Challenges and techniques in Big data security and privacy: A review

Bao, Rongxin; Chen, Zhikui; Obaidat, Mohammad S.

doi:10.1002/spy2.13

Cited by 23 publications

(13 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different privacy related issues and solutions were found in [18], [19], [20] and [21]. From the review of literature, it is understood that the sanitization techniques were used to solve the problem of big data privacy.…”

Section: Related Workmentioning

confidence: 99%

One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

2019

IJRTE

View full text Add to dashboard Cite

Big data privacy has assumed importance as the cloud computing became a phenomenal success in providing a remote platform for sharing computing resources without geographical and time restrictions. However, the privacy concerns on the big data being outsourced to public cloud storage are still exist. Different anonymity or sanitization techniques came into existence for protecting big data from privacy attacks. In our prior works, we have proposed a misusability probability based metric to know the probable percentage of misusability. We additionally planned a system that suggests level of sanitization before actually applying privacy protection to big data. It was based on misusability probability. In this paper, our focus is on further evaluation of our misuse probability based sanitization of big data approach by defining an algorithm which willanalyse the trade-offs between misuse probability and level of sanitization. It throws light into the proposed framework and misusability measure besides evaluation of the framework with an empirical study. Empirical study is made in public cloud environment with Amazon EC2 (compute engine), S3 (storage service) and EMR (MapReduce framework). The experimental results revealed the dynamics of the trade-offs between them. The insights help in making well informed decisions while sanitizing big data to ensure that it is protected without losing utility required.

show abstract

Section: Related Workmentioning

confidence: 99%

One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…Because of the diversity of data generation sources such as Internet of Things (IoT) sensors, Radio-Frequency Identification (RFID), social media, Global Positioning System (GPS), log files, images, videos, texts, and the computerization of all life aspects, the flood of data is increasing exponentially [9]. Due to the exponential growth of data gathered through numerous methods, big data is critical in a wide variety of fields, including business, search engines, and education [2]. The massive volume of data makes processing unfeasible using conventional database and software technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Regrettably, the development of big data continues to face numerous obstacles, with security and privacy concerns ranking among the most serious. For instance, a retail website monitors our shopping history; a social network records our thoughts and private images; and a bank monitors our private property information [2]. It appears as though all of your data and actions are in the hands of others, which is really disturbing.…”

Section: Introductionmentioning

confidence: 99%

Challenges and Obstacles Facing Data in the Big Data Environment

Aloufi¹,

Abdulaziz

2022

Comm. Math. Appl.

View full text Add to dashboard Cite

Big data is dubbed "today's digital oil" and the "new raw resource of the twenty-first century". BD is synonymous with the future of innovation, competition, and productivity. It can produce and find corporate value by analyzing data in ways that older methodologies could not. Regardless of its benefits, the development of big data continues to encounter various barriers, the most important of which are security and privacy concerns. As a result, this study is motivated by the need to address and evaluate big data challenges. Thus, by comparing and contrasting big data difficulties with available and potential solutions, users, developers, and businesses can find pertinent and timely responses to specific dangers, resulting in the best possible big data-based services. The objective of this essay is to highlight the inherent challenges of big data and some essential strategies for overcoming them.The purpose of this article was to extract and analyze significant works in order to contribute to the corpus of literature by emphasizing many critical difficulties in the big data domain and throwing light on how these challenges affect a range of domains, including users, sites, and business. Many issues such as data privacy, information sharing, failures in big data technologies and infrastructure, poor data quality management, managers and policymakers' inability to learn and adapt, the absence of government policies and plans, the lack of successfully implemented big data analytics projects, and the lack of human experience are among the most frequently mentioned issues. Obstacles also exist in the areas of real-time data collection, as well as real-time data processing and visualization, among others.By combining previously identified solutions, this research addressed these concerns. The consequences for both researchers and practitioners have been discussed. Aiming to help scholars gain a comprehensive understanding of these issues and confirm the approaches used to address them, this study's theoretical focus is broad. This study uses tried-and-true solutions to overcome these obstacles. Business and individuals using big data analytics systems will benefit from these solutions.

show abstract

“…There has been a significant increase in the amount of Big Data, with an increase from the PETA-BYTE to ZETA-BYTE level in only the past two decades. The EMC and the IDC predicted that the amount of data would be more than 40 ZETA-BYTES in the year 2020 [3,4]. Newer forecasts by the IDC predict that amount will reach 75 ZETA-BYTES by the year 2025 [5].…”

Section: Introductionmentioning

confidence: 99%

A Proposed Framework for Secure Data Storage in a Big Data Environment Based on Blockchain and Mobile Agent

et al. 2021

View full text Add to dashboard Cite

The sum of Big Data generated from different sources is increasing significantly with each passing day to extent that it is becoming challenging for traditional storage methods to store this massive amount of data. For this reason, most organizations have resolved to use third-party cloud storage to store data. Cloud storage has advanced in recent times, but it still faces numerous challenges with regard to security and privacy. This paper discusses Big Data security and privacy challenges and the minimum requirements that must be provided by future solutions. The main objective of this paper is to propose a new technical framework to control and manage Big Data security and privacy risks. A design science research methodology is used to carry out this project. The proposed framework takes advantage of Blockchain technology to provide secure storage of Big Data by managing its metadata and policies and eliminating external parties to maintain data security and privacy. Additionally, it uses mobile agent technology to take advantage of the benefits related to system performance in general. We present a prototype implementation for our proposed framework using the Ethereum Blockchain in a real data storage scenario. The empirical results and framework evaluation show that our proposed framework provides an effective solution for secure data storage in a Big Data environment.

show abstract

Challenges and techniques in Big data security and privacy: A review

Cited by 23 publications

References 51 publications

One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

Challenges and Obstacles Facing Data in the Big Data Environment

A Proposed Framework for Secure Data Storage in a Big Data Environment Based on Blockchain and Mobile Agent

Contact Info

Product

Resources

About