Big Data Privacy Preserving in Multi-Access Edge Computing for Heterogeneous Internet of Things

2018 ◽  
Vol 56 (8) ◽  
pp. 62-67 ◽  
Author(s):  
Miao Du ◽  
Kun Wang ◽  
Yuanfang Chen ◽  
Xiaoyan Wang ◽  
Yanfei Sun
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Edge Computing ◽  
Big Data Privacy ◽  
Multi Access

scholarly journals D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Wang ◽  
Hongtao Li ◽  
Feng Guo ◽  
Wenyin Zhang ◽  
Yifeng Cui
Keyword(s):  
Big Data ◽  
Private Information ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Computing Time ◽  
Privacy Preserving ◽  
D2d Communication ◽  
Group Data ◽  
Big Data Privacy ◽  
Daunting Challenge

As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. The results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.

scholarly journals Big Data Privacy in the Internet of Things Era

2015 ◽  
Vol 17 (3) ◽  
pp. 32-39 ◽  
Author(s):  
Charith Perera ◽  
Rajiv Ranjan ◽  
Lizhe Wang ◽  
Samee U. Khan ◽  
Albert Y. Zomaya
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Privacy ◽  
The Internet ◽  
Big Data Privacy ◽  
The Internet Of Things

Combination of Access Control and De-Identification for Privacy Preserving in Big Data

2016 ◽  
Vol 10 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Amine Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Privacy ◽  
Scientific Community ◽  
High Efficiency ◽  
Personal Information ◽  
Privacy Preserving ◽  
Big Data Privacy ◽  
Basic Concepts ◽  
Identification Techniques

Despite of its emergence and advantages in various domains, big data still suffers from major disadvantages. Timeless, scalability, and privacy are the main problems that hinder the advance of big data. Privacy preserving has become a wide search era within the scientific community. This paper covers the problem of privacy preserving over big data by combining both access control and data de-identification techniques in order to provide a powerful system. The aim of this system is to carry on all big data properties (volume, variety, velocity, veracity, and value) to ensure protection of users' identities. After many experiments and tests, our system shows high efficiency on detecting and hiding personal information while maintaining the utility of useful data. The remainder of this report is addressed in the presentation of some known works over a privacy preserving domain, the introduction of some basic concepts that are used to build our approach, the presentation of our system, and finally the display and discussion of the main results of our experiments.

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

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