Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 100 tok/s
Gemini 2.5 Pro 58 tok/s Pro
GPT-5 Medium 29 tok/s
GPT-5 High 29 tok/s Pro
GPT-4o 103 tok/s
GPT OSS 120B 480 tok/s Pro
Kimi K2 215 tok/s Pro
2000 character limit reached

A secure and private ensemble matcher using multi-vault obfuscated templates (2404.05205v2)

Published 8 Apr 2024 in cs.CV

Abstract: Generative AI has revolutionized modern machine learning by providing unprecedented realism, diversity, and efficiency in data generation. This technology holds immense potential for biometrics, including for securing sensitive and personally identifiable information. Given the irrevocability of biometric samples and mounting privacy concerns, biometric template security and secure matching are among the most sought-after features of modern biometric systems. This paper proposes a novel obfuscation method using Generative AI to enhance biometric template security. Our approach utilizes synthetic facial images generated by a Generative Adversarial Network (GAN) as "random chaff points" within a secure vault system. Our method creates n sub-templates from the original template, each obfuscated with m GAN chaff points. During verification, s closest vectors to the biometric query are retrieved from each vault and combined to generate hash values, which are then compared with the stored hash value. Thus, our method safeguards user identities during the training and deployment phases by employing the GAN-generated synthetic images. Our protocol was tested using the AT&T, GT, and LFW face datasets, achieving ROC areas under the curve of 0.99, 0.99, and 0.90, respectively. Our results demonstrate that the proposed method can maintain high accuracy and reasonable computational complexity comparable to those unprotected template methods while significantly enhancing security and privacy, underscoring the potential of Generative AI in developing proactive defensive strategies for biometric systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Z. Rui and Z. Yan, “A survey on biometric authentication: Toward secure and privacy-preserving identification,” IEEE Access, vol. 7, pp. 5994–6009, 2019.
  2. X. Zhou, S. D. Wolthusen, C. Busch, and A. Kuijper, “Feature correlation attack on biometric privacy protection schemes,” in 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, pp. 1061–1065.
  3. A. K. Das, M. Wazid, N. Kumar, A. V. Vasilakos, and J. J. P. C. Rodrigues, “Biometrics-based privacy-preserving user authentication scheme for cloud-based industrial internet of things deployment,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4900–4913, 2018.
  4. M. Martinez-Diaz, J. Fierrez-Aguilar, F. Alonso-Fernandez, J. Ortega-Garcia, and J. Siguenza, “Hill-climbing and brute-force attacks on biometric systems: A case study in match-on-card fingerprint verification,” in Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology, 2006, pp. 151–159.
  5. D. F. Smith, A. Wiliem, and B. C. Lovell, “Face recognition on consumer devices: Reflections on replay attacks,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 736–745, 2015.
  6. I. ur Rehman, “Facebook-cambridge analytica data harvesting: What you need to know,” Library Philosophy and Practice, pp. 1–11, 2019.
  7. O. F. Segun and F. B. Olawale, “Healthcare data breaches: Biometric technology to the rescue,” Int. Res. J. Eng. Technol., vol. 4, no. 11, pp. 946–950, 2017.
  8. P. Singh, “Aadhaar and data privacy: biometric identification and anxieties of recognition in india,” Information, Communication & Society, vol. 24, no. 7, pp. 978–993, 2021. [Online]. Available: https://doi.org/10.1080/1369118X.2019.1668459
  9. T. H. Journal, “Healthcare data breach statistics,” 2024. [Online]. Available: https://www.hipaajournal.com/healthcare-data-breach-statistics/
  10. Y. Wang and K. 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, pp. 1280–1293, 2010.
  11. B. Bortolato, M. Ivanovska, P. Rot, J. Križaj, P. Terhörst, N. Damer, P. Peer, and V. Štruc, “Learning privacy-enhancing face representations through feature disentanglement,” in 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), 2020, pp. 495–502.
  12. U. Uludag and A. Jain, “Securing fingerprint template: Fuzzy vault with helper data,” in 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06), 2006, pp. 163–163.
  13. E. Abdellatef, N. A. Ismail, S. E. S. Abd Elrahman, K. N. Ismail, M. Rihan, A. El-Samie, and E. Fathi, “Cancelable multi-biometric recognition system based on deep learning,” The Visual Computer, vol. 36, no. 6, pp. 1097–1109, 2020.
  14. J. Bringer, H. Chabanne, and A. Patey, “Privacy-preserving biometric identification using secure multiparty computation: An overview and recent trends,” IEEE Signal Processing Magazine, vol. 30, no. 2, pp. 42–52, 2013.
