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Multi-key spatio-temporal chaotic image encryption and retrieval scheme

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Abstract

To protect privacy, many users opt to encrypt images prior to outsourcing them to cloud service platforms (CSPs). However, this encryption process results in the loss of image features and subsequent inability to retrieve them. We propose an image encryption and retrieval algorithm, which ensures that privacy is not leaked in both the upload and retrieval stages. First, overcoming the insufficient security and degradation in chaotic systems, we introduce the time-varying functions, and unscented Kalman filter to improve the non-adjacent coupled map lattice complexity and security. Secondly, considering the encryption efficiency, we compress the plaintext image to reduce the time of the encryption phase and improve the overall encryption speed. Finally, we use the locally sensitive hash (LSH) for feature vector dimensionality reduction to improve the retrieval efficiency and perform a secondary LSH on the reduced feature vector to form a new hash-key retrieval structure in the generate index phase, which improves the retrieval efficiency. The experimental results prove that our proposed encryption algorithm can meet the image encryption algorithm's high retrieval accuracy and multi-user without revealing privacy.

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Data availability

Data available on request from the authors. The data that support the findings of this study are available from the corresponding author, upon reasonable request.

References

  1. Xian, Y., Wang, X., Teng, L.: Double parameters fractal sorting matrix and its application in image encryption. IEEE Trans. Circuits Syst. Video Technol. 32, 4028–4037 (2022). https://doi.org/10.1109/TCSVT.2021.3108767

    Article  Google Scholar 

  2. Yang, T., Ma, J., Miao, Y., Wang, Y., Liu, X., Choo, K.-K.R., Xiao, B.: MU-TEIR: traceable encrypted image retrieval in the multi-user setting. IEEE Trans. Serv. Comput. 16, 1282–1295 (2023). https://doi.org/10.1109/TSC.2022.3149962

    Article  Google Scholar 

  3. Lehmkuhl, P.M.R., Srinivasan, A.: DELPHI: a cryptographic inference service for neural networks. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 2505–2522 (2020)

  4. Liu, Z., Chen, F., Xu, J., Pei, W., Lu, G.: Image-text retrieval with cross-modal semantic importance consistency. IEEE Trans. Circuits Syst. Video Technol. (2022). https://doi.org/10.1109/TCSVT.2022.3220297

    Article  Google Scholar 

  5. Xiong, L., Han, X., Yang, C.-N., Shi, Y.-Q.: Robust reversible watermarking in encrypted image with secure multi-party based on lightweight cryptography. IEEE Trans. Circuits Syst. Video Technol. 32, 75–91 (2022). https://doi.org/10.1109/TCSVT.2021.3055072

    Article  Google Scholar 

  6. Babaei, M.: A novel text and image encryption method based on chaos theory and DNA computing. Nat. Comput. 2013, 101–107 (2013)

    Article  MathSciNet  Google Scholar 

  7. Wu, X., Wang, K., Wang, X., Kan, H., Kurths, J.: Color image DNA encryption using NCA map-based CML and one-time keys. Signal Process. 148, 272–287 (2018). https://doi.org/10.1016/j.sigpro.2018.02.028

    Article  Google Scholar 

  8. Wang, M., Wang, X., Zhang, Y., Zhou, S., Zhao, T., Yao, N.: A novel chaotic system and its application in a color image cryptosystem. Opt. Lasers Eng. 121, 479–494 (2019). https://doi.org/10.1016/j.optlaseng.2019.05.013

    Article  Google Scholar 

  9. Zhang, Y.-Q., Wang, X., Liu, J., Chi, Z.: An image encryption scheme based on the MLNCML system using DNA sequences. Opt. Lasers Eng. 82, 95–103 (2016)

    Article  Google Scholar 

  10. Li, W., Zhao, Z., Liu, A.-A., Gao, Z., Yan, C., Mao, Z., Chen, H., Nie, W.: Joint local correlation and global contextual information for unsupervised 3D model retrieval and classification. IEEE Trans. Circuits Syst. Video Technol. 32, 3265–3278 (2022). https://doi.org/10.1109/TCSVT.2021.3099496

