A Smart Access Control Method for Online Social Networks Based on Support Vector Machine

• Published in: IEEE access Vol. 8; pp. 11096 - 11103
• Main Authors: Shan, Fangfang, Liu, Jizhao, Wang, Xueyuan, Liu, Weiguang, Zhou, Bing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access control; access control method; Algorithms; Control methods; Eigenvectors; Kernel; machine learning; Mathematical model; Online social networks; Policies; Privacy; Social networking (online); Social networks; support vector machine; Support vector machines; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2963932

With the rapid development of Internet technology, online social networks (OSNs) has become one of the main ways for people to develop social activities. In order to maintain and strengthen interpersonal relationships, users are willing to share personal behaviors, feelings and other things through OSNs. Whether these resources reveal private information or not depends on the appropriateness of the access control policies set by the owner. However, with the increasing number of friends and complex relationships, it becomes more and more difficult for OSNs users to set appropriate access control policies. Aiming at above problems, a smart access control method for online social networks is proposed based on SVM algorithm to realize smart access control on the basis of integrating relationship types and description information of published content as eigenvectors. The experimental results show that this mechanism can automatically recommend the list of visible friends according to the content published by users and the relationship between users and friends, allowing users to modify the list to obtain the final access control policy, which can effectively protect users' privacy information.