Abstract
With the proliferation of inexpensive video surveillance and face recognition technologies, it is increasingly possible to track and match people as they move through public spaces. To protect the privacy of subjects visible in video sequences, prior research suggests using ad hoc obfuscation methods, such as blurring or pixelation of the face. However, there has been little investigation into how obfuscation influences the usability of images, such as for classification tasks. In this paper, we demonstrate that at high obfuscation levels, ad hoc methods fail to preserve utility for various tasks, whereas at low obfuscation levels, they fail to prevent recognition. To overcome the implied tradeoff between privacy and utility, we introduce a new algorithm, k-Same-Select, which is a formal privacy protection schema based on k-anonymity that provably protects privacy and preserves data utility. We empirically validate our findings through evaluations on the FERET database, a large real world dataset of facial images.
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References
Belsie, L.: The eyes have it - for now. Christian Science Monitor (November 7, 2002)
Sweeney, L.: Surveillance of surveillance camera watch project (2004)
Bowyer, K.: Face recognition technology and the security vs. privacy tradeoff. IEEE Technology and Society, 9–20 (2004)
Crowley, J., Coutaz, J., Berard, F.: Things that see. Communications of the ACM 43, 54–64 (2000)
Neustaedter, C., Greenberg, S.: Balancing privacy and awareness in home media spaces. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864. Springer, Heidelberg (2003)
Newton, E., Sweeney, L., Malin, B.: Preserving privacy by de-identifying facial images. IEEE Transactions on Knowledge and Data Engineering 17, 232–243 (2005)
Boyle, M., Edwards, C., Greenberg, S.: The effects of filtered video on awareness and privacy. In: ACM Conference on Computer Supported Cooperative Work, Philadelphia, PA, pp. 1–10 (2000)
Greenberg, S., Kuzuoka, H.: Using digital but physical surrogates to mediate awareness, communication, and privacy in media spaces. Personal Technologies 4 (2000)
Hudson, S., Smith, I.: Techniques for addressing fundamental privacy and disruption tradeoffs in awareness support systems. In: ACM Conference on Computer Supported Cooperative Work, Boston, MA, pp. 1–10 (1996)
Neustaedter, C., Greenberg, S., Boyle, M.: Blur filtration fails to preserve privacy for home-based video conferencing. ACM Transactions on Computer Human Interactions (TOCHI) (in press, 2005)
Zhao, Q., Stasko, J.: Evaluating image filtering based techniques in media space applications. In: ACM Conference on Computer Supported Cooperative Work, Seattle, WA, pp. 11–18 (1998)
Senior, A., Pankati, S., Hampapur, A., Brown, L., Tian, Y.L., Ekin, A.: Blinkering surveillance: enabling video surveillance privacy through computer vision. IBM Research Report RC22886 (W0308-109), T.J. Watson Research Center, Yorktown Heights (2003)
Alexander, J., Kenny, S.: Engineering privacy in public: Confounding face recognition. In: Dingledine, R. (ed.) PET 2003. LNCS, vol. 2760, pp. 88–106. Springer, Heidelberg (2003)
Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness, and Knowledge-Based Systems 10, 557–570 (2002)
Kanade, T.: Computer Recognition of Human Faces. Birkhauser, Basel (1977)
Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: a literature survey. ACM Computing Surveys 35, 399–458 (2003)
Gross, R., Shi, J., Cohn, J.: Quo vadis face recognition? In: Third Workshop on Empirical Evaluation Methods in Computer Vision (2001)
Blackburn, D., Bone, M., Philips, P.: Facial recognition vendor test 2000: evaluation report (2000)
Phillips, P.J., Grother, P., Ross, J.M., Blackburn, D., Tabassi, E., Bone, M.: Face recognition vendor test 2002: evaluation report (2003)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Penev, P., Atick, J.: Local feature analysis: A general statistical theory for object representation (1996)
Gross, R., Yang, J., Waibel, A.: Face recognition in a meeting room. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France (2000)
Phillips, P.J., Wechsler, H., Huang, J.S., Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16, 295–306 (1998)
Sonka, M., Hlavac, V., Boyle, R.: Image processing, analysis, and machine vision. 2nd edn. Brooks/Cole (1999)
Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines. Cambridge University Press, Cambridge (2000)
Seeger, M.: Learning with labeled and unlabeled data. Technical report, University of Edinburgh (2002)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: COLT: Proceedings of the Workshop on Computational Learning Theory. Morgan Kaufmann Publishers, San Francisco (1998)
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Gross, R., Airoldi, E., Malin, B., Sweeney, L. (2006). Integrating Utility into Face De-identification. In: Danezis, G., Martin, D. (eds) Privacy Enhancing Technologies. PET 2005. Lecture Notes in Computer Science, vol 3856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11767831_15
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DOI: https://doi.org/10.1007/11767831_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34745-3
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