Privacy preservation techniques through data lifecycle: A comprehensive literature survey

被引：0
作者：
Madhusudhanan, Sheema [1 ]
Jose, Arun Cyril [1 ]
机构：
[1] Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam (IIITK), Kerala, Kottayam
来源：
Computers and Security | 2025年 / 155卷
关键词：
Anonymization techniques; Data processing lifecycle; Machine learning for privacy; Privacy preservation; Transaction anonymity;
D O I：
10.1016/j.cose.2025.104473
中图分类号：
学科分类号：
摘要：
With the increasing user data volume, safeguarding sensitive information has become more critical than ever. This survey reviews privacy-preserving techniques and models designed to protect Personally Identifiable Information (PII) and other sensitive data. Privacy is essential at every data lifecycle stage, including data collection, storage, processing, sharing and transmission, retention and deletion, and access control. We discuss the challenges associated with each stage and highlight relevant research work. The survey concludes with a discussion of ongoing challenges and potential research directions in data privacy preservation. © 2025 Elsevier Ltd
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