CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

by Twinkle Patel, Kiran Amin
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 14
Year of Publication: 2024
Authors: Twinkle Patel, Kiran Amin

Twinkle Patel, Kiran Amin . Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity. International Journal of Computer Applications. 186, 14 ( Mar 2024), 1-8. DOI=10.5120/ijca2024923491

@article{ 10.5120/ijca2024923491,
author = { Twinkle Patel, Kiran Amin },
title = { Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2024 },
volume = { 186 },
number = { 14 },
month = { Mar },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2024923491 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-03-27T00:44:45+05:30
%A Twinkle Patel
%A Kiran Amin
%T Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 14
%P 1-8
%D 2024
%I Foundation of Computer Science (FCS), NY, USA

In recent years, the proliferation of extensive personal data has sparked concerns over privacy infringement and data misuse. This data encompasses various facets of individuals’ lives, including shopping patterns, criminal records, medical histories, and credit profiles. While the exchange and analysis of such data offer substantial benefits for businesses and governments, privacy apprehensions can hinder data sharing. To address these concerns, privacy-preserving data publishing techniques have emerged. Our approach focuses on p-sensitive kanonymity, a method that extends traditional k-anonymity to consider multiple sensitive attributes simultaneously. By anonymizing data in this manner, individuals’ identities are protected, mitigating the risk of re-identification while still enabling meaningful analysis. Our proposed approach aims to strike a balance between data utility and privacy protection, facilitating informed decision-making without compromising individual privacy rights.

  1. Mansoor Ali, Faisal Naeem, Muhammad Tariq, and Georges Kaddoum. Federated learning for privacy preservation in smart healthcare systems: A comprehensive survey. IEEE journal of biomedical and health informatics, 27(2):778–789, 2022.
  2. Adeel Anjum, Kim-Kwang Raymond Choo, Abid Khan, Asma Haroon, Sangeen Khan, Samee U Khan, Naveed Ahmad, Basit Raza, et al. An efficient privacy mechanism for electronic health records. computers & security, 72:196–211, 2018.
  3. Vanessa Ayala-Rivera, Patrick McDonagh, Thomas Cerqueus, Liam Murphy, et al. A systematic comparison and evaluation of k-anonymization algorithms for practitioners. Trans. Data Priv., 7(3):337–370, 2014.
  4. Mahawaga Arachchige Pathum Chamikara, Peter Bertok, Dongxi Liu, Seyit Camtepe, and Ibrahim Khalil. Efficient privacy preservation of big data for accurate data mining. Information Sciences, 527:420–443, 2020.
  5. Razaullah Khan, Xiaofeng Tao, Adeel Anjum, Tehsin Kanwal, Saif Ur Rehman Malik, Abid Khan,Waheed Ur Rehman, and Carsten Maple. -sensitive k-anonymity: An anonymization model for iot based electronic health records. Electronics, 9(5):716, 2020.
  6. Jun Liu, Yuan Tian, Yu Zhou, Yang Xiao, and Nirwan Ansari. Privacy preserving distributed data mining based on secure multi-party computation. Computer Communications, 153:208–216, 2020.
  7. Waranya Mahanan, W Art Chaovalitwongse, and Juggapong Natwichai. Data privacy preservation algorithm with kanonymity. World Wide Web, 24:1551–1561, 2021.
  8. David J Martin, Daniel Kifer, Ashwin Machanavajjhala, Johannes Gehrke, and Joseph Y Halpern. Worst-case background knowledge for privacy-preserving data publishing. In 2007 IEEE 23rd International Conference on Data Engineering, pages 126–135. IEEE, 2006.
  9. Amani Mahagoub Omer and Mohd Murtadha Bin Mohamad. Simple and effective method for selecting quasi-identifier. Journal of Theoretical and Applied Information Technology, 89(2):512, 2016.
  10. Mohammad Hosein Panahi Rizi and Seyed Amin Hosseini Seno. A systematic review of technologies and solutions to improve security and privacy protection of citizens in the smart city. Internet of Things, 20:100584, 2022.
  11. Jinyan Wang, Kai Du, Xudong Luo, and Xianxian Li. Two privacy-preserving approaches for data publishing with identity reservation. Knowledge and Information Systems, 60:1039–1080, 2019.
  12. Nan Wang, Haina Song, Tao Luo, Jinkao Sun, and Jianfeng Li. Enhanced p-sensitive k-anonymity models for achieving better privacy. In 2020 IEEE/CIC International Conference on Communications in China (ICCC), pages 148–153. IEEE, 2020.
  13. Siran Yin, Leiming Yan, Yuanmin Shi, Yaoyang Hou, and Yunhong Zhang. A survey on recent advances in privacy preserving deep learning. Journal of Information Hiding and Privacy Protection, 2(4):175, 2020.
  14. Lei Yu, Ling Liu, Calton Pu, Mehmet Emre Gursoy, and Stacey Truex. Differentially private model publishing for deep learning. In 2019 IEEE symposium on security and privacy (SP), pages 332–349. IEEE, 2019.
Index Terms

Computer Science
Information Sciences


Privacy preservation K Anonymity p-sensitive P+ Sensitive