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Privacy preserving trusted social feedback

Published: 24 March 2014 Publication History

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 recommender 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.

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Cited By

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  • (2019)Verifiable top-k searchable encryption for cloud dataSādhanā10.1007/s12046-019-1227-545:1Online publication date: 20-Dec-2019
  • (2018)From existing trends to future trends in privacy-preserving collaborative filteringWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11635:6(276-291)Online publication date: 14-Dec-2018
  • (2014)Foreground Trust as a Security ParadigmInformation Security in Diverse Computing Environments10.4018/978-1-4666-6158-5.ch002(8-23)Online publication date: 2014
  • Show More Cited By

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    cover image ACM Conferences
    SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    March 2014
    1890 pages
    ISBN:9781450324694
    DOI:10.1145/2554850
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 24 March 2014

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    Author Tags

    1. privacy
    2. recommendation
    3. social network
    4. trust

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    SAC 2014
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    SAC 2014: Symposium on Applied Computing
    March 24 - 28, 2014
    Gyeongju, Republic of Korea

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    SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    Cited By

    View all
    • (2019)Verifiable top-k searchable encryption for cloud dataSādhanā10.1007/s12046-019-1227-545:1Online publication date: 20-Dec-2019
    • (2018)From existing trends to future trends in privacy-preserving collaborative filteringWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11635:6(276-291)Online publication date: 14-Dec-2018
    • (2014)Foreground Trust as a Security ParadigmInformation Security in Diverse Computing Environments10.4018/978-1-4666-6158-5.ch002(8-23)Online publication date: 2014
    • (2014)Opinions of peopleACM SIGAPP Applied Computing Review10.1145/2670967.267096814:3(7-21)Online publication date: 22-Sep-2014

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