TR2010-004

Privacy and Security of Features Extracted from Minutiae Aggregates


    •  Nagar, A., Rane, S.D., Vetro, A., "Privacy and Security of Features Extracted from Minutiae Aggregates", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2010.
      BibTeX TR2010-004 PDF
      • @inproceedings{Nagar2010mar,
      • author = {Nagar, A. and Rane, S.D. and Vetro, A.},
      • title = {Privacy and Security of Features Extracted from Minutiae Aggregates},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2010,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2010-004}
      • }
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  • Research Area:

    Information Security

Abstract:

This paper describes our recent analysis on the security and privacy of biometric feature vectors obtained from fingerprint minutiae. A large number of contiguous regions (cuboids) are selected at random in the minutiae space, and several new features are extracted from the minutiae inside each such cuboid. Specifically, the features are extracted from the average minutia coordinate within a cuboid, the standard deviation of the minutiae coordinates, and the aggregate wall distance, i.e., the sum of distance of each minutia from the boundary of the cuboids. In terms of matching performance on a public database, the feature vectors provide an equal error rate of 3% even if the imposter is allowed to use the same local patches as the genuine user. Performance within a secure biometrics framework is evaluated by applying an LDPC code to the feature vectors and storing only the syndrome at the access control device, for use in authentication. The paper concludes with a discussion on methods to analyze security and privacy of biometric systems that use such local-aggregate-based feature vectors in a secure biometric recognition framework. This discussion highlights security attacks via template injection, spoofing, and cancelability compromises and also considers the difficulty of privacy attacks via template inversion.

 

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      Where: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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      Brief
      • The papers "Privacy and Security of Features Extracted from Minutiae Aggregates" by Nagar, A., Rane, S.D. and Vetro, A., "Hiding Information Inside Structured Shapes" by Das, S., Rane, S.D. and Vetro, A., "Ultrasonic Sensing for Robust Speech Recognition" by Srinivasan, S., Raj, B. and Ezzat, T., "Reconstruction of Sparse Signals from Distorted Randomized Measurements" by Boufounos, P.T., "Disparity Search Range Estimation: Enforcing Temporal Consistency" by Min, D., Yea, S., Arican, Z. and Vetro, A., "Synthesizing Speech from Doppler Signals" by Toth, A.R., Raj, B., Kalgaonkar, K. and Ezzat, T., "Spectrogram Dimensionality Reduction with Independence Constraints" by Wilson, K.W. and Raj, B., "Robust Regression using Sparse Learning for High Dimensional Parameter Estimation Problems" by Mitra, K., Veeraraghavan, A.N. and Chellappa, R. and "Subword Unit Approaches for Retrieval by Voice" by Gouvea, E., Ezzat, T. and Raj, B. were presented at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
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