[Retracted] Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Table 2

Comparison of detection characteristics.

Detection feature	Features	The essential	Feature evaluation
Attribute characteristics	Using artificial design methods, it is easy to bypass attackers. The algorithm design is simple; the efficiency is low, the accuracy rate is relatively low, the data level is trim, and it has strict privacy protection.	Breakthrough privacy protection	Uncommonly used
Content characteristics	Natural language processing method is adopted, which is easy to be bypassed by attackers. In addition, the algorithm design is complicated, the efficiency is low, the accuracy rate is relatively low, the data level is significant, and the privacy protection is slight.	Design complex algorithms and reasonable language models	Commonly used
Network characteristics	Adopting complex network processing methods, not easy to be bypassed by attackers, simple algorithm design, low efficiency, relatively low accuracy rate, significant data level, and no privacy protection.	Master the global structure	Mainstream
Activity characteristics	Using behavioural pattern analysis and processing methods, it is not easy to bypass attackers; the algorithm design is simple, the efficiency is high, the accuracy rate is high, the data level is significant, and the privacy protection is slight.	Select the most distinguishable activity information	Mainstream
Auxiliary features	Using time-series model analysis, it is not easy to bypass attackers; the algorithm design is complex, the efficiency is high, the accuracy rate is high, the data level is trim, and it has slight privacy protection.	Effective use of time dimension information	Popular