This paper is in the following e-collection/theme issue:

Ethics, Privacy, and Legal Issues (456)

Published on in Vol 1, No 2 (2012): Jul-Dec

An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods

An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods

An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods

Authors of this article:

Sunyong Yoo1 ;   Moonshik Shin1 ;   Doheon Lee1
Article Authors Cited by (6) Tweetations (1) Metrics

Journals

  1. Somolinos R, Munoz A, Hernando M, Pascual M, Caceres J, Sanchez-de-Madariaga R, Fragua J, Serrano P, Salvador C. Service for the Pseudonymization of Electronic Healthcare Records Based on ISO/EN 13606 for the Secondary Use of Information. IEEE Journal of Biomedical and Health Informatics 2015;19(6):1937 View
  2. Arellano A, Dai W, Wang S, Jiang X, Ohno-Machado L. Privacy Policy and Technology in Biomedical Data Science. Annual Review of Biomedical Data Science 2018;1(1):115 View
  3. Erdemir E, Dragotti P, Gunduz D. Privacy-Aware Time-Series Data Sharing With Deep Reinforcement Learning. IEEE Transactions on Information Forensics and Security 2021;16:389 View
  4. Sepas A, Bangash A, Alraoui O, El Emam K, El-Hussuna A. Algorithms to anonymize structured medical and healthcare data: A systematic review. Frontiers in Bioinformatics 2022;2 View
  5. Erdemir E, Dragotti P, Gündüz D. Active Privacy-Utility Trade-Off Against Inference in Time-Series Data Sharing. IEEE Journal on Selected Areas in Information Theory 2023;4:159 View