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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wu, C., Luo, C., et al.: A greedy deep learning method for medical disease analysis. IEEE Access 6, 20021–20030 (2018)
Gao, Y., Xiang, X., et al.: Human action monitoring for healthcare based on deep learning. IEEE Access 6, 52277–52285 (2018)
Fu, A., Zhang, X., et al.: VFL: a verifiable federated learning with privacy-preserving for big data in industrial IoT, IEEE Trans. Indust. Inform. 18, 3316–3326 (2020)
Xia, F., Hao, R., et al.: Adaptive GTS allocation in IEEE 802.15. 4 for real-time wireless sensor networks. J. Syst. Archit. 59(10), 1231–1242 (2013)
Kumar, P., Kumar, R., et al.: PPSF: a privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities. IEEE Trans. Netw. Sci. Eng. 8(3), 2326–2341 (2021)
Cheng, H., Xie, Z., et al.: Multi-step data prediction in wireless sensor networks based on one-dimensional CNN and bidirectional LSTM. IEEE Access 7, 117883–117896 (2019)
Yao, Y., Xiong, N., et al.: Privacy-preserving max/min query in two-tiered wireless sensor networks. Comput. Math. Appl. 65(9), 1318–1325 (2013)
Zhao, J., Huang, J., et al.: An effective exponential-based trust and reputation evaluation system in wireless sensor networks. IEEE Access 7, 33859–33869 (2019)
Wu, C., Ju, B., et al.: UAV autonomous target search based on deep reinforcement learning in complex disaster scene. IEEE Access 7, 117227–117245 (2019)
Zhang, W., Zhu, S., Tang, J., Xiong, N.: A novel trust management scheme based on Dempster–Shafer evidence theory for malicious nodes detection in wireless sensor networks, J. Supercomput. 74(4), 1779–1801 (2018)
Chen, Y., Zhou, L., et al.: KNN-BLOCK DBSCAN: fast clustering for large-scale data. IEEE Trans. Syst. Man Cybernet. Syst. 51(6), 3939–3953 (2019)
Huang, S., Zeng, Z., Ota, K., et al.: An intelligent collaboration trust interconnections system for mobile information control in ubiquitous 5G networks. IEEE Trans. Netw. Sci. Eng. 8(1), 347–365 (2020)
Qiu, M., Gai, K., Xiong, Z.: Privacy-preserving wireless communications using bipartite matching in social big data. Futur. Gener. Comput. Syst. 87, 772–781 (2018)
Qiu, H., Qiu, M., Lu, Z.: Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf. Fusion 55, 59–67 (2020)
Qiu, M., Zhang, L., Ming, Z., et al.: Security-aware optimization for ubiquitous computing systems with SEAT graph approach. J. Comput. Syst. Sci. 79(5), 518–529 (2013)
Ma, M.Y., Xu, Y., Liu, Z.: Privacy preserving Hamming distance computing problem of DNA sequences. J. Comput. Appl. 39(09), 2636–2640 (2019)
Liu, Z., Seo, H., Kim, H.et al.: Memory-efficient implementation of elliptic curve cryptography for the Internet-of-Things. IEEE Trans. Depend. Secur. Comput. 16(3), 521–529 (2019)
Goldreich, O.: The Fundamental of Crytography: Basic Application. Cambridge University Press, London (2004)
Qiu, M., Xue, C., Shao, Z., et al.: Efficient algorithm of energy minimization for heterogeneous wireless sensor network. In: IEEE EUC, pp. 25–34 (2006)
Qiu, M., Chen, Z., Ming, Z., Qin, X., Niu, J.: Energy-aware data allocation with hybrid memory for mobile cloud systems. IEEE Syst. J. 11(2), 813–822 (2014)
Niu, J., Gao, Y., Qiu, M., Ming, Z.: Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. J. Parallel Distrib. Comput. 72(12), 1565–1575 (2012)
Gai, K., Qiu, M., Elnagdy, S.: A novel secure big data cyber incident analytics framework for cloud-based cybersecurity insurance. In: IEEE BigDataSecurity (2016)
Qiu, H., Zheng, Q., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. Intell. Transp. Syst. 22, 4560–4569 (2020)
Qiu, H., Dong, T., Zhang, T., et al.: Adversarial attacks against network intrusion detection in IoT systems. IEEE Internet Things J. 8(13), 10327–10335 (2020)
Li, S., Wang, W., Du, R.: Protocol for millionaires’ problem in malicious models. Sci. Sin. Inform. 51(1), 75 (2021)
Goldwasser, S., Micali, S., et al.: The knowledge complexity of interactive proof systems. SIAM J. Comput. 18(1), 186–208 (1989)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-28124-2_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28123-5
Online ISBN: 978-3-031-28124-2
eBook Packages: Computer ScienceComputer Science (R0)