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I-Pic: A Platform for Privacy-Compliant Image Capture

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Published:20 June 2016Publication History

ABSTRACT

The ubiquity of portable mobile devices equipped with built-in cameras have led to a transformation in how and when digital images are captured, shared, and archived. Photographs and videos from social gatherings, public events, and even crime scenes are commonplace online. While the spontaneity afforded by these devices have led to new personal and creative outlets, privacy concerns of bystanders (and indeed, in some cases, unwilling subjects) have remained largely unaddressed. We present I-Pic, a trusted software platform that integrates digital capture with user-defined privacy. In I-Pic, users choose alevel of privacy (e.g., image capture allowed or not) based upon social context (e.g., out in public vs. with friends vs. at workplace). Privacy choices of nearby users are advertised via short-range radio, and I-Pic-compliant capture platforms generate edited media to conform to privacy choices of image subjects. I-Pic uses secure multiparty computation to ensure that users' visual features and privacy choices are not revealed publicly, regardless of whether they are the subjects of an image capture. Just as importantly, I-Pic preserves the ease-of-use and spontaneous nature of capture and sharing between trusted users. Our evaluation of I-Pic shows that a practical, energy-efficient system that conforms to the privacy choices of many users within a scene can be built and deployed using current hardware.

References

  1. Lost lake cafe, seattle restaurant, kicks out patron for wearing google glass. http://www.huffingtonpost.com/2013/11/27/lost-lake-cafe-google-glass_n_4350039.html.Google ScholarGoogle Scholar
  2. Franziska Roesner, David Molnar, Alexander Moshchuk, Tadayoshi Kohno, and Helen J. Wang. World-driven access control for continuous sensing. In ACM Conference on Computer and Communications Security (CCS), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Nisarg Raval, Animesh Srivastava, Ali Razeen, Kiron Lebeck, Ashwin Machanavajjhala, and Landon P. Cox. What you mark is what apps see. In ACM International Conference on Mobile Systems, Applications, and Services (Mobisys), 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Cheng Bo, Guobin Shen, Jie Liu, Xiang-Yang Li, Yongguang Zhang, and Feng Zhao. Privacy.tag: Privacy concern expressed and respected. In ACM Conference on Embedded Networked Sensor Systems (Sensys), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Nisarg Raval, Animesh Srivastava, Kiron Lebeck, Landon P. Cox, and Ashwin Machanavajjhala. Markit: Privacy markers for protecting visual secrets. In UPSIDE, Workshop at ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Mathias, R. Benenson, M. Pedersoli, and L. Van Gool. Face detection without bells and whistles. In European Conference on Computer Vision (ECCV), 2014.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Joon Oh, R. Benenson, M. Fritz, and B. Schiele. Person recognition in personal photo collections. In International Conference on Computer Vision (ICCV), 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Bart Goethals, Sven Laur, Helger Lipmaa, and Taneli Mielikainen. On private scalar product computation for privacy-preserving data mining. In 7th Annual International Conference in Information Security and Cryptology (ICISC), 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Andrew Chi-Chih Yao. How to generate and exchange secrets. In 27th Annual Symposium on Foundations of Computer Science (FOCS), 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Terence Sim and Li Zhang. Controllable face privacy. In The 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015.Google ScholarGoogle Scholar
  11. Antonio Criminisi, Patrick Perez, and Kentaro Toyama. Region filling and object removal by exemplar-based image inpainting. In IEEE Transactions on image processing, vol. 13, no. 9, September, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. X. Zhu and D. Ramanan. Face detection, pose estimation and landmark localization in the wild. In Computer Vision and Pattern Recognition (CVPR), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Ning Zhang, Manohar Paluri, Yaniv Taigman, Rob Fergus, and Lubomir Bourdev. Beyond frontal faces: Improving person recognition using multiple cues. In Conference on Computer Vision and Pattern Recognition (CVPR), 2015.Google ScholarGoogle ScholarCross RefCross Ref
  14. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Conference on Neural Information Processing Systems (NIPS). 2012.Google ScholarGoogle Scholar
  15. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. Computer Vision and Pattern Recognition (CVPR), 2009.Google ScholarGoogle ScholarCross RefCross Ref
  16. Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014.Google ScholarGoogle Scholar
  17. Gary B. Huang Erik Learned-Miller. Labeled faces in the wild: Updates and new reporting procedures. Technical Report UM-CS-2014-003, University of Massachusetts, Amherst, May 2014.Google ScholarGoogle Scholar
