A scheme of privacy protection based on genetic algorithm for behavior pattern of social media users
Shuang Ding Wu, Jianping Zeng, Zewen Zhang · 2017
In the context of big data, user data have been released on the Internet on a large scale. Although these data always adopt anonymous ID, the corresponding user's real identification and other private information are still exposed to possible violations by attackers with certain background knowledge. In order to protect user's privacy, Samarita and Sweeny proposed k-anonymity model, which has been widely used in privacy protection. This model is mainly used in point-anonymization at this stage, and there is no good way for line-anonymization. This paper describes a method of anonymization based on genetic algorithm to protect the privacy of user's behavior pattern in stock BBS, which is a development of line-anonymization. Experimental results show that, the method described in this paper performs well in protecting privacy using k-anonymity model and has a controllable information loss.