Enhancing Data Privacy: A Comprehensive Survey of Privacy-Enabling Technologies

被引:0
|
作者
Razi, Qaiser [1 ]
Piyush, Raja [2 ]
Chakrabarti, Arjab [2 ]
Singh, Anushka [2 ]
Hassija, Vikas [2 ]
Chalapathi, G. S. S. [1 ]
机构
[1] Birla Inst Technol & Sci BITS Pilani, Dept Elect & Elect Engn, Pilani Campus, Pilani 333031, Rajasthan, India
[2] Kalinga Inst Ind Technol KIIT, Sch Comp Engn, Bhubaneswar 75102, Odisha, India
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Data privacy; Privacy; Information integrity; Information filtering; Encryption; Synthetic data; Differential privacy; Surveys; Bayes methods; Protection; Privacy engineering; data anonymization; data encryption; synthetic data; differential privacy; privacy preservation; privacy technologies; DIFFERENTIAL PRIVACY; TARGET CLASSIFICATION; MANAGEMENT SCHEME; DE-ANONYMIZATION; BIG DATA; ENCRYPTION; UTILITY; MICROAGGREGATION; ALGORITHM; EFFICIENT;
D O I
10.1109/ACCESS.2025.3546618
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Privacy is a fundamental human right, especially crucial in our modern digital age. With the rapid advancement of technology, ensuring individuals' privacy has become increasingly complex. Our survey paper aims to shed light on various privacy engineering technologies that play a crucial role in protecting personal data. We delve into four key areas: data anonymization, data encryption, synthetic data generation, and differential privacy. These technologies serve as essential tools in safeguarding online privacy. Data anonymization, for instance, includes removing or modifying identifiable information from datasets to protect individuals' identities. Encryption secures data by converting it into a code that can only be decoded by authorized parties. Synthetic data generation creates artificial data that closely resembles real data but doesn't contain any identifiable information. Differential privacy adds a small amount of controlled noise to protect sensitive information. Throughout our exploration, we not only explain the principles and techniques behind these technologies but also the tools used for each of these techniques and evaluation criteria and also examine their practical applications. By understanding their strengths, limitations, and real-world implementations, we gain valuable insights into how they contribute to the broader goal of ensuring privacy in our digital world.
引用
收藏
页码:40354 / 40385
页数:32
相关论文
共 50 条
  • [31] Practical Privacy-Enhancing Technologies
    Hajny, Jan
    Malina, Lukas
    Dzurenda, Petr
    2015 38TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), 2015, : 60 - 64
  • [32] A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures
    Obaidat, Muath A.
    Obeidat, Suhaib
    Holst, Jennifer
    Al Hayajneh, Abdullah
    Brown, Joseph
    COMPUTERS, 2020, 9 (02)
  • [33] Protecting Privacy in Digital Records: The Potential of Privacy-Enhancing Technologies
    Lemieux, Victoria L.
    Werner, John
    ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE, 2023, 16 (04):
  • [34] Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey
    Majeed, Abdul
    Lee, Sungchang
    IEEE ACCESS, 2021, 9 : 8512 - 8545
  • [35] Privacy-preserving big data analytics - A comprehensive survey
    Tran, Hong-Yen
    Hu, Jiankun
    JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2019, 134 : 207 - 218
  • [36] Selecting Privacy-Enhancing Technologies for Managing Health Data Use
    Jordan, Sara
    Fontaine, Clara
    Hendricks-Sturrup, Rachele
    FRONTIERS IN PUBLIC HEALTH, 2022, 10
  • [37] Evaluation and utilisation of privacy enhancing technologies-A data spaces perspective
    Aunon, J. M.
    Hurtado-Ramirez, D.
    Porras-Diaz, L.
    Irigoyen-Pena, B.
    Rahmiana, S.
    Al-Khazraji, Y.
    Soler-Garrido, J.
    Kotsev, A.
    DATA IN BRIEF, 2024, 55
  • [38] Archiving data from new survey technologies: enabling research with high-precision data while preserving participant privacy
    Gonder, Jeffrey
    Burton, Evan
    Murakami, Elaine
    TRANSPORT SURVEY METHODS: EMBRACING BEHAVIOURAL AND TECHNOLOGICAL CHANGES, 2015, 11 : 85 - 97
  • [39] A Survey on Blockchain and Artificial Intelligence Technologies for Enhancing Security and Privacy in Smart Environments
    Oumaima, Fadi
    Karim, Zkik
    Abdellatif, El Ghazi
    Mohammed, Boulmalf
    IEEE ACCESS, 2022, 10 : 93168 - 93186
  • [40] Survey of Privacy Enabling Strategies in IoT Networks
    Hellebrandt, Lukas
    Hujnak, Ondrej
    Hanacek, Petr
    Homoliak, Ivan
    PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (CSAI 2017), 2017, : 216 - 221