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Wireless Communications and Mobile Computing
Volume 2017 (2017), Article ID 1393026, 9 pages
Research Article

An Advanced Private Social Activity Invitation Framework with Friendship Protection

1Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA
2Department of Information Technology, Georgia Southern University, Statesboro, GA 30460, USA
3Department of Electrical Engineering, Georgia Southern University, Statesboro, GA 30460, USA

Correspondence should be addressed to Weitian Tong; ude.nrehtuosaigroeg@gnotw

Received 31 August 2017; Accepted 25 October 2017; Published 16 November 2017

Academic Editor: Chaokun Wang

Copyright © 2017 Weitian Tong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Due to the popularity of social networks and human-carried/human-affiliated devices with sensing abilities, like smartphones and smart wearable devices, a novel application was necessitated recently to organize group activities by learning historical data gathered from smart devices and choosing invitees carefully based on their personal interests. We proposed a private and efficient social activity invitation framework. Our main contributions are () defining a novel friendship to reduce the communication/update cost within the social network and enhance the privacy guarantee at the same time; () designing a strong privacy-preserving algorithm for graph publication, which addresses an open concern proposed recently; () presenting an efficient invitee-selection algorithm, which outperforms the existing ones. Our simulation results show that the proposed framework has good performance. In our framework, the server is assumed to be untrustworthy but can nonetheless help users organize group activities intelligently and efficiently. Moreover, the new definition of the friendship allows the social network to be described by a directed graph. To the best of our knowledge, it is the first work to publish a directed graph in a differentially private manner with an untrustworthy server.