Privacy preserving trusted social feedback

Anirban Basu, Juan Camilo Corena, Shinsaku Kiyomoto, Stephen Marsh, Jaideep Vaidya, Guibing Guo, Jie Zhang, Yutaka Miyake

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


With the growth of social networks, recommender systems have taken advantage of the social network graph structures to provide better recommendation. In this paper, we propose a privacy preserving trusted social feedback (TSF) system, in which users obtain feedback on questions or items from their friends. It is different from and independent of a typical recommender system because the responses from friends are not automated but tailored to specific questions. TSF can be used to complement the results from a recom-mender system. Our experimental prototype runs on the Google App Engine and utilises the Facebook social network graph. In our experimental evaluation, we have looked at users' perceptions of privacy and their trust in the prototype as well as the performances on the client side and the cloud side.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Print)9781450324694
StatePublished - 2014
Event29th Annual ACM Symposium on Applied Computing, SAC 2014 - Gyeongju, Korea, Republic of
Duration: Mar 24 2014Mar 28 2014

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Other29th Annual ACM Symposium on Applied Computing, SAC 2014
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Software


  • Privacy
  • Recommendation
  • Social Network
  • Trust


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