Privacy-preserving distributed service recommendation based on locality-sensitive hashing

Lianyong Qi, Haolong Xiang, Wanchun Dou, Chi Yang, Yongrui (Louie) Qin, Xuyun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

38 Citations (Scopus)


With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users' service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSRLSH is put forward in this paper. Through LSH, DistSRLSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 24th International Conference on Web Services
Subtitle of host publicationICWS 2017
EditorsIlkay Altintas, Shiping Chen
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781538607527
ISBN (Print)9781538607534
Publication statusPublished - 7 Sept 2017
Externally publishedYes
Event24th IEEE International Conference on Web Services, ICWS 2017 - Honolulu, United States
Duration: 25 Jun 201730 Jun 2017


Conference24th IEEE International Conference on Web Services, ICWS 2017
Country/TerritoryUnited States


  • collaborative filtering
  • distributed service recommendation
  • efficiency
  • locality-sensitive hashing
  • privacy


Dive into the research topics of 'Privacy-preserving distributed service recommendation based on locality-sensitive hashing'. Together they form a unique fingerprint.

Cite this