A Cloud-based Framework to Secure Medical Image Processing
Keywords:Cloud computing, medical image processing, security, segmentation, genetic algorithms
In the last few years, advanced software for processing medical images has gained a great interest in modern medicine. In fact, it provides valuable clinical information, and hence, can significantly improve diagnosis and treatment. Nevertheless, implementing these imaging tools often requires an important capital budget in both IT applications and hardware. This solution can unfortunately cause a dramatic increase in operational expenses and medical costs. To mitigate this problem, medical providers are shifting their interest onto using cloud computing, particularly the Software-as-a-Service (SaaS) model, instead of in-house data centres. In this case, healthcare professionals rely on remote applications delivered by an external provider to process patients’ digital records. Interestingly, in this paradigm, consumers are billed based on software utilization. Besides, cloud computing promises to offer a better Quality of Service (QoS), including availability, elasticity, trust, response time, security assurance, etc. Regardless of its significant financial benefits, the transition to the cloud environment gives rise to security and privacy problems, especially in the healthcare domain. Recently, various security measures and mechanisms have been suggested to overcome these challenges and accelerate the adoption of cloud computing services. In this regard, numerous cryptographic techniques are used to safely process digital medical images, techniques that make use of homomorphic cryptosystems, Secret Sharing Schemes (SSS), Service-Oriented Architecture (SOA) and Secure Multi-party Computation (SMC). Although these methods are deemed very promising, they can negatively impact the performance of cloud services. Most precisely, they are not yet mature enough to satisfy Service Level Agreement (SLA) constraints. The main contribution of this study consists of presenting a novel approach to secure cloud-based medical image processing. This proposed solution combines segmentation techniques and genetic algorithms together in one model. Based on this method, we rely on pixel intensity and entropy measurements to split an image into a number of regions to maintain data privacy. The principal reason for using genetic algorithms is to optimize the number of generated regions. Furthermore, we opt for an architecture based on multi-cloud systems and CloudSec module to enable distributed data processing and prevent accidental disclosure of medical information. As shown in the simulation results, the proposal is an appropriate framework for fuelling the integration of cloud applications in the healthcare sector. In particular, it enables clients to securely use remote image processing tools.
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