TY - GEN
T1 - Amplified locality-sensitive hashing for privacy-preserving distributed service recommendation
AU - Qi, Lianyong
AU - Dou, Wanchun
AU - Zhang, Xuyun
AU - Yu, Shui
PY - 2017
Y1 - 2017
N2 - With the ever-increasing volume of services registered in various web communities, service recommendation techniques, e.g., Collaborative Filtering (i.e., CF) have provided a promising way to alleviate the heavy burden on the service selection decisions of target users. However, traditional CF-based service recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios as well as the resulted privacy leakage risks. In view of this shortcoming, Locality-Sensitive Hashing (LSH) technique is recruited in this paper to protect the private information of users when distributed service recommendations are made. Furthermore, LSH is essentially a probability-based search technique and hence may generate “False-positive” or “False-negative” recommended results; therefore, we amplify LSH by AND/OR operations to improve the recommendation accuracy. Finally, through a set of experiments deployed on a real distributed service quality dataset, i.e., WS-DREAM, we validate the feasibility of our proposed recommendation approach named DistSRAmplify-LSH in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation in the distributed environment.
AB - With the ever-increasing volume of services registered in various web communities, service recommendation techniques, e.g., Collaborative Filtering (i.e., CF) have provided a promising way to alleviate the heavy burden on the service selection decisions of target users. However, traditional CF-based service recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios as well as the resulted privacy leakage risks. In view of this shortcoming, Locality-Sensitive Hashing (LSH) technique is recruited in this paper to protect the private information of users when distributed service recommendations are made. Furthermore, LSH is essentially a probability-based search technique and hence may generate “False-positive” or “False-negative” recommended results; therefore, we amplify LSH by AND/OR operations to improve the recommendation accuracy. Finally, through a set of experiments deployed on a real distributed service quality dataset, i.e., WS-DREAM, we validate the feasibility of our proposed recommendation approach named DistSRAmplify-LSH in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation in the distributed environment.
KW - Amplified Locality-Sensitive Hashing
KW - Collaborative Filtering
KW - Distributed service recommendation
KW - Privacy-preservation
KW - Recommendation accuracy
UR - http://www.scopus.com/inward/record.url?scp=85038104097&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-72389-1_23
DO - 10.1007/978-3-319-72389-1_23
M3 - Conference proceeding contribution
AN - SCOPUS:85038104097
SN - 9783319723884
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 280
EP - 297
BT - Security, Privacy, and Anonymity in Computation, Communication, and Storage - 10th International Conference, SpaCCS 2017, Proceedings
A2 - Wang, Guojun
A2 - Atiquzzaman, Mohammed
A2 - Yan, Zheng
A2 - Choo, Kim-Kwang Raymond
PB - Springer, Springer Nature
CY - Cham, Switzerland
T2 - 10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017
Y2 - 12 December 2017 through 15 December 2017
ER -