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13-07-2024

A Review of Anonymization Algorithms and Methods in Big Data

Authors: Elham Shamsinejad, Touraj Banirostam, Mir Mohsen Pedram, Amir Masoud Rahmani

Published in: Annals of Data Science

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Abstract

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data.

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Metadata
Title
A Review of Anonymization Algorithms and Methods in Big Data
Authors
Elham Shamsinejad
Touraj Banirostam
Mir Mohsen Pedram
Amir Masoud Rahmani
Publication date
13-07-2024
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-024-00557-w

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