Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things
研究了在物联网医疗场景下，结合人工智能与K匿名模型，通过三种特征选择策略（最低不同值、最低熵、最高熵）来提升数据隐私保护中的熵效率，并与轻量级和MH-IoT等方法对比，证明所提方法在熵标准上更高效。
The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of “selecting the feature with the lowest number of distinct values,” “selecting the feature with the lowest entropy,” and “selecting the feature with the highest entropy.” For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria.