Abstract
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.
Highlights
Driven by the wave of information technology, urban computing with urban backgrounds has emerged as an emerging field [1], with categories including transportation, environment, economics, social, medical services, and urban planning [2]
Medical big data security and privacy breaches caused by the data storage phase (A3), which includes 17 secondary indicators: Medical staff operation error (B4), Encryption and key management weak (B11), Hardware security (B13), Software security (B14), Virus intrusion(B15), Hacker attacks (B16), Internal personnel stealing information (B17), Physical environment (B18), Virtual vulnerability (B19), Firewall vulnerability (B20), Access control mechanism is not perfect (B21), Identity authentication technology is not complete (B22), Safety audit (B23), Data monitoring (B24), Digital certificate reliability (B25), IDS reliability (B26), Data backup and recovery (B27)
Since the weights of the indicators calculated by GI method will be affected by experts’ personal subjective factors, while the entropy weight method is an objective weight determined according to the variability of the indicators, and the subjective weights calculated by the GI method can be corrected to make the evaluation result more scientific and reasonable
Summary
This work was supported in part by the National Natural Science Foundation of China under Grant 71972165, Grant 61763048, Grant 61263022, and Grant 61303234, in part by the National Social Science Foundation of China under Grant 12XTQ012, in part by the Innovation and Promotion of Education Foundation Project of Science and Technology Development Center, Ministry of Education, under Grant 2018A01042, in part by the Science and Technology Foundation of Yunnan Province under Grant 2017FB095 and Grant 201901S070110, in part by the 18th Yunnan Young and Middle-Aged Academic and Technical Leaders Reserve Personnel Training Program under Grant 2015HB038.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
