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  1. ACM SIGAPP Applied Computing Review (SIAP)
  2. Volume 14
  3. Volume 14, Issue 3, September 2014
  4. Opinions of people
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Volume 17
Volume 16
Volume 15
Volume 14
Volume 14, Issue 4, December 2014
Volume 14, Issue 3, September 2014
Opinions of people
Hardware-software collaboration for secure coexistence with kernel extensions
Early response markers from video games for rehabilitation strategies
Deployment and activation of faulty components at runtime for testing self-recovery mechanisms
Error Control Based Energy Minimization for Cooperative Communication in WSN
Volume 14, Issue 2, June 2014
Volume 14, Issue 1, March 2014
Volume 13
Volume 12
Volume 11
Volume 10
Volume 9
Volume 8
Volume 7
Volume 6
Volume 5
Volume 4
Volume 3
Volume 2
Volume 1

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Opinions of people:factoring in privacy and trust

Content Provider ACM Digital Library
Author Vaidya, Jaideep Kiyomoto, Shinsaku Basu, Anirban Miyake, Yutaka Guo, Guibing Corena, Juan Camilo Zhang, Jie Marsh, Stephen
Abstract The growth of online social networks has seen the utilisation of these network graphs for the purpose of providing recommendations. Automated recommendations, however, do not take into account inter-personal trust levels that exist in a social network. In this article, we propose a privacy-preserving trusted social feedback (TSF) scheme where users can obtain feedback on questions from their friends whom they trust. We show that the concept can be extended to the domain of crowdsourcing -- the trusted crowdsourcing (TCS) scheme. In crowdsourcing, instead of asking friends, one can solicit opinions from experts in the crowd through a privacy preserving trusted feedback mechanism. Our proposal supports categorical answers as well as single-valued numerical answers. We evaluate our proposals in a number of ways: based on a prototype implementation built atop the Google App Engine, we illustrate the performance of the trusted social feedback. In addition, we present a user study to measure the impact that our trusted social feedback proposal has on users' perception of privacy and on foreground trust. We also present another user study to capture a model for user acceptance testing of the trusted crowdsourcing.
Starting Page 7
Ending Page 21
Page Count 15
File Format PDF
ISSN 15596915 19310161
DOI 10.1145/2670967.2670968
Journal ACM SIGAPP Applied Computing Review (SIAP)
Volume Number 14
Issue Number 3
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-06-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Privacy Category Crowd sourcing Social network Trust Recommendation
Content Type Text
Resource Type Article
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