    Article  Google Scholar 

  11. Sun, H., Xu, J., Wang, J., Qi, Q., Ge, C., Liao, J.: DLI-net: dual local interaction network for fine-grained sketch-based image retrieval. IEEE Trans. Circuits Syst. Video Technol. 32, 7177–7189 (2022). https://doi.org/10.1109/TCSVT.2022.3171972

    Article  Google Scholar 

  12. Yang, S., Li, Q., Li, W., Li, X., Liu, A.-A.: Dual-level representation enhancement on characteristic and context for image-text retrieval. IEEE Trans. Circuits Syst. Video Technol. 32, 8037–8050 (2022). https://doi.org/10.1109/TCSVT.2022.3182426

    Article  Google Scholar 

  13. Wang, H., Ning, J., Huang, X., Wei, G., Poh, G.S., Liu, X.: Secure fine-grained encrypted keyword search for E-healthcare cloud. IEEE Trans. Depend. Secure Comput. 18, 1307–1319 (2021)

    Google Scholar 

  14. Li, H., Yang, Y., Dai, Y., Bai, J., Yu, S., Xiang, Y.: Achieving secure and efficient dynamic searchable symmetric encryption over medical cloud data. IEEE Trans. Cloud Comput. 8, 484–494 (2020)

    Article  Google Scholar 

  15. Wang, X., Liu, P.: A new full chaos coupled mapping lattice and its application in privacy image encryption. IEEE Trans. Circuits Syst. I(69), 1291–1301 (2022). https://doi.org/10.1109/TCSI.2021.3133318

    Article  Google Scholar 

  16. Wang, H., Xiao, D., Li, M., Xiang, Y., Li, X.: A visually secure image encryption scheme based on parallel compressive sensing. Signal Process. 155, 218–232 (2019). https://doi.org/10.1016/j.sigpro.2018.10.001

    Article  Google Scholar 

  17. Wang, X., Zhang, M.: An image encryption algorithm based on new chaos and diffusion values of a truth table. Inf. Sci. 579, 128–149 (2021). https://doi.org/10.1016/j.ins.2021.07.096

    Article  MathSciNet  Google Scholar 

  18. Wang, H., Xia, Z., Fei, J., Xiao, F.: An AES-based secure image retrieval scheme using random mapping and BOW in cloud computing. IEEE Access. 8, 61138–61147 (2020). https://doi.org/10.1109/ACCESS.2020.2983194

    Article  Google Scholar 

  19. Li, C., Lin, D., Lu, J., Hao, F.: Cryptanalyzing an image encryption algorithm based on autoblocking and electrocardiography. IEEE Multimedia 25, 46–56 (2018). https://doi.org/10.1109/MMUL.2018.2873472

    Article  Google Scholar 

  20. Li, C., Lin, D., Lü, J.: Cryptanalyzing an image-scrambling encryption algorithm of pixel bits. IEEE Multimedia 24, 64–71 (2017). https://doi.org/10.1109/MMUL.2017.3051512

    Article  Google Scholar 

  21. Zhou, Y., Bao, L., Chen, C.L.P.: A new 1D chaotic system for image encryption. Signal Process. 97, 172–182 (2014). https://doi.org/10.1016/j.sigpro.2013.10.034

    Article  Google Scholar 

  22. Lv, X., Liao, X., Yang, B.: A novel pseudo-random number generator from coupled map lattice with time-varying delay. Nonlinear Dyn. 94, 325–341 (2018). https://doi.org/10.1007/s11071-018-4361-4

    Article  Google Scholar 

  23. Gopalakrishnan, T., Ramakrishnan, S.: Image encryption using hyper-chaotic map for permutation and diffusion by multiple hyper-chaotic maps. Wireless Pers. Commun. 109, 437–454 (2019). https://doi.org/10.1007/s11277-019-06573-x

    Article  Google Scholar 

  24. Wang, Q., Yu, S., Li, C., Lu, J., Fang, X., Guyeux, C., Bahi, J.M.: Theoretical design and FPGA-based implementation of higher-dimensional digital chaotic systems. IEEE Trans. Circuits Syst. 63, 401–412 (2016). https://doi.org/10.1109/TCSI.2016.2515398

