scholarly journals Privacy Detection and Protection for Intelligent Transportation Shared Travel Service

CONVERTER ◽  
2021 ◽  
pp. 52-58
Author(s):  
Hui Ma, Yong Zhang
Keyword(s):  
Big Data ◽  
Privacy Protection ◽  
Security Policy ◽  
Intelligent Transportation ◽  
Data Encryption ◽  
Security Requirements ◽  
Protection Scheme ◽  
Private Data ◽  
System User ◽  
Data Environment

There is a large amount of privacy data in the big data environment. This paper studies the privacy detection and protection for intelligent transportation shared travel service. On the basis of traditional security policy, through the operation of anonymization of private data, the purpose of keeping private data secret under the premise of protecting data characteristics is realized. The anonymized private data can be used by data engineers to upgrade system functions and improve system user experience. In this paper, through the use of Hadoop project HBase tools, complete the data cleaning of privacy data, data desensitization operation. This paper designs and implements a privacy protection scheme for intelligent shared transportation system in big data environment. This paper uses K anonymous protection technology, data encryption technology to achieve the protection of privacy data. In this paper, HBase technology is used to desensitize the privacy data of the server. The experimental results show that the privacy protection scheme proposed in this paper can meet the security requirements of privacy data in transmission, storage and application.

scholarly journals A Privacy Risk Assessment Model for Medical Big Data Based on Adaptive Neuro-Fuzzy Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyue Shi ◽  
Rong Jiang ◽  
Wei Zhou ◽  
Sen Liu ◽  
Savio Sciancalepore
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Privacy Protection ◽  
Internet Security ◽  
Assessment Model ◽  
Security And Privacy ◽  
Risk Assessment Model ◽  
Experimental Process ◽  
Data Environment ◽  
Medical Big Data

Information leakage in the medical industry has become an urgent problem to be solved in the field of Internet security. However, due to the need for automated or semiautomated authorization management for privacy protection in the big data environment, the traditional privacy protection model cannot adapt to this complex open environment. Although some scholars have studied the risk assessment model of privacy disclosure in the medical big data environment, it is still in the initial stage of exploration. This paper analyzes the key indicators that affect medical big data security and privacy leakage, including user access behavior and trust, from the perspective of users through literature review and expert consultation. Also, based on the user’s historical access information and interaction records, the user’s access behavior and trust are quantified with the help of information entropy and probability, and a definition expression is given explicitly. Finally, the entire experimental process and specific operations are introduced in three aspects: the experimental environment, the experimental data, and the experimental process, and then, the predicted results of the model are compared with the actual output through the 10-fold cross verification with Matlab. The results prove that the model in this paper is feasible. In addition, the method in this paper is compared with the current more classical medical big data risk assessment model, and the results show that when the proportion of illegal users is less than 15%, the model in this paper is more superior in terms of accuracy and recall.

A differential privacy protection scheme for sensitive big data in body sensor networks

2016 ◽  
Vol 71 (9-10) ◽  
pp. 465-475 ◽  
Author(s):  
Chi Lin ◽  
Pengyu Wang ◽  
Houbing Song ◽  
Yanhong Zhou ◽  
Qing Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Sensor Networks ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Body Sensor Networks ◽  
Protection Scheme

scholarly journals A Pursuit of Sustainable Privacy Protection in Big Data Environment by an Optimized Clustered-Purpose Based Algorithm

2020 ◽  
Vol 26 (4) ◽  
pp. 1217-1231
Author(s):  
Norjihan Binti Abdul Ghani ◽  
Muneer Ahmad ◽  
Zahra Mahmoud ◽  
Raja Majid Mehmood
Keyword(s):  
Big Data ◽  
Privacy Protection ◽  
Data Environment
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