Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

Feng Xu, Mingming Su and Yating Hou
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Feng Xu: Nanjing University of Aeronautics and Astronautics, Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China
Mingming Su: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Yating Hou: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

International Journal of Information Security and Privacy (IJISP), 2019, vol. 13, issue 2, 104-119

Abstract: The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

Date: 2019
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