The Interactive Query Method with Clustering and Differential Privacy Protection Model Under Big Data Environment

2021 ◽  
pp. 327-336
Author(s):  
Huanyu Fan ◽  
Yunan Zhu ◽  
Chao Shan
Keyword(s):  
Big Data ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Interactive Query ◽  
Data Environment

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 Query Language for Medical Data: Study Design (Preprint)

2020 ◽  
Author(s):  
Huanhuan Wang ◽  
Xiang Wu ◽  
Yongqi Tan ◽  
Hongsheng Yin ◽  
Xiaochun Cheng ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Privacy Protection ◽  
Modular Design ◽  
Differential Privacy ◽  
Query Language ◽  
Medical Data ◽  
Medical Data Mining ◽  
Medical Big Data ◽  
Mining Algorithms

BACKGROUND Medical data mining and sharing is an important process to realize the value of medical big data in E-Health applications. However, medical data contains a large amount of personal private information of patients, there is a risk of privacy disclosure when sharing and mining. Therefore, how to ensure the security of medical big data in the process of publishing, sharing and mining has become the focus of current researches. OBJECTIVE The objective of our study is to design a framework based on differential privacy protection mechanism to ensure the security sharing of medical data. We developed a privacy Protection Query Language (PQL) that can integrate multiple machine mining methods and provide secure sharing functions for medical data. METHODS This paper adopts a modular design method with three sub-modules, including parsing module, mining module and noising module. Each module encapsulates different computing devices, such as composite parser, noise jammer, etc. In the PQL framework, we apply the differential privacy mechanism to the results of the module collaborative calculation to optimize the security of various mining algorithms. These computing devices operate independently, but the mining results depend on their cooperation. RESULTS Designed and developed a query language framework that provides medical data mining, sharing and privacy preserving functions. We theoretically proved the performance of the PQL framework. The experimental results showed that the PQL framework can ensure the security of each mining result, and the average usefulness of the output results is above 97%. CONCLUSIONS We presented a security framework that enables medical data providers to securely share the health data or treatment data, and developed a usable query language based on differential privacy mechanism that enables researchers to mine potential information securely using data mining algorithms. CLINICALTRIAL

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

scholarly journals Research on Privacy Protection Technology of Mobile Social Network Based on Data Mining under Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Jiawen Du ◽  
Yong Pi
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Big Data ◽  
Private Information ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Study Data ◽  
Data Publishing ◽  
Data Mining Algorithm ◽  
Data Mining Algorithms

With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user’s identity from being identified and the user’s private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users’ privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks.

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