Privacy‐preserving and efficient user matching based on attribute encryption in mobile social networks

Lei Wu, Shengnan Zhao, Xia Wei and Lingzhen Meng

International Journal of Network Management, 2023, vol. 33, issue 3

Abstract: With the rapid popularity of social networking platforms, users can be matched when sharing their profiles. However, there is a risk of leakage of sensitive user information during the user matching process, which leads to the lack of user privacy protection. In this paper, we propose a privacy protection scheme based on the encryption of hidden attributes during user matching in mobile social networks, which uses linear secret sharing scheme (LSSS) as the access structure based on ciphertext policy attribute‐based encryption (CP‐ABE), and the match server can perform friend recommendation by completing bi‐directional attribute matching determination without disclosing user attribute information. In addition, the use of selective keywords protects the privacy of requesters and publishers in selecting keywords and selecting plaintext attacks. The scheme reduces the encryption and decryption overhead for users by dividing encryption into a preparation phase and an online phase and shifting most of the decryption overhead from the requester to the match server. The experimental results show that the scheme ensures user privacy while effectively reducing communication overhead.

Date: 2023
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https://doi.org/10.1002/nem.2192

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Persistent link: https://EconPapers.repec.org/RePEc:wly:intnem:v:33:y:2023:i:3:n:e2192

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