Privacy protection of medical data in social network

• Published in: BMC medical informatics and decision making 2021-10, Vol.21 (Suppl 1), p.1-286, Article 286
• Main Authors: Su, Jie, Cao, Yi, Chen, Yuehui, Liu, Yahui, Song, Jinming
• Format: Article
• Language: English
• Subjects: Algorithms; China; Cluster; Clustering; Data points; Data security; Datasets; Ethical aspects; Evaluation; Gender; Greedy algorithms; Health care; Health informatics; Hospitals; Information management; Information systems; K-anonymity; Legislation; Medical data; Medical records; Medical research; Methods; Optimization; Patients; Preservation; Privacy; Privacy protection; Privacy, Right of; Safety and security measures; Security; Social media; Social networks; Social organization; Society; Software; Technology application
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-021-01645-0

Background Protection of privacy data published in the health care field is an important research field. The Health Insurance Portability and Accountability Act (HIPAA) in the USA is the current legislation for privacy protection. However, the Institute of Medicine Committee on Health Research and the Privacy of Health Information recently concluded that HIPAA cannot adequately safeguard the privacy, while at the same time researchers cannot use the medical data for effective researches. Therefore, more effective privacy protection methods are urgently needed to ensure the security of released medical data. Methods Privacy protection methods based on clustering are the methods and algorithms to ensure that the published data remains useful and protected. In this paper, we first analyzed the importance of the key attributes of medical data in the social network. According to the attribute function and the main objective of privacy protection, the attribute information was divided into three categories. We then proposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. Finally, we analyzed the loss of information during the procedure of clustering, and evaluated the proposed approach with respect to classification accuracy and information loss rates on a medical dataset. Results The associated social network of a medical dataset was analyzed for privacy preservation. We evaluated the values of generalization loss and structure loss for different values of k and a, i.e. [formula omitted] = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30}, a = {0, 0.2, 0.4, 0.6, 0.8, 1}. The experimental results in our proposed approach showed that the generalization loss approached optimal when a = 1 and k = 21, and structure loss approached optimal when a = 0.4 and k = 3. Conclusion We showed the importance of the attributes and the structure of the released health data in privacy preservation. Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss. The proposed method to evaluate loss obtained a balance between the data availability and the risk of privacy leakage. Keywords: Medical data, Privacy protection, Cluster, K-anonymity