A Privacy Security Risk Analysis Method for Medical Big Data in Urban Computing

As an emerging computing mode, urban computing is mainly used to integrate, analyze and reuse urban resources by using perceptual computing, data mining and intelligent extraction to eliminate the phenomenon of data islands and provide wisdom for people to make decisions. But in the era of big data, the security and privacy leakage of users has become a major obstacle in urban computing. Taking medical big data as an example, this paper analyzed the risk of security and privacy leakage in the collection, transmission, storage, use and sharing of medical big data, and established a medical big data security and privacy leakage risk indicator system with 4 primary indicators and 35 secondary indicators. In addition, the weight of each indicator was calculated by GI method and entropy weight method. Then the fuzzy comprehensive evaluation model was established to verify the risk of medical big data security and privacy disclosure in urban computing. The results show that the risk of medical big data security and privacy leakage in the Grade II Level A hospitals is higher than that in the Grade III Level A hospitals, and in the life cycle of medical big data, the two stages of data storage, data use and sharing may cause more prominent problems of data security and privacy disclosure, while the data collection and data transmission are slightly less. Finally, the comparison of performance further proved the scientificity and effectiveness of this method.