Abstract
The emerging growth of online social networks has opened new doors for various kinds of applications such as business intelligence and expanding social connections through friend recommendations. In particular, friend recommendation facilitates users to explore new friendships based on social network structures, user profile information (similar interest) or both. However, as the privacy concerns of users are on the rise, searching for new friends is not a straightforward task under the assumption that users' information is kept private. Along this direction, this paper proposes two private friend recommendation algorithms based on the social network structure and the users' social tags. The first protocol is more efficient from a user's perspective compared to the second protocol, and this efficiency gain comes at the expense of relaxing the underlying privacy assumptions. On the other hand, the second protocol provides the best security guarantee. In addition, we empirically analyze the complexities of the proposed protocols and provide various experimental results.
Recommended Citation
B. K. Samanthula and W. Jiang, "Interest-driven Private Friend Recommendation," Knowledge and Information Systems, vol. 42, no. 3, pp. 663 - 687, Springer, Mar 2015.
The definitive version is available at https://doi.org/10.1007/s10115-013-0699-6
Department(s)
Computer Science
Keywords and Phrases
Friend recommendation; Privacy; Social tags
International Standard Serial Number (ISSN)
0219-3116; 0219-1377
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Mar 2015
Comments
National Science Foundation, Grant CNS-1011984