Privacy-Aware Framework for Matching Online Social Identities in Multiple Social Networking Services
- Authors
- Nguyen Hoang Long; Jung, Jason J.
- Issue Date
- Feb-2015
- Publisher
- TAYLOR & FRANCIS INC
- Keywords
- social networking services; social internetworking scenario; identity matching; privacy-aware
- Citation
- CYBERNETICS AND SYSTEMS, v.46, no.1-2, pp 69 - 83
- Pages
- 15
- Journal Title
- CYBERNETICS AND SYSTEMS
- Volume
- 46
- Number
- 1-2
- Start Page
- 69
- End Page
- 83
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/9863
- DOI
- 10.1080/01969722.2015.1007737
- ISSN
- 0196-9722
1087-6553
- Abstract
- With various emerging Social Networking Services (SNS), it is possible for users to join multiple SNS for social relationships with other users and to collect a large amount of information (e.g., statuses on Facebook and tweets on Twitter). However, these users have been facing difficulties in managing all the data collected from the multiple SNS. It is important to match social identities from the multiple SNS. In this study, we propose a privacy-aware framework for a social identity matching (SIM) method across these multiple SNS. It means that the proposed approach can protect user privacy, because only the public information (e.g., username and the social relationships of the users) is employed to find the best matches between social identities. As a result, we have shown by evaluation that the F-measure of the proposed SIM method is about 60%.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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