Anticollusion Attack Strategy Combining Trust Metrics and Secret Sharing for Friendships Protection

Author:

Tian Junfeng1ORCID,Li Yue1ORCID

Affiliation:

1. School of Cyberspace Security and Computer Institute, Hebei University, Baoding 071000, China

Abstract

Online social networks provide users with services such as online interaction, instant messaging, and information sharing. The friend search engine, a new type of social application, provides users with the service for querying the list of other individuals’ friends. Currently, the existing research focuses on independent attacks for friend search engines while ignoring the more complicated collusion attacks, which can expose more friendships that users are not willing to share. Compared with independent attackers, collusion attackers share query results by cooperating with each other. In this article, we propose a resistance strategy against collusion attacks to protect the friendship privacy. The proposed trust metric is based on users’ behaviors and is combined with Shamir’s secret sharing system, which can transform friendships into secrets. Through secret distribution and reconfiguration, only the participants who meet the query requirements can successfully reconstruct the secret, while the participants who do not meet the query conditions cannot successfully obtain the secret fragments even if they obtain the secret fragments. Experiments are conducted to verify the effectiveness of the proposed strategy and proved that this strategy can greatly limit the number of malicious attackers, greatly reduce the probability of successful collusion attacks, and reduce the number of victims.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

Reference38 articles.

1. Privacy-aware display strategy in friend search

2. Retrieving Hidden Friends: A Collusion Privacy Attack Against Online Friend Search Engine

3. Friendguard: a friend search engine with guaranteed friend exposure degree;J. Morris

4. A personalized privacy protection framework for mobile crowdsensing in IIoT;J. Xiong;IEEE Transactions on Industrial Informatics,2019

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