基于同态加密域的自适应提升小波
Adaptive Lifting Wavelet Scheme Based on Homomorphic Encrypted Domain

DOI: 10.12677/CSA.2019.94075, PP. 663-673

Keywords: 自适应提升小波，同态加密系统，加密信息处理
Adaptive Lifting Wavelet Scheme, Homomorphic Cryptosystem, Signal Processing in the Encrypted Domain

Full-Text   Cite this paper   Add to My Lib

Abstract:

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。
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.

References

[1]	[1] Rivest, R., Shamir, A. and Adleman, L. (1978) A Method for Obtaining Digital Signatures and Public-Key Cryptosys-tems. Communications of the ACM, 21, 120-126. https://doi.org/10.1145/359340.359342
[2]	Elgamal, T. (1985) A Public-Key Cryptosystems and a Signature Scheme Based on Discrete Logarithms. IEEE Transactions on Information Theory, 31, 469-472. https://doi.org/10.1109/TIT.1985.1057074
[3]	Paillier, P. (1999) Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. International Conference on the Theory and Application of Crypto-graphic Techniques, Prague, 2-6 May 1999, 223-238. https://doi.org/10.1007/3-540-48910-X_16
[4]	Damgard, I. and Jurik, M. (2001) A Generalization, a Simplifica-tion and Some Application of Paillier’s Probabilistic Public-Key System. 4th International Workshop on Practice and Theory in Public Key Cryptography, Cheju Island, 13-15 February 2001, 119-136. https://doi.org/10.1007/3-540-44586-2_9
[5]	Rivest, R., Adleman, L. and Dertouzos, M. (1978) On Data Banks and Privacy Homomorphisms. In: DeMillo, R.A., et al., Eds., Foundations of Secure Computation, MIT Press, Cam-bridge, 169-178.
[6]	Run, R.S., Horng, S.J., Lai, J.L., Kao, T.W. and Chen, R.J. (2012) An Improved SVD-Based Watermarking Technique for Copyright Protection. Expert Systems with Applications, 39, 673-689. https://doi.org/10.1016/j.eswa.2011.07.059
[7]	Wang, X., Liang, J.H. and Wang, M.Z. (2013) On-Line Fast Palmprint Identification Based on Adaptive Lifting Wavelet Scheme. Knowledge-Based Systems, 42, 68-73. https://doi.org/10.1016/j.knosys.2013.01.013
[8]	Hsu, C.H., Lu, C.S. and Pei, S.C. (2012) Image Feature Extrac-tion in Encrypted Domain with Privacy-Preserving SIFT. IEEE Transactions on Image Processing, 21, 4593-4607. https://doi.org/10.1109/TIP.2012.2204272
[9]	Dalali, S. and Suresh, L. (2016) Daubechive Wavelet Based Face Recognition Using Modified LBP. Procedia Computer Science, 93, 344-350. https://doi.org/10.1016/j.procs.2016.07.219
[10]	Yang, W.K., Wang, Z.Y. and Zhang, B.C. (2016) Face Recogni-tion Using Adaptive Local Ternary Patterns Method. Neurocomputing, 213, 183-190. https://doi.org/10.1016/j.neucom.2015.11.134
[11]	Ojala, T., Pietik？inen, M. and M？enp？？, T. (2002) Multiresolu-tion Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971-987. https://doi.org/10.1109/TPAMI.2002.1017623
[12]	Zheng, P.J. (2013) Student Member, Discrete Wavelet Trans-form and Data Expansion Reduction in Homomorphic Encrypted Domain. IEEE Transactions on Image Processing, 22, 2455-2468. https://doi.org/10.1109/TIP.2013.2253474
[13]	Cox, I., Kilian, J., Leighton, F. and Shamoon, T. (1997) Secure Spread Spectrum Watermarking for Multimedia. IEEE Transactions on Image Processing, 6, 1673-1687. https://doi.org/10.1109/83.650120
[14]	Zhang, D., Kong, A., You, J. and Wong, M. (2003) Online Palmprint Iden-tification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1041-1050. https://doi.org/10.1109/TPAMI.2003.1227981
[15]	Pan, X. and Ruan, Q.Q. (2008) Palmprint Recognition Using Gabor Feature-Based (2D) 2 PCAJ. Neurocomputing, 71, 3032-3036. https://doi.org/10.1016/j.neucom.2007.12.030
[16]	Kong, W.K., Zhang, D. and Li, W. (2003) Palmprint Feature Extraction Using 2-D Gabor Filters. Pattern Recognition, 36, 2339-2347. https://doi.org/10.1016/S0031-3203(03)00121-3
[17]	Wang, X., Lei, L. and Wang, M. (2012) Palmprint Verifica-tion Based on 2D-Gabor Wavelet and Pulse-Coupled Neural Network. Knowledge-Based Systems, 27, 451-455. https://doi.org/10.1016/j.knosys.2011.10.008
[18]	Wu, X., Zhang, D. and Wang, K. (2003) Fisherpalms Based Palmprint Reconition. Pattern Recognition Letters, 24, 2829-2838. https://doi.org/10.1016/S0167-8655(03)00141-7
[19]	Lu, G., Zhang, D. and Wang, K. (2003) Palmprint Recogni-tion Using Eigenpalms Features. Pattern Recognition Letters, 24, 1463-1467. https://doi.org/10.1016/S0167-8655(02)00386-0

Full-Text