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Browsing by Author Yan, H.

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  • A mathematical model of tortuosity in soil considering particle arrangement

    • Authors: Zhang, S.;  Advisor: -;  Participants: Yan, H.; Teng, J.; Sheng, D. (2020)

  • Tortuosity is an important parameter for studying the permeability of soil. Existing studies of soil tortuosity are usually of empirical nature and attempt to relate tortuosity to soil porosity alone. By assuming a laminar flow through the pores of two‐dimensional square solid particles, we present a mathematical model for calculating soil tortuosity under different particle arrangements. The effect of the randomness of the particle arrangement on the tortuosity is evaluated, which generates the variation range of the tortuosity. The proposed model provides the upper and lower bounds of the tortuosity, while existing empirical models tend to fall within these bounds. The consistency b...
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  • An Efficient Outsourced Privacy Preserving Machine Learning Scheme With Public Verifiability

    • Authors: Hassan, A.;  Advisor: -;  Participants: Hamza, R.; Yan, H.; Li, P. (2019)

  • Cloud computing has been widely applied in numerous applications for storage and data analytics tasks. However, cloud servers engaged through a third party cannot be fully trusted by multiple data users. Thus, security and privacy concerns become the main obstructions to use machine learning services, especially with multiple data providers. Additionally, some recent outsourcing machine learning schemes have been proposed in order to preserve the privacy of data providers. Yet, these schemes cannot satisfy the property of public veri ability. In this paper, we present an ef cient privacy-preserving machine learning scheme for multiple data providers. The proposed scheme allows all par...