scholarly journals Unique Software Engineering Techniques: Panacea for Threat Complexities in Secure Multiparty Computation (MPC) with Big Data

2020 ◽  
Author(s):  
Uchechukwu Emejeamara ◽  
Udochukwu Nwoduh ◽  
Andrew Madu
Keyword(s):  
Big Data ◽  
Software Engineering ◽  
Data Security ◽  
Mitigation Strategies ◽  
Security Measures ◽  
Multiple Sources ◽  
Private Data ◽  
Special Software ◽  
Privacy Measures ◽  
Protocol Execution

Most large corporations with big data have adopted more privacy measures in handling their sensitive/private data and as a result, employing the use of analytic tools to run across multiple sources has become ineffective. Joint computation across multiple parties is allowed through the use of secure multi-party computations (MPC). The practicality of MPC is impaired when dealing with large datasets as more of its algorithms are poorly scaled with data sizes. Despite its limitations, MPC continues to attract increasing attention from industry players who have viewed it as a better approach to exploiting big data. Secure MPC is however, faced with complexities that most times overwhelm its handlers, so the need for special software engineering techniques for resolving these threat complexities. This research presents cryptographic data security measures, garbed circuits protocol, optimizing circuits, and protocol execution techniques as some of the special techniques for resolving threat complexities associated with MPC’s. Honest majority, asymmetric trust, covert security, and trading off leakage are some of the experimental outcomes of implementing these special techniques. This paper also reveals that an essential approach in developing suitable mitigation strategies is having knowledge of the adversary type.

scholarly journals Dynamic Data Replication for Higher Availability and Security

2021 ◽  
Vol 1 (1) ◽  
pp. 31-42
Author(s):  
Mohammad Hasan Abd
Keyword(s):  
Interpersonal Communication ◽  
Data Security ◽  
High Performance ◽  
Multiple Channels ◽  
Security Measures ◽  
Multiple Sources ◽  
Blockchain Technology ◽  
Strength Based ◽  
Military Applications ◽  
Usage Patterns

The paradigm and domain of data security is the key point as per the current era in which the data is getting transmitted to multiple channels from multiple sources. The data leakage and security loopholes are enormous and there is need to enforce the higher levels of security, privacy and integrity. Such sections incorporate e-administration, long range interpersonal communication, internet business, transportation, coordinations, proficient correspondences and numerous others. The work on security and trustworthiness is very conspicuous in the systems based situations and the private based condition. This examination original copy is exhibiting the efficacious use of security based methodology towards the execution with blockchain programming utilizing robustness and different devices. The blockchain based mix is currently days utilized for e-administrations and military applications for the noticeable security based applications. To work with the high performance approaches and algorithms, the blockchain technology is quite prominent and used in huge performance aware patterns whereby the need to enforce the security is there. The work integrates the usage patterns of blockchain technologies so that the overall security and integrity can be improved in which there is immutability and strength based algorithms for enforce the security measures.

scholarly journals Data Security Measures using Hybrid Encryption Technique

2019 ◽  
Vol 8 (10S) ◽  
pp. 323-325
Keyword(s):  
Data Security ◽  
Data Privacy ◽  
Research Work ◽  
Digital Data ◽  
Hadamard Transform ◽  
Security Measures ◽  
Novel Approach ◽  
Dna Cryptography ◽  
Privacy Measures ◽  
Using Data

Data security is a process of enhancing the data privacy measures to filebase, database, websites while preventing unauthorized access to the datasets and data-streams. Encryption is the key data security technique that prevents the access of digital data from unauthorized person or hackers. This research work proposes a novel approach using data transformation and encapsulation techniques namely Hadamard Transform along with DNA Cryptography and Amino Acid for enhancing data security.

scholarly journals Big-Data and Consumer Profiles – The hidden traps of data collection on social media networks

2019 ◽  
Vol 13 (1) ◽  
pp. 1070-1078
Author(s):  
Corina Pelau ◽  
Mihaela Stanescu ◽  
Daniela Serban
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Security ◽  
Intelligent Systems ◽  
Social Media Networks ◽  
Private Data ◽  
The Social ◽  
Consumer Data ◽  
Main Components ◽  
Or Applications

Abstract The development and increased popularity of the social media networks has changed the way consumers communicate and interact with each other. But besides the positive aspects regarding socializing, real-time communication and information sharing, the social media networks have also several disadvantages. Private data security, invasion of privacy, misuse of information are just some of the negative aspects associated to social media networks, of which many of the consumers are not aware. This paper gives an overview of the different methods in which private consumer data and consumer profiles are created with the help of social media networks. The paper focuses on three main components, namely the data provided by the consumers, the technologies that have the ability to collect data in an aware or unaware manner and the contribution and advantages of business in this process. The results show that some of the data are given by the consumer, while other are just gathered with the help of automated and intelligent systems or applications.

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.

scholarly journals The Impact of Big Data on Electronic Commerce in Profit Organisations in Saudi Arabia

2019 ◽  
Vol 10 (4) ◽  
pp. 106
Author(s):  
Bader A. Alyoubi
Keyword(s):  
Saudi Arabia ◽  
Big Data ◽  
Inventory Management ◽  
New Technologies ◽  
Predictive Analytics ◽  
Business Environment ◽  
Primary Data ◽  
Research Approach ◽  
Multiple Sources ◽  
The Impact

Big Data is gaining rapid popularity in e-commerce sector across the globe. There is a general consensus among experts that Saudi organisations are late in adopting new technologies. It is generally believed that the lack of research in latest technologies that are specific to Saudi Arabia that is culturally, socially, and economically different from the West, is one of the key factors for the delay in technology adoption in Saudi Arabia. Hence, to fill this gap to a certain extent and create awareness about Big Data technology, the primary goal of this research was to identify the impact of Big Data on e-commerce organisations in Saudi Arabia. Internet has changed the business environment of Saudi Arabia too. E-commerce is set for achieving new heights due to latest technological advancements. A qualitative research approach was used by conducting interviews with highly experienced professional to gather primary data. Using multiple sources of evidence, this research found out that traditional databases are not capable of handling massive data. Big Data is a promising technology that can be adopted by e-commerce companies in Saudi Arabia. Big Data’s predictive analytics will certainly help e-commerce companies to gain better insight of the consumer behaviour and thus offer customised products and services. The key finding of this research is that Big Data has a significant impact in e-commerce organisations in Saudi Arabia on various verticals like customer retention, inventory management, product customisation, and fraud detection.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

Sign in / Sign up

Export Citation Format

Share Document