SpringerLink
Log in

A Differential Privacy Image Publishing Method Based on Wavelet Transform

  • Conference paper
  • First Online:
Parallel and Distributed Computing, Applications and Technologies (PDCAT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13148))

  • 1539 Accesses

Abstract

Image is an important information-bearing medium with many important attributes. If the image data is released directly, personal privacy will be compromised. This paper aims at how to use the method of differential privacy to protect the privacy of image data and make the image data have high usability. In this paper, a WIP method based on wavelet change is proposed. Firstly, wavelet transform is used to compress the image. Then, noise is added to the main features after transformation to obtain the published image satisfying the differential privacy. It solves the problem of low usability of large images and the problem that Fourier transform cannot deal with abrupt signal. Experimental results show that compared with similar methods in the frequency domain, the denoised image obtained by the proposed WIP method is more distinguishable and the information entropy is closer to the original image. The accuracy is 10% higher than other methods. Compared with other frequency-domain methods for image differential privacy protection, the proposed WIP method has higher usability and robustness.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 74.89
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 96.29
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kamijo, S., Matsushita, Y., Ikeuchi, K., et al.: Traffic monitoring and accident detection at intersections. IEEE Trans. Intell. Transp. Syst. 1(2), 108–118 (2000)

    Article  Google Scholar 

  2. Hill, S., Zhou, Z., Saul, L., et al.: On the (in) effectiveness of mosaicing and blurring as tools for document redaction. Proc. Priv. Enhancing Technol. 2016(4), 403–417 (2016)

    Article  Google Scholar 

  3. Ra, M.R., Govindan, R., Ortega, A.: P3: toward privacy-preserving photo sharing. Comput. Sci. 1(2), 515–528 (2013)

    Google Scholar 

  4. Vengadapurvaja, A.M., Nisha, G., Aarthy, R., et al.: An efficient homomorphic medical image encryption algorithm for cloud storage security. Procedia Comput. Sci. 115(1), 643–650 (2017)

    Article  Google Scholar 

  5. Jiang, L., Xu, C., Wang, X., et al.: Secure outsourcing SIFT: efficient and privacy-preserving image feature extraction in the encrypted domain. IEEE Trans. Dependable Secure Comput. 17(1), 179–193 (2017)

    Article  Google Scholar 

  6. Wang, S., Nassar, M., Atallah, M., Malluhi, Q.: Secure and private outsourcing of shape-based feature extraction. In: Qing, S., Zhou, J., Liu, D. (eds.) Information and Communications Security. ICICS 2013. Lecture Notes in Computer Science, vol. 8233, pp. 90–99 Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02726-5_7

  7. Wang, Q., Hu, S., Ren, K., et al.: Catch me in the dark: effective privacy-preserving outsourcing of feature extractions over image data. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, vol. 2, no. 2, pp. 1–9. IEEE (2016)

    Google Scholar 

  8. Guo, J.M., Prasetyo, H.: False-positive-free SVD-based image watermarking. J. Vis. Commun. Image Represent. 25(5), 1149–1163 (2014)

    Article  Google Scholar 

  9. Zhang, X.J., Fu, C.C., Meng, X.F.: Facial image publication with differential privacy. J. Image Graph. 23(9), 1305–1315 (2018)

    Google Scholar 

  10. Zhang, X.J., Fu, C.C., Meng, X.F.: Private facial image publication through matrix decomposition. J. Image Graph. 25(04), 0655–0668 (2020)

    Google Scholar 

  11. Liu, C., Yang, J., Zhao, W., et al.: Face image publication based on differential privacy. Wirel. Commun. Mob. Comput. 2021, 1–20 (2021)

    Google Scholar 

  12. Jorgensen, Z., Yu, T., Cormode, G.: Publishing attributed social graphs with formal privacy guarantee. In: Proceedings of the 2016 international conference on management of data, vol. 4, no. 1, pp. 107–122 (2016)

    Google Scholar 

  13. Biega, J.A., Gummadi, K.P., Mele, I., et al.: R-susceptibility: An ir-centric approach to assessing privacy risks for users in online communities. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval vol. 2, no. 1, pp. 365–374 (2016)

    Google Scholar 

  14. **ao, D., Eltabakh, M.Y., Kong, X.: Sharing uncertain graphs using syntactic private graph models. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), vol. 1, no. 2, pp. 1336–1339. IEEE (2018)

    Google Scholar 

  15. Zheng, X., Luo, G., Cai, Z.: A fair mechanism for private data publication in online social networks. IEEE Trans. Netw. Sci. Eng. 7(2), 880–891 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

This work is partially supported by the National Natural Science Foundation of China (No. 61862007).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China

    Guifen Zhang, Hangui Wei, Lina Ge & **a Qin

  2. Key Laboratory of Network Communication Engineering, Guangxi University for Nationalities, Nanning, China

    Guifen Zhang & Lina Ge

Authors
  1. Guifen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hangui Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lina Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. **a Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lina Ge .

Editor information

Editors and Affiliations

  1. Sun Yat-sen University, Guangzhou, Guangdong, China

    Hong Shen

  2. Sun Yat-sen University, Guangzhou, China

    Yingpeng Sang

  3. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Yong Zhang

  4. Sun Yat-sen University, Guangzhou, China

    Nong **ao

  5. University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  6. University of Utah, Salt Lake City, USA

    Geoffrey Fox

  7. Western Michigan University, Kalamazoo, MI, USA

    Ajay Gupta

  8. Stevens Institute of Technology, Hoboken, NJ, USA

    Manu Malek

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, G., Wei, H., Ge, L., Qin, X. (2022). A Differential Privacy Image Publishing Method Based on Wavelet Transform. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96772-7_55

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96771-0

  • Online ISBN: 978-3-030-96772-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation