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A Cloud Based Solution for Collaborative and Secure Sharing of Medical Data
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Author(s): Mbarek Marwan (Chouaib Doukkali University, Morocco), Ali Kartit (Chouaib Doukkali University, Morocco)and Hassan Ouahmane (LTI Laboratory, ENSA, Chouaib Doukkali University, El Jadida, Morocco)
Copyright: 2019
Pages: 20
Source title:
Cloud Security: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8176-5.ch077
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Abstract
Healthcare sector is under pressure to reduce costs while delivering high quality of care services. This situation requires that clinical staff, equipment and IT tools to be used more equitably, judiciously and efficiently. In this sense, collaborative systems have the ability to provide opportunities for healthcare organizations to share resources and create a collaborative working environment. The lack of interoperability between dissimilar systems and operating costs are the major obstacle to the implementation of this concept. Fortunately, cloud computing has great potential for addressing interoperability issues and significantly reducing operating costs. Since the laws and regulations prohibit the disclosure of health information, it is necessary to carry out a comprehensive study on security and privacy issues in cloud computing. Based on their analysis of these constraints, the authors propose a simple and efficient method that enables secure collaboration between healthcare institutions. For this reason, they propose Secure Multi-party Computation (SMC) protocols to ensure compliance with data protection legislation. Specifically, the authors use Paillier scheme to protect medical data against unauthorized usage when outsourcing computations to a public cloud. Another useful feature of this algorithm is the possibility to perform arithmetic operations over encrypted data without access to the original data. In fact, the Paillier algorithm is an efficient homomorphic encryption that supports addition operations on ciphertexts. Based on the simulation results, the proposed framework helps healthcare organizations to successfully evaluate a public function directly on encrypted data without revealing their private inputs. Consequently, the proposed collaborative application ensures privacy of medical data while completing a task.
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