Abstract
Recently, Device to Device (D2D) based mobile social networking in proximity (MSNP) has witnessed great development on smartphones, which enable actively/passively and continuously seek for relevant value in one’s physical proximity, through direct communicating with other individuals within the communication range, without the support of centralized networking infrastructure. Specially, a user would like to find out and interact with some strangers with similar interest in vicinity through profile matching. However, in matching process, individuals always have to reveal their personal and private profiles to strangers, which conflicts with users’ growing privacy concerns. To achieve privacy preserving profile matching (i.e., friend discovery), many schemes are proposed based on homomorphic and commutative encryption, which bring tremendous computation and communication overheads, and are not practical for the resource limited mobile devices in MSNP. In this paper we adapt Confusion Matrix Transformation (CMT) method to design a Lightweighted fIne-grained Privacy-Preserving Profile matching mechanism, LIP3, which can not only efficiently realize privacy-preserving profile matching, but obtain the strict measurement of cosine similarity between individuals, while other existing CMT-based schemes can only roughly estimate the matching value.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, Y.F., Vasilakos, A.V., Jin, Q., Ma, J.H.: Survey on mobile social networking in proximity (MSNP): approaches, challenges and architecture. ACM/Springer, Wirel. Netw. (WINET) 20(6), 1295–1311 (2014)
Wang, Y.F., Xu J.: Overview on privacy-preserving profile-matching mechanisms in mobile social networks in proximity (MSNP). In: Proceedings of the 9th Asia Joint Conference on Information Security (AsiaJCIS) (2014)
Niu, B., Zhang, T., Zhu, X., et al.: Priority-Aware Private Matching Schemes for Proximity-based Mobile Social Networks, arXiv preprint arXiv: 1401.8064 (2014)
Zhang, R., Zhang, Y., Sun, J., et al.: Fine-grained private matching for proximity-based mobile social networking. In: Proceedings of the IEEE INFOCOM (2012)
Zhang, R., Zhang, J., Zhang, Y., et al.: Privacy-preserving profile matching for proximity-based mobile social networking. IEEE J. Selected Areas Commun. 31(9), 656–668 (2013)
Zhu, H.J., Du, S.G., Li, M.Y., Gao, Z.Y.: Fairness-aware and privacy-preserving friend matching protocol in mobile social networks. IEEE Trans. Emerg. Topics Comput. 1(1), 192–200 (2013)
Lu, R., Lin, X., Shen, X.: SPOC: a secure and privacy-preserving opportunistic computing framework for mobile-healthcare emergency. IEEE Trans. Parallel Distrib. Syst. 24(3), 614–624 (2013)
Zhu, X.Y., Liu, J., Jiang, S.R., Chen, Z.B, Li, H., Efficient weight-based private matching for proximity-based mobile social networks. In: Proceedings of the IEEE ICC (2014)
Acknowledgments
This work was supported by the NSFC 61171092, the JiangSu Educational Bureau Project under Grant 14KJA510004, and Prospective Research Project on Future Networks (JiangSu Future Networks Innovation Institute).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Y., Chen, X., Jin, Q., Ma, J. (2015). LIP3: A Lightweighted Fine-Grained Privacy-Preserving Profile Matching Mechanism for Mobile Social Networks in Proximity. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9532. Springer, Cham. https://doi.org/10.1007/978-3-319-27161-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-27161-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27160-6
Online ISBN: 978-3-319-27161-3
eBook Packages: Computer ScienceComputer Science (R0)