A Review of Anonymization Algorithms and Methods in Big Data

Elham Shamsinejad (), Touraj Banirostam (), Mir Mohsen Pedram () and Amir Masoud Rahmani ()
Additional contact information
Elham Shamsinejad: Islamic Azad University
Touraj Banirostam: Islamic Azad University
Mir Mohsen Pedram: Kharazmi University
Amir Masoud Rahmani: Islamic Azad University

Annals of Data Science, 2025, vol. 12, issue 1, No 11, 253-279

Abstract: 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.

Keywords: Big Data; Anonymity; Confidentiality; Data Disclosure; Privacy (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-024-00557-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:12:y:2025:i:1:d:10.1007_s40745-024-00557-w

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-024-00557-w

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().