相关论文: A Determination Scheme for Quasi-Identifiers Using…
Quasi-identifier-based deidentification techniques (QI-deidentification) are widely used in practice, including $k$-anonymity, $\ell$-diversity, and $t$-closeness. We present three new attacks on QI-deidentification: two theoretical attacks…
The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream utility. This paper presents DeID-Clinic, a multi-layered…
Free-text clinical notes detail all aspects of patient care and have great potential to facilitate quality improvement and assurance initiatives as well as advance clinical research. However, concerns about patient privacy and…
Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the…
Sharing protected health information (PHI) is critical for furthering biomedical research. Before data can be distributed, practitioners often perform deidentification to remove any PHI contained in the text. Contemporary deidentification…
The robust development of Electronic Health Records (EHRs) causes a significant growth in sharing EHRs for clinical research. However, such a sharing makes it difficult to protect patient's privacy. A number of automated de-identification…
The concept of k-anonymity, used in the recent literature to formally evaluate the privacy preservation of published tables, was introduced based on the notion of quasi-identifiers (or QI for short). The process of obtaining k-anonymity for…
Objective: The use of routinely-acquired medical data for research purposes requires the protection of patient confidentiality via data anonymisation. The objective of this work is to calculate the risk of re-identification arising from a…
Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be…
Sharing clinical research data is key for increasing the pace of medical discoveries that improve human health. However, concern about study participants' privacy, confidentiality, and safety is a major factor that deters researchers from…
Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic health records. This process, known as de-identification, is…
Clinical notes often describe the most important aspects of a patient's physiology and are therefore critical to medical research. However, these notes are typically inaccessible to researchers without prior removal of sensitive protected…
Background: More than half (57%) of pharma clinical research spend is in support of clinical trials. One reason is that Electronic Health Record (EHR) systems and HIPAA privacy rules often limit how broadly patient information can be…
Unstructured textual data are at the heart of health systems: liaison letters between doctors, operating reports, coding of procedures according to the ICD-10 standard, etc. The details included in these documents make it possible to get to…
Quantum Artificial Intelligence (QAI), the integration of Artificial Intelligence (AI) and Quantum Computing (QC), promises transformative advances, including AI-enabled quantum cryptography and quantum-resistant encryption protocols.…
High-quality real-world data (RWD) is essential for healthcare but must be transformed to comply with the General Data Protection Regulation (GDPR). GDPRs broad definitions of quasi-identifiers (QIDs) and sensitive attributes (SAs)…
Over the last decade, proliferation of various online platforms and their increasing adoption by billions of users have heightened the privacy risk of a user enormously. In fact, security researchers have shown that sparse microdata…
For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data…
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data…
Medical data employed in research frequently comprises sensitive patient health information (PHI), which is subject to rigorous legal frameworks such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and…