@inproceedings{68388a6cda6c48ae8ec0952f2833d600,
title = "Privacy preserving trusted social feedback",
abstract = "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.",
keywords = "Privacy, Recommendation, Social Network, Trust",
author = "Anirban Basu and Corena, {Juan Camilo} and Shinsaku Kiyomoto and Stephen Marsh and Jaideep Vaidya and Guibing Guo and Jie Zhang and Yutaka Miyake",
year = "2014",
doi = "10.1145/2554850.2554860",
language = "English (US)",
isbn = "9781450324694",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery",
pages = "1706--1711",
booktitle = "Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014",
note = "29th Annual ACM Symposium on Applied Computing, SAC 2014 ; Conference date: 24-03-2014 Through 28-03-2014",
}