Privacy Prevention of Big Data Applications: A Systematic Literature Review

Abstract

This paper focuses on privacy and security concerns in Big Data. This paper also covers the encryption techniques by taking existing methods such as differential privacy, k-anonymity, T-closeness, and L-diversity. Several privacy-preserving techniques have been created to safeguard privacy at various phases of a large data life cycle. The purpose of this work is to offer a comprehensive analysis of the privacy preservation techniques in Big Data, as well as to explain the problems for existing systems. The advanced repository search option was utilized for the search of the following keywords in the search: “Cyber security” OR “Cybercrime”) AND ((“privacy prevention”) OR (“Big Data applications”)). During Internet research, many search engines and digital libraries were utilized to obtain information. The obtained findings were carefully gathered out of which 103 papers from 2,099 were found to gain the best information sources to address the provided study subjects. Hence a systemic review of 32 papers from 103 found in major databases (IEEExplore, SAGE, Science Direct, Springer, and MDPIs) were carried out, showing that the majority of them focus on the privacy prediction of Big Data applications with a contents-based approach and the hybrid, which address the major security challenge and violation of Big Data. We end with a few recommendations for improving the efficiency of Big Data projects and provide secure possible techniques and proposed solutions and model that minimizes privacy violations, showing four different types of data protection violations and the involvement of different entities in reducing their impacts.

Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

“…With the proliferation of surveillance technologies and data collection practices, maintaining anonymity and pseudonymity becomes increasingly challenging, as individuals' activities, behaviors, and interactions are continuously monitored, tracked, and analyzed. The erosion of anonymity undermines individuals' ability to control their personal information, protect their privacy, and maintain autonomy over their digital identities (Rafiq, et al, 2022).…”

Technology integration in higher education and student privacy beyond learning environments—A comparison of the UK and US perspective

“…For instance, some scholars have been exploring cybersecurity risks in HE (Aliyu et al, 2020; Ulven & Wangen, 2021), while others have proposed interdisciplinary strategies to develop more privacy‐conscious mHealth interventions for the HE community (Blackmon & Lanford, 2023). Others still are exploring how to preserve privacy in Big Data applications (Rafiq et al, 2022) and how the institutional decision to centralise IT decision‐making may impact cybersecurity breaches (Liu et al, 2020). It is against this backdrop that we offer our legal contribution.…”

Next-Generation Intrusion Detection and Prevention System Performance in Distributed Big Data Network Security Architectures

“…However, as the use of big data systems grows, the capability of attackers to leverage associated parallel computing power for nefarious reasons also increases [3]. A systematic review of 32 papers pertaining to securing big data found that a critical need in future research is building more secure big data infrastructure [4]. Contributing to the latter objective, the researchers demonstrate how varying network architectures impact the security and performance of big data systems.…”