Abstract
Implementing cloud computing in medical fields would undoubtedly help achieving the best health outcomes. Obviously, this model simultaneously improves the quality of clinical decisions through advanced IT services, and lowers operating expenses. Indeed, cloud services are usually characterized by remarkable features such as cost-efficient, availability and easy exploitation. In particular, image processing using cloud has presently gained an expanding interest. Since cloud is an evolving technology, the usage of this new paradigm in such a sensitive domain requires filling the potential gaps related particularly to data privacy and security. In order to maintain data privacy, several security measures, which are based on different techniques and countermeasures, are developed, especially Service-Oriented Architecture (SOA), Secure Multi-party Computation (SMC), homomorphic cryptosystems and Secret Share Scheme (SSS). Although these existing methods are generally a promising approach, applying them to process medical data has negative effects on performance and privacy. In fact, they are inadequate to deal with a very high volume data effectively because they are originally designed for individual pixel values and text data. The main contribution of this paper is to provide a novel solution based on three-level architecture and clustering technique to secure Software-as-a-Service (SaaS) model. In this case, we use K-means clustering method to break up the secret image into a fixed number of regions, thereby processing each portion in a distinct node. This approach is meant to eliminate or reduce the risk of the potential disclosure of sensitive data. Further, we use a trusted component acting as an interface between consumers and cloud providers for minimizing security risks and cloud security threats. Specifically, this architecture is a highly efficient solution to mitigate anonymity and unlinkability issues in cloud environment. Simulation results have demonstrated the utility of the proposed methodology in ensuring the safety of medical data when using cloud services.
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Marwan, M., Kartit, A., Ouahmane, H. (2019). A New Medical Image Processing Approach for the Security of Cloud Services. In: Mizera-Pietraszko, J., Pichappan, P., Mohamed, L. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2017. Advances in Intelligent Systems and Computing, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-91337-7_34
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