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Confidentially Computing DNA Matching Against Malicious Adversaries

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Smart Computing and Communication (SmartCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13828))

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Abstract

DNA is one of the most important information in every living thing. The DNA matching experiment is helpful for the study of paternity testing, species identification, gene mutation, suspect determination, and so on. How to study the DNA matching in the case of privacy protection has become the inevitable problems in the research of information security. The Hamming distance can reflect the similarity degree of two DNA sequences. The smaller the Hamming distance is, the more similar the two DNA sequences are. In this paper, the DNA sequence with \(l\) length is encoded with a 0–1 string with \(3l\) length, and the protocol of confidentially computing Hamming distance is designed, which calculated the matching degree of two DNA under the premise of protecting DNA privacy. In addition, in view of the criminal suspect DNA matching problem, we design a secure computation protocol against malicious adversaries using the zero-knowledge proof and the cut-choose method to prevent or find malicious behaviors, which can resist malicious attacks.

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Acknowledgement

This work is supported by National Natural Science Foundation of China: Big Data Analysis based on Software Defined Networking Architecture (No. 62177019; F0701); Inner Mongolia Natural Science Foundation (2021MS06006); 2022 Basic Scientific Research Project of Direct Universities of Inner Mongolia (20220101); 2022 Fund Project of Central Government Guiding Local Science and Technology Development (20220175); 2022 "Western Light" Talent Training Program "Western Young Scholars" Project; Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Open Project Fund (IMDBD202020);Baotou Kundulun District Science and Technology Plan Project (YF2020013); the 14th Five Year Plan of Education and Science of Inner Mongolia (NGJGH2021167); Inner Mongolia Science and Technology Major Project (2019ZD025); 2022 Inner Mongolia Postgraduate Education and Teaching Reform Project (20220213); the 2022 Ministry of Education Central and Western China Young Backbone Teachers and Domestic Visiting Scholars Program (20220393); Basic Scientific Research Business Fee Project of Beijing Municipal Commission of Education (110052972027); Research Startup Fund Project of North China University of Technology (110051360002).

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Authors and Affiliations

  1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China

    Xiaofen Tu, Xin Liu, Xiangyu Hu & Baoshan Li

  2. School of Computer Science, Shanxi Normal University, Xi’an, 710062, China

    Xin Liu

  3. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China

    Neal N. Xiong

  4. Department of Computer Science and Mathematics, Sul Ross State University, Alpine, TX, 79830, USA

    Neal N. Xiong

Authors
  1. Xiaofen Tu
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  2. Xin Liu
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  3. Xiangyu Hu
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  4. Baoshan Li
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  5. Neal N. Xiong
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Corresponding author

Correspondence to Xin Liu .

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Editors and Affiliations

  1. Dakota State University, Madison, SD, USA

    Meikang Qiu

  2. Fudan University, Shanghai, China

    Zhihui Lu

  3. Ibaraki University, Ibaraki, Japan

    Cheng Zhang

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Tu, X., Liu, X., Hu, X., Li, B., Xiong, N.N. (2023). Confidentially Computing DNA Matching Against Malicious Adversaries. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_16

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  • DOI: https://doi.org/10.1007/978-3-031-28124-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28123-5

  • Online ISBN: 978-3-031-28124-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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