER SYSTEMS;

EID: 85029107074     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-55753-3_36     Document Type: Conference Paper

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