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.
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.
Personal health monitoring tools, such as commercially available wireless ECG patches, can significantly reduce healthcare costs by allowing patient monitoring outside the healthcare organizations. These tools transmit the acquired medical data into the cloud, which could provide an invaluable diagnosis tool for healthcare professionals. Despite the potential of such systems to revolutionize the medical field, the adoption of medical cloud computing in general has been slow due to the strict privacy regulations on patient health information. We present a novel medical cloud computing approach that eliminates privacy concerns associated with the cloud provider. Our approach capitalizes on Fully Homomorphic Encryption (FHE), which enables computations on private health information without actually observing the underlying data. For a feasibility study, we present a working implementation of a long-term cardiac health monitoring application using a well-established open source FHE library.
With a large number of commercially-available noninvasive health monitoring sensors today, remote health monitoring of patients in their homes is becoming widespread. In remote health monitoring, acquired sensory data is transferred into a private or public cloud for storage and processing. While simple encryption techniques can assure data privacy in the case of private clouds, ensuring data privacy becomes a lot more challenging when a public cloud (e.g., Amazon EC2) is used to store and process data. We present an approach that eliminates data privacy concerns in the public cloud scenario, by utilizing an emerging encryption technique called Fully Homomorphic Encryption (FHE). The ability of FHE to allow computations without actually observing the data itself makes it an attractive option for certain medical applications. In this paper, we use cardiac health monitoring for our feasibility assessment and demonstrate the advantages and challenges of our approach by utilizing a well-established FHE library called HElib.
Main-stream general-purpose microprocessors require a collection of high-performance interconnects to supply the necessary data movement. The trend of continued increase in core count has prompted designs of packet-switched network as a scalable solution for future-generation chips. However, the cost of scalability can be significant and especially hard to justify for smaller-scale chips. In contrast, a circuit-switched bus using transmission lines and corresponding circuits offers lower latencies and much lower energy costs for smaller-scale chips, making it a better choice than a full-blown network-on-chip (NoC) architecture. However, shared-medium designs are perceived as only a niche solution for small-to medium-scale chips.In this paper, we show that there are many low-cost mechanisms to enhance the effective throughput of a bus architecture. When a handful of highly cost-effective techniques are applied, the performance advantage of even the most idealistically configured NoCs becomes vanishingly small. We find transmission line-based buses to be a more compelling interconnect even for large-scale chipmultiprocessors, and thus bring into doubt the centrality of packet switching in future on-chip interconnect.
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