Efficient Fragile Privacy-Preserving Audio Watermarking Using Homomorphic Encryption

被引:2
|
作者
Lai, Ruopan [1 ]
Fang, Xiongjie [1 ]
Zheng, Peijia [1 ,2 ]
Liu, Hongmei [1 ]
Lu, Wei [1 ]
Luo, Weiqi [1 ]
机构
[1] Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangdong Key Lab Informat Secur Technol, Guangzhou 510006, Peoples R China
[2] Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China
关键词
Audio watermarking; Secure watermarking; Discrete wavelet transform; Fully homomorphic encryption; Signal processing in the encrypted domain; Cloud computing; SEARCH; SECURE;
D O I
10.1007/978-3-031-06791-4_30
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:373 / 385
页数:13
相关论文
共 50 条
  • [1] Efficient homomorphic encryption framework for privacy-preserving regression
    Byun, Junyoung
    Park, Saerom
    Choi, Yujin
    Lee, Jaewook
    APPLIED INTELLIGENCE, 2023, 53 (09) : 10114 - 10129
  • [2] Efficient homomorphic encryption framework for privacy-preserving regression
    Junyoung Byun
    Saerom Park
    Yujin Choi
    Jaewook Lee
    Applied Intelligence, 2023, 53 : 10114 - 10129
  • [3] Privacy-Preserving Decentralized Optimization Using Homomorphic Encryption
    Huo, Xiang
    Liu, Mingxi
    IFAC PAPERSONLINE, 2020, 53 (05): : 630 - 633
  • [4] Privacy-Preserving Federated Learning Using Homomorphic Encryption
    Park, Jaehyoung
    Lim, Hyuk
    APPLIED SCIENCES-BASEL, 2022, 12 (02):
  • [5] Privacy-preserving Surveillance Methods using Homomorphic Encryption
    Bowditch, William
    Abramson, Will
    Buchanan, William J.
    Pitropakis, Nikolaos
    Hall, Adam J.
    ICISSP: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY, 2020, : 240 - 248
  • [6] Privacy-Preserving Biometric Matching Using Homomorphic Encryption
    Pradel, Gaetan
    Mitchell, Chris
    2021 IEEE 20TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2021), 2021, : 494 - 505
  • [7] Privacy-Preserving Collective Learning With Homomorphic Encryption
    Paul, Jestine
    Annamalai, Meenatchi Sundaram Muthu Selva
    Ming, William
    Al Badawi, Ahmad
    Veeravalli, Bharadwaj
    Aung, Khin Mi Mi
    IEEE ACCESS, 2021, 9 : 132084 - 132096
  • [8] A privacy-preserving parallel and homomorphic encryption scheme
    Min, Zhaoe
    Yang, Geng
    Shi, Jingqi
    OPEN PHYSICS, 2017, 15 (01): : 135 - 142
  • [9] A Review of Homomorphic Encryption for Privacy-Preserving Biometrics
    Yang, Wencheng
    Wang, Song
    Cui, Hui
    Tang, Zhaohui
    Li, Yan
    SENSORS, 2023, 23 (07)
  • [10] Privacy-Preserving Collaborative Filtering Using Fully Homomorphic Encryption
    Jumonji, Seiya
    Sakai, Kazuya
    Sun, Min-Te
    Ku, Wei-Shinn
    2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022), 2022, : 1551 - 1552