TY - GEN
T1 - Two-phase locality-sensitive hashing for privacy-preserving distributed service recommendation
AU - Qi, Lianyong
AU - Dou, Wanchun
AU - Zhang, Xuyun
PY - 2017/1/1
Y1 - 2017/1/1
N2 - With the ever-increasing volume of services registered in various web communities, it becomes a challenging task to find the web services that a target user is really interested in from the massive candidates. In this situation, Collaborative Filtering (i.e., CF) technique is introduced to alleviate the heavy burden on the service selection decisions of target users. However, present CF-based recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios where data are multi-sourced. Furthermore, distributed service recommendation calls for the collaborations among multiple involved parties, during which the private information of users may be exposed. In view of these challenges, we propose a novel privacy-preserving distributed service recommendation approach based on two-phase Locality-Sensitive Hashing (LSH), named SerRectwo-LSH, in this paper. Concretely, in SerRectwo-LSH, we first look for the “similar friends” of a target user through a privacy-preserving two-phase LSH process; afterwards, we determine the services preferred by the “similar friends” of the target user, and then recommend them to the target user. Finally, through a set of experiments conducted on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.
AB - With the ever-increasing volume of services registered in various web communities, it becomes a challenging task to find the web services that a target user is really interested in from the massive candidates. In this situation, Collaborative Filtering (i.e., CF) technique is introduced to alleviate the heavy burden on the service selection decisions of target users. However, present CF-based recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios where data are multi-sourced. Furthermore, distributed service recommendation calls for the collaborations among multiple involved parties, during which the private information of users may be exposed. In view of these challenges, we propose a novel privacy-preserving distributed service recommendation approach based on two-phase Locality-Sensitive Hashing (LSH), named SerRectwo-LSH, in this paper. Concretely, in SerRectwo-LSH, we first look for the “similar friends” of a target user through a privacy-preserving two-phase LSH process; afterwards, we determine the services preferred by the “similar friends” of the target user, and then recommend them to the target user. Finally, through a set of experiments conducted on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.
KW - Collaborative Filtering
KW - Distributed service recommendation
KW - Efficiency
KW - Privacy-preservation
KW - Two-Phase Locality-Sensitive hashing
UR - http://www.scopus.com/inward/record.url?scp=85034271133&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-69471-9_13
DO - 10.1007/978-3-319-69471-9_13
M3 - Conference proceeding contribution
AN - SCOPUS:85034271133
SN - 9783319694702
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 176
EP - 188
BT - Cyberspace Safety and Security
A2 - Wen, Sheng
A2 - Wu, Wei
A2 - Castiglione, Aniello
PB - Springer, Springer Nature
T2 - 9th International Symposium on Cyberspace Safety and Security, CSS 2017
Y2 - 23 October 2017 through 25 October 2017
ER -