Photo Based Location Information Services Using Encryption Free Framework with Duplicate Checking

Authors

  • M. Chandraleka  PG Student, Department of computer science and engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India
  • Mrs. D. Anitha  M.E., Assistant Professor,Department of computer science and Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India

DOI:

https://doi.org/10.32628/CSEIT206532

Keywords:

CNN, Feature extraction, Feature Transformation, Information retrieval.

Abstract

In mobile, many applications provide services to the users based on the photos provided by the user.Certain applications, client users take a photo of a certain spot and send it to a server, the server identifies the spot with an image recognizer and returns its related information to the users.It can cause a privacy issue because image recognition results are sometimes privacy sensitive.To overcome the problems of existing approaches, proposed an Encryption-Free framework for Privacy preserving Image Recognition, called Enfpire.InEnfPire, the server cannot identify the client users current location, its candidates can only be presented. In proposed thefeature extraction with CNN algorithm help to collect the unique and accurate features from input image and also used Duplicate Detection process to detect images with same features present within same index.In proposed approach user transform the extracted image feature x into y on the user server and sends it to the public server.With the transformation , the effectiveness of the original feature x is degraded so that the public server cannot uniquely recognize the spot-ID of user from y.It only retrives the relevant spot ID’s.The unique spot ID will identify and information regarding the spot and relevant images will be given to the user.

References

  1. Koskela et.al[2],proposed “Convolutional network features for scene recognition,” in Proceedings 22nd ACM International Conference  Multimedia, 2014.
  2. Lee et.al[3],proposed “Protecting location privacy using location semantics,” in Proceedings 17th ACM International Conference Knowledge Discovery Data Mining, 2011.
  3. Zhang et.al[3],proposed “Preserving user location privacy in mobile data management infrastructures,” in Proceedings 6th International Workshop Privacy Enhancing Technology, 2006.
  4. C.Chang et.al, proposed “A privacy-preserving bipartite graph matching framework for multimedia analysis and retrieval,” in Proceedings 5th ACM Internatioanl Conference Multimedia Retrieval., 2015.
  5. Jia et al.,proposed “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings 22nd ACM International Conference  Multimedia, 2014.
  6. Kido et.al[2],proposed “An anonymous communication technique using dummies for location-based services,” in Proceedings International Conference Pervasive Services, Jul. 2005.
  7. Mika et.al[4],proposed “Fisher discriminant analysis with kernels” in Proceedings Neural Network Signal Process,1999.
  8. R.Sadeghi et.al.,proposed “Efficient privacypreserving face recognition” in Proceedings of the 12th International Conference on Information Security Cryptology,2009.
  9. Zhou et.al[4],proposed“Learning deep features for scene recognition using places database” in Proceedings 27th International Conference Neural Information Process System, 2014.
  10. Weng et.al[3],proposed“A privacy-preserving framework for large-scale content-based information retrieval,” IEEE Transactions Information Forensics Security, 2015.
  11. Xie et.al[4] Proposed “Hybrid CNN and dictionary-based models for scene recognition and domain adaptation” IEEE Transactions on Circuits and System for Video Technology,2017.
  12. Krizhevsky et.al[3], Proposed “Image net classification with deep convolutional neural networks” in Proceedings of the 25th International Conference on Neural Information Processing System,2012.
  13. Agrawal et.al[3], proposed “Analyzing the performance of multilayer neural networks for object recognition” in proceedings of 13th European Conference on computer vision,2014.
  14. Liang et.al[3], Proposed “Recurrent convolutional neural network for object recognition” in Proceedings of the 28th IEEE Conference on Computer Vision,2015.
  15. Nakamura et.al[2], Proposed “A Framework of Privacy-Preserving Image Recognition for Photo-Based Information Services” IEEE Transaction on Information Forensics,2017.
  16. -Y. Hsu, C.-S. Lu, and S.-c. Pei, “Image Feature Extraction in Encrypted Domain With Privacy-Preserving SIFT,” IEEE Trans. Image Process., vol. 21, no. 11, pp. 4593–4607, 2012.
  17. Zhang, T. Jung, C. Liu, X. Ding, X.-Y. Li, and Y. Liu, “Pop: Privacy-preserving outsourced photo sharing and searching for mobile devices,” in ICDCS. IEEE, 2015.
  18. Daneshi and J. Guo, “Image reconstruction based on local feature descriptors,” 2011.
  19. Qin, J. Yan, K. Ren, C. Chen, and C. Wang: “Towards Efficient Privacy-Preserving Image Feature Extraction in Cloud Computing,” inProceedings of the 22nd ACM International Conference on Multimedia, pp.497–506, 2014.
  20. Qin, J. Yan, K. Ren, C. Chen, and C. Wang: “SecSIFT: Secure Image SIFT Feature Extraction in Cloud Computing,” ACM Transactions on Multimedia Computing, Communications, and Applications, Vol.12, No.4, 2016.
  21. Hsu, C. Lu, and S. Pei: “Image Feature Extraction in Encrypted Transactions on Image Processing Domain with Privacy-Preserving SIFT,” IEEE, Vol.21, No.11, pp.4593–4607, 2012.
  22. Chu and F. Chang: “A Privacy-Preserving Bipartite Graph Matching Framework for Multimedia Analysis and Retrieval,” in Proceeding of the 5th ACM International Conference on Multimedia Retrieval, pp.243– 250, 2015.
  23. Kantarcıoglu and C. Clifton: “Privately Computing a Distributed k-NN Classifier,” in Proceedings of the 8th European Conference on Principles of Data Mining and Knowledge Discovery, 2004.
  24. Ji Zhang “Efficient and Effective Filtering of Duplication Detection in Large Database Applications”, JOURNAL OF SOFTWARE, vol. 7, no. 11, november 2012.
  25. Jeffrey Dean and Sanjay Ghemawat “MapReduce: Simplified Data Processing on Large Clusters”, OSDI2004.
  26. C. Conceicao “A Comparison of Map Reduce and Parallel Database Management Systems”, ICONS 2013: The Eighth International Conference on Systems.
  27. -W. Shou and C. T. Lin, "Image descreening by GA-CNN-Based eof feature curves between Texture Classification," IEEE Trans. on circuits and system I, vol. 51, pp. 2287-2299, 2004.
  28. Shrestha and L. Vanneschi, "Improved Fully Convolutional Network with Conditional Random Fields for Building Extraction," Remote Sensing, vol. 10, 2018.
  29. Xu, L. Wu, Z. Xie, and Z. Chen, "Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters," Remote Sensing, vol. 10, 2018.
  30. Maoguo Gong, Jiaojiao Zhao, Jia Liu, Qiguang Miao, and Licheng Jiao, “Change detection in synthetic aper- ture radar images based on deep neural networks,” IEEE Transactions on Neural Networks and Learning Sys- tems, vol. 27, pp. 125–138, Jan2016.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-10-30

Issue

Volume 6, Issue 5, September-October-2020

Section

Research Articles

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How to Cite

[1]
M. Chandraleka, Mrs. D. Anitha, " Photo Based Location Information Services Using Encryption Free Framework with Duplicate Checking" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 5, pp.141-150, September-October-2020. Available at doi : https://doi.org/10.32628/CSEIT206532