Exploring and Presenting Security Measures in Big Data Paradigm

2021 ◽  
pp. 51-68
Author(s):  
Astik Kumar Pradhan ◽  
Jitendra Kumar Rout ◽  
Niranjan Kumar Ray
Keyword(s):  
Big Data ◽  
Security Measures

Cloud-Centric Blockchain Public Key Infrastructure for Big Data Applications

2020 ◽  
pp. 125-140
Author(s):  
Brian Tuan Khieu ◽  
Melody Moh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Public Key Infrastructure ◽  
Public Key ◽  
Cloud Provider ◽  
Smart Contracts ◽  
Security Measures ◽  
Certificate Authority ◽  
Blockchain Technology ◽  
Attack Surface

A cloud-based public key infrastructure (PKI) utilizing blockchain technology is proposed. Big data ecosystems have scalable and resilient needs that current PKI cannot satisfy. Enhancements include using blockchains to establish persistent access to certificate data and certificate revocation lists, decoupling of data from certificate authority, and hosting it on a cloud provider to tap into its traffic security measures. Instead of holding data within the transaction data fields, certificate data and status were embedded into smart contracts. The tests revealed a significant performance increase over that of both traditional and the version that stored data within blocks. The proposed method reduced the mining data size, and lowered the mining time to 6.6% of the time used for the block data storage method. Also, the mining gas cost per certificate was consequently cut by 87%. In summary, completely decoupling the certificate authority portion of a PKI and storing certificate data inside smart contracts yields a sizable performance boost while decreasing the attack surface.

scholarly journals C4I System Security Architecture: A Perspective on Big Data Lifecycle in a Military Environment

2021 ◽  
Vol 13 (24) ◽  
pp. 13827
Author(s):  
Seungjin Baek ◽  
Young-Gab Kim
Keyword(s):  
Big Data ◽  
Smart Grids ◽  
Smart Cities ◽  
Security Requirements ◽  
Layered System ◽  
Security Architecture ◽  
Security Measures ◽  
System Framework ◽  
Data Lifecycle ◽  
The Military

Although the defense field is also one of the key areas that use big data for security reasons, there is a lack of study that designs system frameworks and presents security requirements to implement big data in defense. However, we overcome the security matters by examining the battlefield environment and the system through the flow of data in the battlefield. As such, this research was conducted to apply big data in the defense domain, which is a unique field. In particular, a three-layered system framework was designed to apply big data in the C4I system, which collects, manages, and analyzes data generated from the battlefield, and the security measures required for each layer were developed. First, to enhance the general understanding of big data and the military environment, an overview of the C4I system, the characteristics of the 6V’s, and the five-phase big data lifecycle were described. While presenting a framework that divides the C4I system into three layers, the roles and components of each layer are described in detail, considering the big data lifecycle and system framework. A security architecture is finally proposed by specifying security requirements for each field in the three-layered C4I system. The proposed system framework and security architecture more accurately explain the unique nature of the military domain than those studied in healthcare, smart grids, and smart cities; development directions requiring further research are described.

Security of NoSQL Database Against Intruders

2019 ◽  
Vol 13 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Khaleel Ahmad ◽  
Mohammad Shoaib Alam ◽  
Nur Izura Udzir
Keyword(s):  
Big Data ◽  
Relational Database ◽  
Large Scale ◽  
Distributed Databases ◽  
Structural Data ◽  
Database Management System ◽  
Base System ◽  
Security Measures ◽  
Nosql Databases ◽  
Nosql Database

Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.

IoT Big Data Security, Privacy and Challenges Related to Smart Grid

2022 ◽  
pp. 368-379
Author(s):  
Kimmi Kumari ◽  
M. Mrunalini
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Smart Grids ◽  
Type Systems ◽  
Heterogeneous Data ◽  
Security Measures ◽  
Smart Meters ◽  
Efficient Manner ◽  
Security Issues ◽  
Iot Devices

