According to many scientists and clinicians, genomics is the “next big thing” in the field of medicine. On one hand, decreasing costs in genome sequencing has been paving the way to better preventive and personalized medicine. On the other hand, genomic data also raises serious privacy concerns, as it is the ultimate identifier of an individual and it contains privacy-sensitive data (e.g., disease predispositions, ancestry information). Thus, it is necessary to find ways of using genomic data without abusing the genomic privacy of individuals. To get a more comprehensive medical assessment, genomic information must be combined with other clinical and environmental data (such as demographic information, family history, disease history, laboratory test results, etc.) that are also privacy-sensitive (e.g., HIV status of an individual) and need to be treated as such. Focusing on disease risk tests, in this paper, we propose a privacy-preserving system for storing and processing genomic, clinical, and environmental data by using homomorphic encryption and privacy-preserving integer comparison. We implement the proposed system using real patient data and reliable disease risk factors. In particular, we use 23 genetic and 14 clinical and environmental risk factors to compute the risk of coronary artery disease in a privacypreserving way. Finally, we show the practicality of the proposed system via a complexity evaluation.

Online social networks (OSNs) have formed virtual social networks where people meet and share information. Among all shared information, health related information (HRI) has received considerable attention from researchers and individual users. While considered beneficial, sharing HRI, which is personal in nature, comes with its privacy drawback. Privacy is a process of boundary regulation that is related to the individual and her perception of the surrounding environment. As a result, the subjective privacy risk perceptions associated with sharing HRI in OSN have driven people to adopt different types of behaviour, both in terms of HRI sharing and privacy risk mitigation.

Through an online survey, we examined factors that affect users’ perceived privacy risks along with their risk-mitigating behaviour, when it comes to sharing HRI in OSNs. The results suggest that the majority (over 95%) of participants share some HRI, with the “type” and the “recipient” of the shared HRI being the key factors that affect the perceived privacy risk and the risk-mitigating behavioural responses.

Electronic health record (EHR) systems must log all transactions with protected health information (PHI) to deter unauthorized behavior and prevent users from denying that they created, read, updated, or deleted PHI. However, a plethora of standardization and governing organizations publish documentation (such as standards, suggestions, and requirements) to outline transactions that should be logged and the data that should be captured for each log entry. The objective of this research is to guide the design of electronic health record systems by cataloging suggested information that should be captured by logging mechanisms from both healthcare and non-healthcare documentation. In this paper, we focus on three types of information: data transactions, security events, and log entry content. We collect a set of ten healthcare-related and six non- healthcare related documents that contain specifications for logging mechanisms. From these 16 sources, we catalog 11 data transactions, 77 security events, and 22 data elements for log entry content. Overall, we identify 14 security events and 2 data elements for log entry content that are not explicitly addressed by healthcare documents. We found that developers must consider 13 of the 16 documents to extract 100% of the security events and log entry content cataloged.

Over the past decade, various regulations have been proposed and promulgated to support the auditing of accesses to Electronic Medical Record (EMRs). Current tools to support this process can improve their use of statistical and machine learning techniques and auditor interfaces. We sketch requirements and design for an Extensible Medical Open Audit Toolkit (EMOAT) to enable progress in these areas. A key objective is to provide interfaces that support three types of stakeholders: (1) expert analysts, (2) privacy and security ocers, and (3) patients. Our system design provides for an application programming interface that enables ocers and patients to access both simple and complex analytic systems. We illustrate how EMOAT has been adapted to support certain audit functionalities with data from the EMR systems of several large hospital systems.

Patients are increasingly reliant on implantable medical device systems today. For patients with diabetes, an implantable insulin pump system can greatly improve their quality of life. As with any device, these devices can and do suffer from software and hardware issues, often reported as a safety event. For a forensic investigator, a safety event is indistinguishable from a potential security event. In this paper, we propose a new sensor system that can be transparently integrated into existing and future electronic diabetes therapy systems while providing additional forensic data to help distinguish between safety and security events.