    Article  MathSciNet  Google Scholar 

  25. Xuejing, K., Zihui, G.: A new color image encryption scheme based on DNA encoding and spatiotemporal chaotic system. Signal Process.: Image Commun. 80, 1–11 (2020). https://doi.org/10.1016/j.image.2019.115670

    Article  Google Scholar 

  26. Lu, W., Varna, A.L., Swaminathan, A., Wu, M.: Secure image retrieval through feature protection. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1533–1536. IEEE, Taipei, Taiwan (2009)

  27. Chabanne, H., de Wargny, A., Milgram, J., Morel, C., Prouff, E., Identity, S.: Privacy-preserving classification on deep neural network. Design and FPGA-Based Implementation of Higher-Dimensional

  28. Xia, Z., Wang, X., Zhang, L., Qin, Z., Sun, X., Ren, K.: A Privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans. Inform. Forensic Secur. 11, 2594–2608 (2016). https://doi.org/10.1109/TIFS.2016.2590944

    Article  Google Scholar 

  29. Liu, J., Wang, C., Tu, Z., Wang, X.A., Lin, C., Li, Z.: Secure KNN classification scheme based on homomorphic encryption for cyberspace. Secur. Commun. Netw. 2021, 1–12 (2021). https://doi.org/10.1155/2021/8759922

    Article  Google Scholar 

  30. Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Order preserving encryption for numeric data. In: Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp. 563–574. ACM Press, Paris, France (2004)

  31. Fan, K., Wang, X., Suto, K., Li, H., Yang, Y.: Secure and efficient privacy-preserving ciphertext retrieval in connected vehicular cloud computing. IEEE Network 32, 52–57 (2018). https://doi.org/10.1109/MNET.2018.1700327

    Article  Google Scholar 

  32. Zou, Q., Jianfeng, W., Jun, Y., Jian, S., Xiaofeng, C.: Efficient and secure encrypted image search in mobile cloud computing. Soft. Comput. 21, 2959–2969 (2017). https://doi.org/10.1007/s00500-016-2153-7

    Article  Google Scholar 

  33. Qin, Z., Yan, J., Ren, K., Chen, C.W., Wang, C.: Towards efficient privacy-preserving image feature extraction in cloud computing. In: Proceedings of the 22nd ACM international conference on Multimedia, pp. 497–506. ACM, Orlando Florida USA (2014)

  34. Huang, H., Guo, W., Zhang, Y.: Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm. In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, pp. 272–276. IEEE, Wuhan, China (2008)

  35. Yan Ke, Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, pp. 506–513. IEEE, Washington, DC, USA (2004)

  36. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 International Conference on Computer Vision. pp. 2564–2571. IEEE, Barcelona, Spain (2011)

  37. Hsu, C.-Y., Lu, C.-S., Pei, S.-C.: Image feature extraction in encrypted domain with privacy-preserving SIFT. IEEE Trans. Image Process. 21, 4593–4607 (2012). https://doi.org/10.1109/TIP.2012.2204272

    Article  MathSciNet  Google Scholar 

  38. Li, J.-S., Liu, I.-H., Tsai, C.-J., Su, Z.-Y., Li, C.-F., Liu, C.-G.: Secure content-based image retrieval in the cloud with key confidentiality. IEEE Access. 8, 114940–114952 (2020). https://doi.org/10.1109/ACCESS.2020.3003928

    Article  Google Scholar 

  39. Zhang, L., Xia, R., Tian, W., Cheng, Z., Yan, Z., Tang, P.: FLSIR: secure image retrieval based on federated learning and additive secret sharing. IEEE Access. 10, 64028–64042 (2022). https://doi.org/10.1109/ACCESS.2022.3183224

    Article  Google Scholar 

  40. Ying-Qian, Z.: A new image encryption algorithm based on non-adjacent coupled map lattices. Appl. Soft Comput. 11 (2015)

  41. Liu, H., Kadir, A., Liu, J.: Keyed hash function using hyper chaotic system with time-varying parameters perturbation. IEEE Access. 7, 37211–37219 (2019)

    Article  Google Scholar 

  42. Man, Z., Li, J., Di, X., Sheng, Y., Liu, Z.: Double image encryption algorithm based on neural network and chaos. Chaos Solitons Fractals 152, 111318 (2021). https://doi.org/10.1016/j.chaos.2021.111318

