Privacy aware community based recommender service for conferences attendees

Ahmed M. Elmisery, Kevin Doolin, Dmitri Botvich

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

7 Citations (Scopus)


With the rapid growth of social networks and users communities the need to attain privacy for end-users becomes mandatory especially with the recent privacy breaches and the inefficiency of anonymisation techniques [1]. The problem of maintaining privacy in recommender services become increasingly important since it aims at finding information that might be interesting to end-users without disclosing their real interests to the service. In this paper, we present a middleware that runs in end-users' mobile phones to provide referrals for joining different sub-communities in conferences or exhibitions in private way. Moreover, the proposed middleware facilitates identifying similarity between various attendees in order to build a community with specific interest without disclosing their real preferences or interests to other parties. Our proposed middleware equipped with two cryptography protocols in order to achieve this purpose. In such case, the attendees can submit their preferences in an encrypted form and the further computation of recommendation proceeds over the encrypted data using secure multiparty computation protocols. We also provide a scenario for community based recommender service for conferences along with experimentation results. Our results shows that our proposed middleware not only protect the attendees' privacy, but also can maintain the recommendation accuracy.

Original languageEnglish
Title of host publicationAdvances in Knowledge-Based and Intelligent Information and Engineering Systems
PublisherIOS Press
Number of pages13
ISBN (Print)9781614991045
Publication statusPublished - 2012

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


  • Clustering
  • Community Recommendations
  • Middleware
  • Privacy


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