A Review of Anonymization for Healthcare Data
원문 초록
Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, reduced cost, and it drives innovative solutions within the healthcare sector. However, health data is highly sensitive and subject to regulations such as the General Data Protection Regulation (GDPR), which aims to ensure patient's privacy. Anonymization or removal of patient identifiable information, though the most conventional way, is the first important step to adhere to the regulations and incorporate privacy concerns. In this paper, we review the existing anonymization techniques and their applicability to various types (relational and graph-based) of health data. Besides, we provide an overview of possible attacks on anonymized data. We illustrate via a reconstruction attack that anonymization though necessary, is not sufficient to address patient privacy and discuss methods for protecting against such attacks. Finally, we discuss tools that can be used to achieve anonymization.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
Face Reconstruction Transfer Attack as Out-of-Distribution Generalization
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
Trained Random Forests Completely Reveal your Dataset
토론
이 논문과 연결된 포럼 글이 아직 없습니다.