基于同态加密域的自适应提升小波
Adaptive Lifting Wavelet Scheme Based on Homomorphic Encrypted Domain
摘要: 随着信息时代的不断发展，信息安全与隐私保护问题越来越受到人们的重视，由于有价值的信号可能受到恶意攻击，因此它们必须在加密的域中进行处理。本文利用Paillier密码系统的同态特性，对加密域内的待处理信号进行小波分解，提出了一种加密的自适应提升小波方法。此外，还提出了采用加密的LBP方案对细节信号进行编码的方法，这些方法用于服务器在没有私钥的情况下进行数据处理，从而避免敏感信息泄露是非常有用的。我们进行了加密域的重构实验、水印实验和掌纹分类实验，验证了我们提出加密域自适应提升小波的有效性和可行性。
Abstract: With the development of information industry, data privacy’s protection becomes a necessary issue to face with. Since the valuable signals need to be protected from a malicious device, they must be processed in an encrypted domain. In this paper, we proposed an encrypted adaptive lifting wavelet method by using the homomorphic properties of Paillier’s cryptosystem to act wavelet decomposition on pending signal in encrypted domain. In addition, the implementation of encrypted LBP scheme is also proposed to encode detail signal which is prerequisite for classification work. The mentioned works are useful for classification servers to process data without private-key so that the sensitive message will not divulge. We conduct some experiments include reconstruction, simplified watermarking, and palmprint classification experiments in encrypted domain to demonstrate the usefulness and feasibility of our encrypted processing work.
文章引用：吴怡宣, 王晅. 基于同态加密域的自适应提升小波[J]. 计算机科学与应用, 2019, 9(4): 663-673. https://doi.org/10.12677/CSA.2019.94075

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