SOAD: Securing Oncology EMR by Anonymizing DICOM Images

Shahbaz, Sidra; Mahmood, Asiah; Anwar, Zahid

doi:10.1109/fit.2013.30

Cited by 4 publications

References 5 publications

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SOK: Evaluating Privacy and Security Vulnerabilities of Patients’ Data in Healthcare

Tazi

Dykstra²,

Rajivan

et al. 2022

Lecture Notes in Computer Science

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Sensitive information is intrinsically tied to interactions in healthcare, and its protection is of paramount importance for achieving high-quality patient outcomes. Research in healthcare privacy and security is predominantly focused on understanding the factors that increase the susceptibility of users to privacy and security breaches. To understand further, we systematically review 26 research papers in this domain to explore the existing user studies in healthcare privacy and security. Following the review, we conducted a card-sorting exercise, allowing us to identify 12 themes integral to this subject such as "Data Sharing," "Risk Awareness," and "Privacy." Further to the identification of these themes, we performed an in-depth analysis of the 26 research papers report on the insights into the discourse within the research community about healthcare privacy and security, particularly from the user perspective. MotivationSecurity and privacy integration in the healthcare domain is essential to protect patients' data [12], considering medical records include sensitive health and personal information. The healthcare industry is often a prime target for cybercriminals considering that these data sets could contain a plethora of sensitive information such as social security numbers, birth dates, employment information, emergency contacts, and insurance and billing data; these data are also notoriously difficult to monitor or safeguard after a breach [23]. Furthermore, healthcare data are lucrative on the black market. Sahi et al. noted

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SOK: Evaluating Privacy and Security Vulnerabilities of Patients’ Data in Healthcare

Tazi

Dykstra²,

Rajivan

et al. 2022

Lecture Notes in Computer Science

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A machine learning methodology for medical imaging anonymization

Monteiro

Costa

Oliveira

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Privacy protection is a major requirement for the complete success of EHR systems, becoming even more critical in collaborative scenarios, where data is shared among institutions and practitioners. While textual data can be easily de-identified, patient data in medical images implies a more elaborate approach. In this work we present a solution for sensitive word identification in medical images based on a combination of two machine-learning models, achieving a F1-score of 0.94. Three experts evaluated the system performance. They analyzed the output of the present methodology and categorized the studies in three groups: studies that had their sensitive words removed (true positive), studies with complete patient identity (false negative) and studies with mistakenly removed data (false positive). The experts were unanimous regarding the relevance of the present tool in collaborative medical environments, as it may improve the exchange of anonymized patient data between institutions.

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