The past few decades have witnessed incredible advances in human health care, owing to the invention of devices such as MRI scanners, which allow physicians to monitor personal health in more detail than was ever previously possible. Such advances have drastically improved diagnostic quality and patient health care. Central to this incredible progress was the uncanny ability of technologists and academics to invent ever more useful tools to help physicians, be it the X-ray machine, CT, or MRI scanner. Whereas the aforementioned past-decades' tools aimed at acquiring personal data, the advent of the Internet-of-Things, vast computational power available in the cloud, and new data analytics algorithms will completely change the way we acquire and process medical data to improve health care going forward. In this paper, we conduct a quantitative feasibility study of a Digital Health (D-Health) system that is aimed at acquiring and processing health data using the emerging Internet-of-Everything paradigm. We specifically investigate the technological feasibility of communication, software, and data privacy aspects.

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.

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.

In this paper, we design, implement, and realize a cryptographic unit (CU) that can easily be integrated to any reduced instruction set computing (RISC)-type processor for the safe and efficient execution of cryptographic algorithms. Design of the CU takes a novel approach in the execution of cryptographic algorithms when compared with cryptographic accelerators and architectural enhancements. Although it is integrated to a pipeline of an embedded RISC processor, it is partially an autonomous unit with its own resources, which is analogous to the floating point unit in this sense. It provides new instructions to accelerate cryptographic algorithms, and its associated cost in terms of area is acceptable and justified by the improvement in the performance and efficiency. The CU can also be instrumental in protecting the cryptographic computation against active and passive attacks and other malicious processes running simultaneously. We demonstrate that the execution of Advanced Encryption Standart (AES) encryption can be performed inside the CU, which prevents secret and/or sensitive information from leaving the CU during the cryptographic computation.

Current financial and regulatory pressure has provided strong incentives to institute better disease prevention, improved patient monitoring, and push U.S. healthcare into the digital era. This transition requires that data privacy be ensured for digital health data in three distinct phases: I. acquisition, II. storage, and III. computation. Each phase comes with unique challenges in terms of proper implementation and privacy. While the privacy of the data can be ensured with existing AES encryption techniques in phases I (acquisition) and II (storage), to enable healthcare organizations to take advantage of cloud computing using resources such as Amazon Web Services, phase III (computation) must also enable the privacy of the data. Currently, there exists no system to enable direct computation in the cloud while assuring data privacy. Fully Homomorphic Encryption (FHE) is an emerging cryptographic technique to permit computation on encrypted data directly in the cloud without the need to bring the data back to the computational node. However, this promising technique comes with significant performance- and storage-related challenges. While it will take more years before true FHE is mainstream, we provide a feasibility study for its application to a simple longterm patient ECG-data monitoring system.

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 chip-multiprocessors, and thus bring into doubt the centrality of packet switching in future on-chip interconnect.