A new verifiable outsourced ciphertext-policy attribute based encryption for big data privacy and access control in cloud

2018 ◽  
Vol 10 (7) ◽  
pp. 2693-2707 ◽  
Author(s):  
Praveen Kumar Premkamal ◽  
Syam Kumar Pasupuleti ◽  
P. J. A. Alphonse
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Privacy ◽  
Attribute Based Encryption ◽  
Big Data Privacy ◽  
Ciphertext Policy

scholarly journals A Survey on Security Models for Data Privacy in Big Data Analytics

2018 ◽  
Vol 7 (S1) ◽  
pp. 87-89
Author(s):  
Avula Satya Sai Kumar ◽  
S. Mohan ◽  
R. Arunkumar
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Private Key ◽  
Access Structures ◽  
Security Models ◽  
Attribute Based Encryption ◽  
And Performance

As emerging data world like Google and Wikipedia, volume of the data growing gradually for centralization and provide high availability. The storing and retrieval in large volume of data is specialized with the big data techniques. In addition to the data management, big data techniques should need more concentration on the security aspects and data privacy when the data deals with authorized and confidential. It is to provide secure encryption and access control in centralized data through Attribute Based Encryption (ABE) Algorithm. A set of most descriptive attributes is used as categorize to produce secret private key and performs access control. Several works proposed in existing based on the different access structures of ABE algorithms. Thus the algorithms and the proposed applications are literally surveyed and detailed explained and also discuss the functionalities and performance aspects comparison for desired ABE systems.

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.

Big Data Privacy Breach Prevention Strategies

2020 ◽  
Author(s):  
Shipra Varshney ◽  
Dheeraj Munjal ◽  
Orijit Bhattacharya ◽  
Shagun Saboo ◽  
Nikunj Aggarwal
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Prevention Strategies ◽  
Privacy Breach ◽  
Big Data Privacy

Versatile lightweight key distribution for big data privacy in vehicular ad hoc networks

2015 ◽  
Vol 28 (10) ◽  
pp. 2920-2939 ◽  
Author(s):  
Bo Qin ◽  
Linxiao Wang ◽  
Yujue Wang ◽  
Qianhong Wu ◽  
Wenchang Shi ◽  
...  
Keyword(s):  
Big Data ◽  
Ad Hoc Networks ◽  
Data Privacy ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Key Distribution ◽  
Big Data Privacy ◽  
Hoc Networks

Driving for Big Data? Privacy Concerns in Vehicular Networking

2014 ◽  
Vol 12 (1) ◽  
pp. 77-79 ◽  
Author(s):  
David Eckhoff ◽  
Christoph Sommer
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Concerns ◽  
Vehicular Networking ◽  
Big Data Privacy
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