10th EAI International Conference on Mobile Multimedia Communications

Research Article

Smart and Private Social Activity Invitation Framework Based on Historical Data from Smart Devices

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  • @INPROCEEDINGS{10.4108/eai.13-7-2017.2270271,
        author={Weitian Tong and Scott Buglass and Jeffrey Li and Lei Chen and Chunyu Ai},
        title={Smart and Private Social Activity Invitation Framework Based on Historical Data from Smart Devices},
        proceedings={10th EAI International Conference on Mobile Multimedia Communications},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2017},
        month={12},
        keywords={smart device activity invitation social network differential privacy k-core graph perturbed graph},
        doi={10.4108/eai.13-7-2017.2270271}
    }
    
  • Weitian Tong
    Scott Buglass
    Jeffrey Li
    Lei Chen
    Chunyu Ai
    Year: 2017
    Smart and Private Social Activity Invitation Framework Based on Historical Data from Smart Devices
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.13-7-2017.2270271
Weitian Tong1,*, Scott Buglass1, Jeffrey Li1, Lei Chen2, Chunyu Ai3
  • 1: Department of Computer Sciences, Georgia Southern University
  • 2: Department of Information Technology, Georgia Southern University
  • 3: Division of Mathematics & Computer Science, University of South Carolina Upstate
*Contact email: wtong@georgiasouthern.edu

Abstract

Modern social networks bring people together and help facilitate the organization of various group activities. Inspired by the recent work by Ai et al. [2], we design a smart and private social activity invitation framework based on historical data from smart devices. Our paradigm aims at helping users organize group activities in a smart and efficient way while finding compromises to satisfy all involved parties. Compared with Ai et al.’s work [2], our framework is more realistic, whereby users report their personal information to the app server, which is used to provide organizing services to registered members. The app server, however, is untrustworthy and could be motivated by factors such as advertising revenue. Therefore, the app may advertise itself by providing aggregate statistical information about current users to attract new users. This creates a dilemma between the existing users’ concerns about personal privacy and the app developers’ agenda. Our framework ameliorates this conflict by securing existing users’ information under a state-of-the-art privacy concept – differential privacy – guaranteeing quality services to existing users, while also allowing the server to give informative answers to new potential users. In addition, the proposed framework encourages less active or isolated users via a new method based on perturbed graphs. Our simulation results demonstrate that the proposed framework performs well.