scholarly journals A STUDY ON GREEN DATA REVOLUTION: OPPORTUNITIES AND CHALLENGES

2018 ◽  
pp. 73-81
Author(s):  
Heena Makhija ◽  
Bhavesh Bharad
Keyword(s):  
Big Data ◽  
Soil Fertility ◽  
Data Security ◽  
Data Privacy ◽  
New Product ◽  
Soil Conditions ◽  
Sensitive Information ◽  
Agriculture Industry

India is agriculture country. Big data has found its way to the agriculture industry. The problem of inflation, wastage, low productivity, soil fertility, productivity, financing to farmers and the lack of institutional farmers can be addressed through the data. However, while it can be helpful with full of opportunities on one level it comes with handful of challenges. The study focuses on challenges such as the use of collected data by farmers and companies, who collect and store data on everything from fertilizers, rate to yield to soil conditions. The study focuses on issues such as data security, data privacy and data analyzing. The paper also highlights challenges faced in agriculture data revolution, such as the approach of companies to sell the data to others or make a new product based on sensitive information.

Internet of Things Services, Applications, Issues, and Challenges

2017 ◽  
pp. 491-506
Author(s):  
Padmalaya Nayak
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Wireless Communication ◽  
Embedded Systems ◽  
Data Security ◽  
Data Privacy ◽  
The Internet ◽  
Human Intervention ◽  
Unique Identifier ◽  
Mechanical Devices

Internet of Things (IoT) is not a futuristic intuition, it is present everywhere. It is with devices, Sensors, Clouds, Big data, and data with business. It is the combination of traditional embedded systems combined with small wireless micro sensors, control systems with automation, and others that makes a huge infrastructure. The integration of wireless communication, micro electro mechanical devices, and Internet has led to the development of new things in the Internet. It is a network of network objects that can be accessed through the Internet and every object can be identified by unique identifier. By replacing IPV4, IPV6 plays a key role and provides a huge increase of address spaces for the development of things in the Internet. The objective of IoT application is to make the things smart without the human intervention. With the increasing number of smart nodes and amount of data that generated by each node is expected to create new concerns about data privacy, data scalability, data security, data manageability and many more issues that have been discussed in this chapter.

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

2021 ◽  
pp. 327-343
Author(s):  
Anitha J. ◽  
Prasad S. P.
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Personal Information ◽  
Technological Development ◽  
High Dimensional ◽  
Sensitive Information ◽  
Data Anonymization ◽  
Secure Protocol ◽  
Data Owner

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.

scholarly journals Research on Big data Security privacy Protection based on cloud Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042077
Author(s):  
Tongtong Xu ◽  
Lei Shi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Security ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cloud Services ◽  
Security Mechanism ◽  
Computing Platform ◽  
Cloud Servers

Abstract Cloud computing is a new way of computing and storage. Users do not need to master professional skills, but can enjoy convenient network services as long as they pay according to their own needs. When we use cloud services, we need to upload data to cloud servers. As the cloud is an open environment, it is easy for attackers to use cloud computing to conduct excessive computational analysis on big data, which is bound to infringe on others’ privacy. In this process, we inevitably face the challenge of data security. How to ensure data privacy security in the cloud environment has become an urgent problem to be solved. This paper studies the big data security privacy protection based on cloud computing platform. This paper starts from two aspects: implicit security mechanism and display security mechanism (encryption mechanism), so as to protect the security privacy of cloud big data platform in data storage and data computing processing.

Internet of Things Services, Applications, Issues, and Challenges

2017 ◽  
pp. 353-368
Author(s):  
Padmalaya Nayak
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Wireless Communication ◽  
Embedded Systems ◽  
Data Security ◽  
Data Privacy ◽  
The Internet ◽  
Human Intervention ◽  
Unique Identifier ◽  
Mechanical Devices

Internet of Things (IoT) is not a futuristic intuition, it is present everywhere. It is with devices, Sensors, Clouds, Big data, and data with business. It is the combination of traditional embedded systems combined with small wireless micro sensors, control systems with automation, and others that makes a huge infrastructure. The integration of wireless communication, micro electro mechanical devices, and Internet has led to the development of new things in the Internet. It is a network of network objects that can be accessed through the Internet and every object can be identified by unique identifier. By replacing IPV4, IPV6 plays a key role and provides a huge increase of address spaces for the development of things in the Internet. The objective of IoT application is to make the things smart without the human intervention. With the increasing number of smart nodes and amount of data that generated by each node is expected to create new concerns about data privacy, data scalability, data security, data manageability and many more issues that have been discussed in this chapter.

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

2020 ◽  
pp. 347-363
Author(s):  
Anitha J. ◽  
Prasad S. P.
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Personal Information ◽  
Technological Development ◽  
High Dimensional ◽  
Sensitive Information ◽  
Data Anonymization ◽  
Secure Protocol ◽  
Data Owner

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.

Big Data Security Challenges and Solution of Distributed Computing in Hadoop Environment: A Security Framework

2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Sets ◽  
Security Framework ◽  
Hadoop Distributed File System ◽  
Current Scenario ◽  
Hadoop Cluster ◽  
Ciphertext Policy ◽  
In Transit ◽  
Hadoop Framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

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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