Emerging Security Mechanisms for Medical Cyber Physical Systems.

The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.

Cloud-based privacy-preserving remote ECG monitoring and surveillance.

BACKGROUND: The number of technical solutions for monitoring patients in their daily activities is expected to increase significantly in the near future. Blood pressure, heart rate, temperature, BMI, oxygen saturation, and electrolytes are few of the physiologic factors that will soon be available to patients and their physicians almost continuously. The availability and transfer of this information from the patient to the health provider raises privacy concerns. Moreover, current data encryption approaches expose patient data during processing, therefore restricting their utility in applications requiring data analysis. METHODS: We propose a system that couples health monitoring techniques with analytic methods to permit the extraction of relevant information from patient data without compromising privacy. This proposal is based on the concept of fully homomorphic encryption (FHE). Since this technique is known to be resource-heavy, we develop a proof-of-concept to assess its practicality. Results are presented from our prototype system, which mimics live QT monitoring and detection of drug-induced QT prolongation. RESULTS: Transferring FHE-encrypted QT and RR samples requires about 2 Mbps of network bandwidth per patient. Comparing FHE-encrypted values--for example, comparing QTc to a given threshold-runs quickly enough on modest hardware to alert the doctor of important results in real-time. CONCLUSIONS: We demonstrate that FHE could be used to securely transfer and analyze ambulatory health monitoring data. We present a unique concept that could represent a disruptive type of technology with broad applications to multiple monitoring devices. Future work will focus on performance optimizations to accelerate expansion to these other applications.