  15. K. Simoens, P. Tuyls, and B. Preneel, “Privacy weaknesses in biometric sketches,” in 2009 30th IEEE Symposium on Security and Privacy, 2009, pp. 188–203.
  16. E. Newton, L. Sweeney, and B. Malin, “Preserving privacy by de-identifying face images,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 2, pp. 232–243, 2005.
  17. I. Natgunanathan, A. Mehmood, Y. Xiang, G. Beliakov, and J. Yearwood, “Protection of privacy in biometric data,” IEEE Access, vol. 4, pp. 880–892, 2016.
  18. K. Karabina and A. Robinson, “Revisiting the false acceptance rate attack on biometric visual cryptographic schemes,” in Information Theoretic Security: 9th International Conference, ICITS 2016, Tacoma, WA, USA, August 9-12, 2016, Revised Selected Papers 9.   Springer, 2016, pp. 114–125.
  19. A. K. Jindal, S. Chalamala, and S. K. Jami, “Face template protection using deep convolutional neural network,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018, pp. 575–5758.
  20. O. Delgado-Mohatar, J. Fierrez, R. Tolosana, and R. Vera-Rodriguez, “Biometric template storage with blockchain: A first look into cost and performance tradeoffs,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2829–2837.
  21. A. Morales, J. Fierrez, R. Vera-Rodriguez, and R. Tolosana, “Sensitivenets: Learning agnostic representations with application to face images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 6, pp. 2158–2164, 2021.
  22. P. Terhörst, D. Fährmann, N. Damer, F. Kirchbuchner, and A. Kuijper, “On soft-biometric information stored in biometric face embeddings,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 4, pp. 519–534, 2021.
  23. S. P. Singh and S. Tiwari, “A dual multimodal biometric authentication system based on woa-ann and ssa-dbn techniques,” Sci, vol. 5, no. 1, p. 10, 2023.
  24. K. Sasireka and R. Rajesh, “Dual biometric authentication scheme for privacy protection,” in 2014 International Conference on Communication and Network Technologies, 2014, pp. 105–108.
  25. Y. Dodis, L. Reyzin, and A. Smith, “Fuzzy extractors: How to generate strong keys from biometrics and other noisy data,” in International conference on the theory and applications of cryptographic techniques.   Springer, 2004, pp. 523–540.
  26. Y. Dodis, R. Ostrovsky, L. Reyzin, and A. Smith, “Fuzzy extractors: How to generate strong keys from biometrics and other noisy data,” SIAM journal on computing, vol. 38, no. 1, pp. 97–139, 2008.
  27. E.-C. Chang and Q. Li, “Hiding secret points amidst chaff,” in Annual International Conference on the Theory and Applications of Cryptographic Techniques.   Springer, 2006, pp. 59–72.
  28. A. L. Cambridge, “The database of faces,” cam-orl.co.uk/facedatabase.html.
  29. A. V. Nefian, “Georgia tech face database,” 1999. [Online]. Available: http://www.anefian.com/research/face_reco.htm
  30. H. V. Nguyen and L. Bai, “Cosine similarity metric learning for face verification,” in Asian conference on computer vision.   Springer, 2010, pp. 709–720.
  31. T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4401–4410.
  32. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in Thirty-first AAAI conference on artificial intelligence, 2017.
  33. Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “Vggface2: A dataset for recognising faces across pose and age,” in 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018).   IEEE, 2018, pp. 67–74.
  34. T. Esler, “facenet-pytorch,” https://github.com/timesler/facenet-pytorch, 2020.
  35. D. Yi, Z. Lei, S. Liao, and S. Z. Li, “Learning face representation from scratch,” arXiv preprint arXiv:1411.7923, 2014.
  36. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE signal processing letters, vol. 23, no. 10, pp. 1499–1503, 2016.
  37. J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4690–4699.
  38. Z. Zhu, G. Huang, J. Deng, Y. Ye, J. Huang, X. Chen, J. Zhu, T. Yang, J. Lu, D. Du et al., “Webface260m: A benchmark unveiling the power of million-scale deep face recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10 492–10 502.
  39. X. An, X. Zhu, Y. Gao, Y. Xiao, Y. Zhao, Z. Feng, L. Wu, B. Qin, M. Zhang, D. Zhang et al., “Partial fc: Training 10 million identities on a single machine,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 1445–1449.
  40. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  41. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” University of Massachusetts, Amherst, Tech. Rep. 07-49, October 2007.
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

List Streamline Icon: https://streamlinehq.com Explore 10 Community Prompts
Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now