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A Secure and Efficient Privacy-Preserving Attribute Matchmaking Protocol in Proximity-Based Mobile Social Networks

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Advanced Data Mining and Applications (ADMA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8933))

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Abstract

Since the advent of matchmaking protocols, many such protocols have been proposed. The bane of most of these protocols has been with the preservation of users′ privacy and reduce or remove completely the leaking of users′ private information. Most of the existing matchmaking protocols simply match-pair persons without checking if they have enough common attributes to be an appropriate pair. Also, in most matchmaking protocols, since the inputs are private, malicious participants may choose their sets arbitrarily and use this flexibility to affect the results or learn more about the input of an honest individual. As an improvement, we propose a novel hybrid matchmaking protocol. In this proposed protocol, the initiator sets a threshold number of common attributes that a-would-be pair should have to qualify as a match-pair. With the use of certification of attributes to ensure that the inputs are not arbitrary and a preset threshold number of common attributes defined, an initiator can adequately find pair(s) without leaking any private information. In addition to helping find the most appropriate pair, our proposed protocol also has the ability to resist semi-honest and malicious attacks.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Electronic Science and Technology of China, Chengdu, China

    Solomon Sarpong & Chunxiang Xu

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  1. Solomon Sarpong
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  2. Chunxiang Xu
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Editors and Affiliations

  1. Sun Yat-sen University, Guangzhou, P.R. China

    Xudong Luo

  2. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Jeffrey Xu Yu

  3. Guanxi Normal University, Guilin, P.R. China

    Zhi Li

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Sarpong, S., Xu, C. (2014). A Secure and Efficient Privacy-Preserving Attribute Matchmaking Protocol in Proximity-Based Mobile Social Networks. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_24

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  • DOI: https://doi.org/10.1007/978-3-319-14717-8_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14716-1

  • Online ISBN: 978-3-319-14717-8

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