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Interest-driven private friend recommendation

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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.

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Notes

  1. More specifically, the Euclidean length/norm of \(v_i\) and \(v_j\) are given as \(||v_i|| = \sqrt{\sum _{k=1}^{n} v_{i}[k]^2}\) and \(||v_j|| = \sqrt{\sum _{k=1}^{n} v_{j}[k]^2}\).

  2. To make the presentation clear we simply omit the \(\hbox { mod } N^2\) operation in the expansion of Eq. 5. However, we emphasize that this does not affect our derived results.

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Acknowledgments

The authors would like to thank the anonymous reviewers of the Knowledge and Information Systems (KAIS) Journal for their helpful comments. This material is based upon work supported by the Office of Naval Research under Award No. N000141110256 and NSF under award No. CNS-1011984.

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  1. Department of Computer Science, Missouri University of Science and Technology, 500 West 15th Street, Rolla, MO, 65401, USA

    Bharath K. Samanthula & Wei Jiang

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  1. Bharath K. Samanthula
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Correspondence to Wei Jiang.

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Samanthula, B.K., Jiang, W. Interest-driven private friend recommendation. Knowl Inf Syst 42, 663–687 (2015). https://doi.org/10.1007/s10115-013-0699-6

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