  18. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Pascal Paillier. Public-key cryptosystems based on composite degree residuosity classes. In Advances in Cryptology (EUROCRYPT), 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Paarijaat Aditya et al. Technical Report: I-Pic: A Platform for Privacy-Compliant Image Capture. http://www.mpi-sws.org/ paditya/papers/ipic-tr.pdf.Google ScholarGoogle Scholar
  21. Yan Huang, Lior Malka, David Evans, and Jonathan Katz. Efficient privacy-preserving biometric identification. In 18th Network and Distributed System Security Conference (NDSS), 2011.Google ScholarGoogle Scholar
  22. Moni Naor and Benny Pinkas. Computationally secure oblivious transfer. In Journal of Cryptology, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Yuval Ishai, Joe Kilian, Kobbi Nissim, and Erez Petrank. Extending oblivious transfers efficiently. In Advances in Cryptology (CRYPTO), 2003.Google ScholarGoogle ScholarCross RefCross Ref
  24. Yehuda Lindell. Fast cut-and-choose based protocols for malicious and covert adversaries. In Advances in Cryptology (CRYPTO), 2013.Google ScholarGoogle ScholarCross RefCross Ref
  25. Project Tango Tablet Development Kit. https://store.google.com/product/project_tango_tablet_development_kit.Google ScholarGoogle Scholar
  26. CUDA. http://www.nvidia.com/object/cuda_home_new.html.Google ScholarGoogle Scholar
  27. Jose-Luis Lisani, Ana-Belen Petro, and Catalina Sbert. Color and Contrast Enhancement by Controlled Piecewise Affine Histogram Equalization. Image Processing On Line, 2:243--265, 2012. http://dx.doi.org/10.5201/ipol.2012.lps-pae.Google ScholarGoogle ScholarCross RefCross Ref
  28. Caffe-Android-Lib. https://github.com/sh1r0/caffe-android-lib.Google ScholarGoogle Scholar
  29. Might Be Evil. http://mightbeevil.org/.Google ScholarGoogle Scholar
  30. A universal labeling tool: Sloth. https://cvhci.anthropomatik.kit.edu/ baeuml/projects/a-universal-labeling-tool-for-computer-vision-sloth/.Google ScholarGoogle Scholar
  31. Monsoon Power Monitor. https://www.msoon.com/LabEquipment/PowerMonitor.Google ScholarGoogle Scholar
  32. Nvidia. Nvidia Shield Tablet K1. https://shield.nvidia.com/tablet/k1.Google ScholarGoogle Scholar
  33. Roberto Hoyle, Robert Templeman, Steven Armes, Denise Anthony, David Crandall, and Apu Kapadia. Privacy behaviors of lifeloggers using wearable cameras. In ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Tamara Denning, Zakariya Dehlawi, and Tadayoshi Kohno. In situ with bystanders of augmented reality glasses: Perspectives on recording and privacy-mediating technologies. In ACM Conference on Human Factors in Computing Systems (CHI), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Jaeyeon Jung and Matthai Philipose. Courteous glass. In UPSIDE, Workshop at ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Loris D'Antoni, Alan Dunn, Suman Jana, Tadayoshi Kohno, Benjamin Livshits, David Molnar, Alexander Moshchuk, Eyal Ofek, Franziska Roesner, Scott Saponas, Margus Veanes, and Helen J. Wang. Operating system support for augmented reality applications. In Workshop on Hot Topics in Operating Systems (HotOS), 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Suman Jana, Arvind Narayanan, and Vitaly Shmatikov. A scanner darkly: Protecting user privacy from perceptual applications. In IEEE Symposium on Security and Privacy, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Suman Jana, David Molnar, Alexander Moshchuk, Alan Dunn, Benjamin Livshits, Helen J. Wang, and Eyal Ofek. Enabling fine-grained permissions for augmented reality applications with recognizers. In Usenix Security Symposium (Usenix Security), 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Christopher Smowton, Jacob R. Lorch, David Molnar, Stefan Saroiu, and Alec Wolman. Zero-effort payments: Design, deployment, and lessons. In Proceedings of the ACM International Joint Conference on Pervasive and UbiquitousComputing (UbiComp), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf. Deepface: Closing the gap to human-level performance in face verification. In Conference on Computer Vision and Pattern Recognition (CVPR), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Describing people: Poselet-based attribute classification. In International Conference on Computer Vision (ICCV), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, and Lubomir D. Bourdev. PANDA: pose aligned networks for deep attribute modeling. In Conference on Computer Vision and Pattern Recognition (CVPR), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Zhou Lingli and Lai Jianghuang. Security algorithm of face recognition based on local binary pattern and random projection. In International Conference on Computational Intelligence (ICCI), 2010.Google ScholarGoogle ScholarCross RefCross Ref
  44. Yongjin Wang and Konstantinos N. Plataniotis. An analysis of random projection for changeable and privacy-preserving biometric verification. IEEE Transactions on Systems, Man, and, Cybernetics: part B: CYBERNETICS, Vol. 40, No. 5, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Per Hallgren, Martin Ochoa, and Andrei Sabelfeld. Innercircle: A parallelizable decentralized privacy-preserving location proximity protocol. In Proceedings of the 13th Annual Conference on Privacy, Security and Trust (PST), 2015.Google ScholarGoogle ScholarCross RefCross Ref

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