The highly interconnected network of heterogeneous devices which enables all kinds of communications to take place in an efficient manner is referred to as “IOT.” In the current situation, the data are increasing day by day in size as well as in terms of complexities. These are the big data which are in huge demand in the industrial sectors. Various IT sectors are adopting big data present on IOT for the growth of their companies and fulfilling their requirements. But organizations are facing a lot of security issues and challenges while protecting their confidential data. IOT type systems require security while communications which is required currently by configuration levels of security algorithms, but these algorithms give more priority to functionalities of the applications over security. Smart grids have become one of the major subjects of discussions when the demands for IOT devices increases. The requirements arise related to the generation and transmission of electricity, consumption of electricity being monitored, etc. The system which is responsible to collect heterogeneous data are a complicated structure and some of its major subsystems which they require for smooth communications include log servers, smart meters, appliances which are intelligent, different sensors chosen based on their requirements, actuators with proper and efficient infrastructure. Security measures like collection, storage, manipulations and a massive amount of data retention are required as the system is highly diverse in its architecture and even the heterogeneous IOT devices are interacting with each other. In this article, security challenges and concerns of IOT big data associated with smart grid are discussed along with the new security enhancements for identification and authentications of things in IOT big data environments.

scholarly journals Review: Big Data Privacy and Security Risk and Solutions

2021 ◽  
pp. 85-94
Author(s):  
Madhavi Tota
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
High Efficiency ◽  
Data Access ◽  
Database Systems ◽  
Mobile Internet ◽  
Security Measures ◽  
Security Risk ◽  
Privacy And Security ◽  
Big Data Privacy

Big Data is very dynamic issues in the current year, enables computing resources as a data to be provided as Information Technology services with high efficiency and effectiveness. The high amount of data in world is growing day by day. Data is growing very rapidly because of use of internet, smart phone and social network. Now size of the data is in Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyze this large amount of data. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the limits. However, the current scenario the growth rate of such large data creates number of challenges, such as the fast growth of data, access speed, diverse data, and security. This paper shows the fundamental concepts of Big Data. Privacy threats and security methods used in Big Data. With the development of various research application and recourses of Internet/Mobile Internet, social networks, Internet of Things, big data has become the very important topic of research across the world, at the same time, big data has security risks and privacy protection during different stages such as collecting, storing, analyzing and utilizing. This paper introduces security measures of big data, then proposes the technology to solve the security threats.

scholarly journals AN ENHANCED PRIVACY PRESERVING APPROACH WITH ENFORCING POLICIES FOR PROCESSING BIG DATA IN SPARK FRAMEWORK

2018 ◽  
Vol 7 (3.10) ◽  
Author(s):  
Revathy Swaminathan ◽  
Arunkumar Thangavelu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Privacy Preservation ◽  
Personal Information ◽  
Key Generation ◽  
Security Measures ◽  
Sensitive Data ◽  
Time Consumption ◽  
Critical Process ◽  
Spark Framework

Ensuring the privacy for the big data stored in a cloud system is one of the demanding and critical process in recent days. Generally, the big data contains a huge amount of data, which requires some security measures and rules for assuring the confidentiality.  For this reason, different techniques have been developed in the traditional works, which intends to guarantee the privacy of the big data by implementing key generation, encryption, and anonymization mechanisms. But, it limits the issues of increased time consumption, computational complexity, and error rate. Thus, the proposed work aims to design an enhanced mechanism for a secure big data storage. Here, the user’s bank dataset is considered as the input, which is protected from the unauthorized users by guaranteeing both the privacy and secrecy of the data. Here, the raw dataset is preprocessed to increase the data quality and correctness. Then, the security policies (i.e. rules) are generated for allowing the restricted access on the data by using an Improved FP-Growth (IFP-G) algorithm. Consequently, the sensitive and non-sensitive data attributes are classified based on the extracted features by using an Enhanced Random Forest (ERF) classification technique. At last, the privacy of user’s personal information and other details are protected with the use of a Modified Incognito Anonymization based Privacy Preservation (MIA-PP) algorithm. These enhanced mechanisms guarantee the security and confidentiality of the big data with reduced time consumption and increased accuracy. During experimental evaluation, the results of the proposed privacy mechanism is analyzed and compared by using different measures. Also, some of the existing anonymization and classification techniques have been considered to prove the betterment of the proposed technique. 

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