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Efficient Fragile Privacy-Preserving Audio Watermarking Using Homomorphic Encryption

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Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13340))

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Abstract

Audio has become increasingly important in modern social communication and mobile Internet. In cloud computing, practical cloud-based applications should achieve high computation efficiency and secure data protection simultaneously. In this paper, we study the application of an efficient encrypted audio fragile watermarking using homomorphic encryption and the batching technique SIMD in cloud computing. We firstly implement the algorithm of Haar wavelet transform in the encrypted domain (BS-HWT) using the batching technique SIMD with the fully homomorphic encryption scheme CKKS. By performing BS-HWT on the encrypted audio, we transform the encrypted audio signal into the encrypted frequency-domain coefficients. The encrypted fragile watermark is then embedded into the encrypted discrete wavelet transform domain. Our experimental results show that the proposed watermarking scheme is highly efficient and sensitive to common audio attacks. We also present the proposed scheme has a good ability of tamper localization.

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Acknowledgement

This work was supported in part by the Natural Science Foundation of Guangdong (2019A1515010746, 2022A1515011897), in part by the Science and Technology Projects in Guangzhou (202102080354), in part by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness (HNTS2022023).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Guangdong Key Laboratory of Information Security Technology, Sun Yat-Sen University, Guangzhou, 510006, China

    Ruopan Lai, Xiongjie Fang, Peijia Zheng, Hongmei Liu, Wei Lu & Weiqi Luo

  2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Peijia Zheng

Authors
  1. Ruopan Lai
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  2. Xiongjie Fang
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  3. Peijia Zheng
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  4. Hongmei Liu
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  5. Wei Lu
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  6. Weiqi Luo
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Corresponding authors

Correspondence to Peijia Zheng or Hongmei Liu .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Lai, R., Fang, X., Zheng, P., Liu, H., Lu, W., Luo, W. (2022). Efficient Fragile Privacy-Preserving Audio Watermarking Using Homomorphic Encryption. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_30

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  • DOI: https://doi.org/10.1007/978-3-031-06791-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06790-7

  • Online ISBN: 978-3-031-06791-4

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