Advertisement

Soft Computing

, Volume 23, Issue 23, pp 12813–12820 | Cite as

Secure face retrieval for group mobile users

  • Xin Jin
  • Yujie Li
  • Shiming GeEmail author
  • Chenggen Song
  • Le Wu
  • Xinghui Zhou
Methodologies and Application
  • 33 Downloads

Abstract

Recently, cloud storage and processing have been widely adopted. Mobile users in one family or one team may automatically backup their photos to the same shared cloud storage space. The powerful face detector trained and provided by a 3rd party may be used to retrieve the photo collection which contains a specific group of persons from the cloud storage server. However, the privacy of the mobile users may be leaked to the cloud server providers. In the meanwhile, the copyright of the face detector should be protected. Thus, in this paper, we propose a protocol of privacy preserving face retrieval in the cloud for mobile users, which protects the user photos and the face detector simultaneously. The cloud server only provides the resources of storage and computing and cannot learn anything of the user photos and the face detector. We test our protocol inside several families and classes. The experimental results reveal that our protocol can successfully retrieve the proper photos from the cloud server and protect the user photos and the face detector.

Keywords

Privacy preserving Face retrieval Cloud computing 

Notes

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61772047, 61772513), the Open Funds of CETC Big Data Research Institute Co.,Ltd., (Grant No. W-2018022), the Science and Technology Project of the State Archives Administrator (Grant No. 2015-B-10), and the Fundamental Research Funds for the Central Universities (Grant Nos. 328201803, 328201801).

Compliance with ethical standards

Conflict of interest

None of the authors declare that he/she has conflict of interest.

Human or animals rights

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Avidan S, Butman M (2006) Blind vision. In: Computer vision-ECCV, 9th European conference on computer vision, Graz, Austria, 7–13 May 2006, Proceedings, Part III, pp 1–13Google Scholar
  2. Bost R, Popa RA, Tu S, Goldwasser S (2015) Machine learning classification over encrypted data. In: 22nd annual network and distributed system security symposium, NDSS, San Diego, California, USA, 8–11 Feb 2014Google Scholar
  3. Chu C, Jung J, Liu Z, Mahajan R (2014) Strack: secure tracking in community surveillance. In: Proceedings of the ACM international conference on multimedia, MM’14, Orlando, FL, USA, 3–7 Nov 2014, pp 837–840Google Scholar
  4. Daemen J, Rijmen V (2002) The design of Rijndael: AES-the advanced encryption standard. Springer, BerlinCrossRefGoogle Scholar
  5. Fanti GC, Finiasz M, Ramchandran K (2013) One-way private media search on public databases: the role of signal processing. IEEE Signal Process Mag 30(2):53–61CrossRefGoogle Scholar
  6. Jin X, Chen Y, Ge S, Zhang K, Li X, Li Y, Liu Y, Guo K, Tian Y, Zhao G, Zhang X, Wang Z (2015) Applications and techniques in information security: 6th international conference, ATIS, Beijing, China, 4–6 Nov 2015, Proceedings, chap. color image encryption in CIE L*a*b* Space, pp 74–85. Springer, Berlin, HeidelbergGoogle Scholar
  7. Jin X, Guo K, Song C, Li X, Zhao G, Luo J, Li Y, Chen Y, Liu Y, Wang H (2016a) Private video foreground extraction through chaotic mapping based encryption in the cloud. In: Multimedia modeling: 22nd international conference, MMM, Miami, FL, USA, 4–6 Jan 2016, Proceedings, Part I, pp 562–573Google Scholar
  8. Jin X, Wu Y, Li X, Li Y, Zhao G, Guo K (2016b) Ppvibe: privacy preserving background extractor via secret sharing in multiple cloud servers. In: 8th international conference on wireless communications & signal processing, WCSP, Yangzhou, China, 13–15 Oct 2016, pp 1–5Google Scholar
  9. Jin X, Yuan P, Li X, Song C, Ge S, Zhao G, Chen Y (2017a) Efficient privacy preserving Viola–Jones type object detection via random base image representation. In: Proceedings of IEEE international conference on multimedia and expo (ICME), Hong Kong, China, 10–14 July 2017Google Scholar
  10. Jin X, Zhu S, Xiao C, Sun H, Li X, Zhao G, Ge S (2017b) 3D textured model encryption via 3D lu chaotic mapping. Sci China Inf Sci 60(12):122107:1-9Google Scholar
  11. Lu H, Li Y, Mu S et al (2017a) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J 5(4):2315–2322CrossRefGoogle Scholar
  12. Lu H, Li B, Zhu J et al (2017b) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comput Pract Exp 29(6):e3927CrossRefGoogle Scholar
  13. Lu H, Li Y, Chen M, Kim H, Serikawa S (2018a) Brain intelligence: go beyond artificial intelligence. Mob Netw Appl 23(2):368–375.  https://doi.org/10.1007/s11036-017-0932-8 CrossRefGoogle Scholar
  14. Lu H, Li Y, Uemura T et al (2018b) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Gener Comput SystGoogle Scholar
  15. Osadchy M, Pinkas B, Jarrous A, Moskovich B (2010) Scifi-a system for secure face identification. In: 31st IEEE symposium on security and privacy, S&P, 16–19 May 2010. Berkeley/Oakland, California, USA, pp 239–254Google Scholar
  16. Shashank J, Kowshik P, Srinathan K, Jawahar CV, (2008) Private content based image retrieval. In, (2008) IEEE computer society conference on computer vision and pattern recognition (CVPR),24–26 June 2008. Anchorage, Alaska, USAGoogle Scholar
  17. Sohn H, Plataniotis KN, Ro YM (2010) Privacy-preserving watch list screening in video surveillance system. In: Advances in multimedia information processing-PCM, 11th Pacific rim conference on multimedia, Shanghai, China, 21–24 Sept 2010, Proceedings, Part I, pp 622–632Google Scholar
  18. Upmanyu M, Namboodiri AM, Srinathan K, Jawahar CV (2009) Efficient privacy preserving video surveillance. In: IEEE 12th international conference on computer vision, ICCV, Kyoto, Japan, 27 Sept–4 Oct 2009, pp 1639–1646Google Scholar
  19. Viola PA, Jones MJ (2001) Robust real-time face detection. In: IEEE 8th international conference on computer vision ICCV, Vancouver, British Columbia, Canada, 7–14 July 2001, p 747Google Scholar
  20. Viola PA, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRefGoogle Scholar
  21. Wong WK, Cheung DWl, Kao B, Mamoulis N (2009) Secure knn computation on encrypted databases. In: Proceedings of the 2009 ACM SIGMOD international conference on management of data, p 139152, ACM, New York, NY, USAGoogle Scholar
  22. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRefGoogle Scholar
  23. Xiaobin Z, Jing L, Jinqiao W, Changsheng L, Hanqing L (2014) Sparse representation for robust abnormality detection in crowded scenes. Pattern Recognit 47(5):1791–1799CrossRefGoogle Scholar
  24. Zhu X, Jin X, Zhang X, Li C, He F, Wang L (2015) Context-aware local abnormality detection in crowded scene. Sci China Inf Sci 58(5):052110:111 (CCF-B)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Electronic Science and Technology InstituteBeijingChina
  2. 2.CETC Big Data Research Institute Co.,Ltd.GuiyangChina
  3. 3.Kyushu Institute of Technology, KITKitakyushuJapan
  4. 4.Fukuoka University, FUFukuokaJapan
  5. 5.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  6. 6.OracleChain TechnologyBeijingChina

Personalised recommendations