Opinions of people: factoring in privacy and trust

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.

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Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction