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Model Parameter Estimation and Inference on Encrypted Domain: Application to Noise Reduction in Encrypted Images

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Information Security Applications (WISA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10763))

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Abstract

One of the major issues in security is how to protect the privacy of multimedia big data on cloud systems. Homomorphic Encryption (HE) is increasingly regarded as a way to maintain user privacy on the untrusted cloud. However, HE is not widely used in machine learning and signal processing communities because the HE libraries are currently supporting only simple operations like integer addition and multiplication. It is known that division and other advanced operations cannot feasibly be designed and implemented in HE libraries. Therefore, we propose a novel approach to building a practical matrix inversion operation using approximation theory on HE. The approximated inversion operation is applied to reduce unwanted noise on encrypted images. Our research also suggests the efficient computation techniques for encrypted matrices. We conduct the experiment with real binary images using open source library of HE.

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Acknowledgments

This research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the IITP (Institute for Information & communications Technology Promotion) support program (2017-0-00545).

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Authors and Affiliations

  1. School of Information Security, Korea University, Seoul, South Korea

    Saetbyeol Lee & Jiwon Yoon

Authors
  1. Saetbyeol Lee
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  2. Jiwon Yoon
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Corresponding author

Correspondence to Jiwon Yoon .

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Editors and Affiliations

  1. KAIST, Daejeon, Korea (Republic of)

    Brent ByungHoon Kang

  2. Georgia Institute of Technology, Atlanta, Georgia, USA

    Taesoo Kim

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Lee, S., Yoon, J. (2018). Model Parameter Estimation and Inference on Encrypted Domain: Application to Noise Reduction in Encrypted Images. In: Kang, B., Kim, T. (eds) Information Security Applications. WISA 2017. Lecture Notes in Computer Science(), vol 10763. Springer, Cham. https://doi.org/10.1007/978-3-319-93563-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-93563-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93562-1

  • Online ISBN: 978-3-319-93563-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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