Utilizing Homomorphic Encryption to Implement Secure and Private Medical Cloud Computing

Abstract

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.

Efficient Parallel Binary Operations on Homomorphic Encrypted Real Numbers

“…Literature specifically looking at finding min-max on encrypted number sets using FHE is limited. Kocabaş et al [22] uses the HElib library for min-max heart rate computation involving a logical comparison circuit examined later in Section 5.3. A log 2 N stage binary tree is used to repeatedly applying min-max on N ciphertexts packed with a vector of n-bit integers, resulting in an overall circuit depth of (log 2 n + 2) · log 2 N .…”

Visualization of Health Monitoring Data Acquired from Distributed Sensors for Multiple Patients

“…Random linear network coding along with lightweight homomorphic encryption is shown to be efficient to overcome malicious adversities via network analysis in multi-hop wireless networks [26], but fully homomorphic encryption is impractical [27]- [29].…”

Fog Computing as Privacy Enabler

“…While we agree that there are indeed some privacy (and security) risks in fog computing, we believe that these are largely technical challenges that can be surmounted with existing technology. On the other hand, fog computing provides invaluable privacy opportunities that are primarily of structural nature and can valuably complement existing, especially cryptographic techniques such as homomorphic encryption [7], secure multi-party computation [9], or mechanisms for differential privacy [10]. These structure-based opportunities can be used to significantly heighten data privacy, as compared to well-established cloud-centric models of data processing.…”