    Article  MathSciNet  Google Scholar 

  43. Wang, X., Wang, Y., Wang, S., Zhang, Y., Wu, X.: A novel pseudo-random coupled LP spatiotemporal chaos and its application in image encryption. Chin. Phys. B 27, 110502 (2018)

    Article  Google Scholar 

  44. Luo, Y., Liu, Y., Liu, J., Tang, S., Harkin, J., Cao, Y.: Counteracting dynamical degradation of a class of digital chaotic systems via Unscented Kalman Filter and perturbation. Inf. Sci. 556, 49–66 (2021). https://doi.org/10.1016/j.ins.2020.12.065

    Article  MathSciNet  Google Scholar 

  45. Hu, H., Deng, Y., Liu, L.: Counteracting the dynamical degradation of digital chaos via hybrid control. Commun. Nonlinear Sci. Numer. Simul. 19, 1970–1984 (2014). https://doi.org/10.1016/j.cnsns.2013.10.031

    Article  Google Scholar 

  46. Hu, H., Liu, L., Ding, N.: Pseudorandom sequence generator based on the Chen chaotic system. Comput. Phys. Commun. 184, 765–768 (2013). https://doi.org/10.1016/j.cpc.2012.11.017

    Article  MathSciNet  Google Scholar 

  47. Cao, N., Wang, C., Li, M., Ren, K., Lou, W.: Privacy-preserving multi-keyword ranked search over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. 25, 222–233 (2014). https://doi.org/10.1109/TPDS.2013.45

    Article  Google Scholar 

  48. Wang, M.: Spatiotemporal chaos in improved cross coupled map lattice and its application in a bit-level image encryption scheme. Inf. Sci. 544, 1–24 (2021)

    Article  MathSciNet  Google Scholar 

  49. Wu, Y., Agaian, S.: NPCR and UACI randomness tests for image encryption. J. Select. Areas Telecommun. 11, 31–38 (2011)

    Google Scholar 

  50. Wu, Y., Zhou, Y., Noonan, J.P., Agaian, S.: Design of image cipher using latin squares. Inf. Sci. 264, 317–339 (2014). https://doi.org/10.1016/j.ins.2013.11.027

    Article  MathSciNet  Google Scholar 

  51. Xia, Z., Xiong, N.N., Vasilakos, A.V., Sun, X.: EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Inf. Sci. 387, 195–204 (2017). https://doi.org/10.1016/j.ins.2016.12.030

    Article  Google Scholar 

  52. Wang, X., Guan, N., Zhao, H., Wang, S., Zhang, Y.: A new image encryption scheme based on coupling map lattices with mixed multi-chaos. Sci. Rep. 10, 1–15 (2020). https://doi.org/10.1038/s41598-020-66486-9

    Article  Google Scholar 

  53. Liu, W., Sun, K., Zhu, C.: A fast image encryption algorithm based on chaotic map. Opt. Lasers Eng. 84, 26–36 (2016). https://doi.org/10.1016/j.optlaseng.2016.03.019

    Article  Google Scholar 

  54. Guo, C., Su, S., Choo, K.-K.R., Tang, X.: A Fast Nearest Neighbor Search Scheme over Outsourced Encrypted Medical Images. IEEE Trans. Industr. Inf. 17, 514–523 (2018). https://doi.org/10.1109/TII.2018.2883680

    Article  Google Scholar 

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Acknowledgements

This research is supported by the National key research and development program of China (No. 2020YFE0200600, 62002058) and National Natural Science Foundation of China (No. U22B2026).

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Authors and Affiliations

  1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China

    Yu Wang

  2. School of Cyber Science and Engineering, Southeast University, Nanjing, 210000, China

    Yu Wang, Liquan Chen, Kunliang Yu & Huiyu Fang

  3. Purple Mountain Laboratories for Network and Communication Security, Nanjing, 211111, China

    Liquan Chen

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  1. Yu Wang
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Correspondence to Yu Wang.

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Wang, Y., Chen, L., Yu, K. et al. Multi-key spatio-temporal chaotic image encryption and retrieval scheme. Nonlinear Dyn 112, 3003–3025 (2024). https://doi.org/10.1007/s11071-023-09170-7

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