Data Privatizer for Biometric Applications and Online Identity Management

  • Giuseppe GarofaloEmail author
  • Davy Preuveneers
  • Wouter Joosen
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 576)


Biometric data embeds information about the user which enables transparent and frictionless authentication. Despite being a more reliable alternative to traditional knowledge-based mechanisms, sharing the biometric template with third-parties raises privacy concerns for the user. Recent research has shown how biometric traces can be used to infer sensitive attributes like medical conditions or soft biometrics, e.g. age and gender. In this work, we investigate a novel methodology for private feature extraction in online biometric authentication. We aim to suppress soft biometrics, i.e. age and gender, while boosting the identification potential of the input trace. To this extent, we devise a min-max loss function which combines a siamese network for authentication and a predictor for private attribute inference. The multi-objective loss function harnesses the output of the predictor through adversarial optimization and gradient flipping to maximize the final gain. We empirically evaluate our model on gait data extracted from accelerometer and gyroscope sensors: our experiments show a drop from 73% to 52% accuracy for gender classification while loosing around 6% in the identity verification task. Our work demonstrates that a better trade-off between privacy and utility in biometric authentication is not only desirable but feasible.



This research is partially funded by the Research Fund KU Leuven. Work for this paper was supported by the European Commission through the H2020 project CyberSec4Europe ( under grant No. 830929. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.


  1. 1.
    Alvi, M., Zisserman, A., Nellåker, C.: Turning a blind eye: explicit removal of biases and variation from deep neural network embeddings. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 556–572. Springer, Cham (2019). Scholar
  2. 2.
    American Academy of Ophthalmology: Evidence mounts that an eye scan may detect early Alzheimer’s disease (2018). Accessed 14 May 2019
  3. 3.
    Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271 (2018)
  4. 4.
    Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of the 1st Conference on Fairness, Accountability and Transparency. Proceedings of Machine Learning Research, vol. 81, pp. 77–91. PMLR, New York, 23–24 February 2018Google Scholar
  5. 5.
    Cohn, J.: Google’s algorithms discriminate against women and people of colour (2019). Accessed 14 May 2019
  6. 6.
    Dantcheva, A., Elia, P., Ross, A.: What else does your biometric data reveal? A survey on soft biometrics. IEEE Trans. Inf. Forensics Secur. 11(3), 441–467 (2016)CrossRefGoogle Scholar
  7. 7.
    Das, A., Dantcheva, A., Bremond, F.: Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 573–585. Springer, Cham (2019). Scholar
  8. 8.
    European Parliament: Regulation (EU) 2016 of the European Parliament and of the Council, on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (2016)Google Scholar
  9. 9.
    Gafurov, D.: A survey of biometric gait recognition: approaches, security and challenges. In: NIK Conference (2007)Google Scholar
  10. 10.
    Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1–35 (2016)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Gomez-Barrero, M., Galbally, J., Rathgeb, C., Busch, C.: General framework to evaluate unlinkability in biometric template protection systems. IEEE Trans. Inf. Forensics Secur. 13(6), 1406–1420 (2018)CrossRefGoogle Scholar
  12. 12.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27, pp. 2672–2680. Curran Associates, Inc. (2014)Google Scholar
  13. 13.
    Hao, K.: This is how AI bias really happens—and why it’s so hard to fix (2019). Accessed 14 May 2019
  14. 14.
    Huang, C., Kairouz, P., Chen, X., Sankar, L., Rajagopal, R.: Context-aware generative adversarial privacy. Entropy 19(12), 656 (2017)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Information technology - Security techniques - Biometric information protection. Standard, International Organization for Standardization (2011)Google Scholar
  16. 16.
    Malekzadeh, M., Clegg, R.G., Cavallaro, A., Haddadi, H.: Mobile sensor data anonymization. In: Proceedings of the International Conference on Internet of Things Design and Implementation, IoTDI 2019, pp. 49–58. ACM (2019)Google Scholar
  17. 17.
    Marsico, M.D., Mecca, A.: A survey on gait recognition via wearable sensors. ACM Comput. Surv. 52(4), 86:1–86:39 (2019)CrossRefGoogle Scholar
  18. 18.
    Matovu, R., Serwadda, A.: Your substance abuse disorder is an open secret! Gleaning sensitive personal information from templates in an EEG-based authentication system. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–7, September 2016Google Scholar
  19. 19.
    Mirjalili, V., Raschka, S., Ross, A.: Gender privacy: an ensemble of semi adversarial networks for confounding arbitrary gender classifiers. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–10, October 2018Google Scholar
  20. 20.
    Mirjalili, V., Raschka, S., Ross, A.: FlowSAN: privacy-enhancing semi-adversarial networks to confound arbitrary face-based gender classifiers. IEEE Access 7, 99735–99745 (2019)CrossRefGoogle Scholar
  21. 21.
    Morales, A., Fiérrez, J., Vera-Rodríguez, R.: SensitiveNets: learning agnostic representations with application to face recognition. CoRR abs/1902.00334 (2019)Google Scholar
  22. 22.
    Mordini, E., Ashton, H.: The transparent body: medical information, physical privacy and respect for body integrity. In: Mordini, E., Tzovaras, D. (eds.) Second Generation Biometrics: The Ethical, Legal and Social Context. The International Library of Ethics, Law and Technology, vol. 11, pp. 257–283. Springer, Dordrecht (2012). Scholar
  23. 23.
    Ngo, T.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., Yagi, Y.: Similar gait action recognition using an inertial sensor. Pattern Recogn. 48(4), 1289–1301 (2015)CrossRefGoogle Scholar
  24. 24.
    Ossia, S.A., Shamsabadi, A.S., Taheri, A., Rabiee, H.R., Lane, N.D., Haddadi, H.: A hybrid deep learning architecture for privacy-preserving mobile analytics. CoRR abs/1703.02952 (2017)Google Scholar
  25. 25.
    Pittaluga, F., Koppal, S., Chakrabarti, A.: Learning privacy preserving encodings through adversarial training. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2019)Google Scholar
  26. 26.
    Rui, Z., Yan, Z.: A survey on biometric authentication: toward secure and privacy-preserving identification. IEEE Access 7, 5994–6009 (2019)CrossRefGoogle Scholar
  27. 27.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823, June 2015Google Scholar
  28. 28.
    Van hamme, T., Garofalo, G., Argones Rúa, E., Preuveneers, D., Joosen, W.: A systematic comparison of age and gender prediction on IMU sensor-based gait traces. Sensors 19(13), 2945 (2019)CrossRefGoogle Scholar
  29. 29.
    Van hamme, T., Preuveneers, D., Joosen, W.: Improving resilience of behaviometric based continuous authentication with multiple accelerometers. In: Livraga, G., Zhu, S. (eds.) DBSec 2017. LNCS, vol. 10359, pp. 473–485. Springer, Cham (2017). Scholar
  30. 30.
    Wan, C., Wang, L., Phoha, V.V.: A survey on gait recognition. ACM Comput. Surv. 51(5), 89:1–89:35 (2018)CrossRefGoogle Scholar
  31. 31.
    Winner, L.: Autonomous Technology: Technics-Out-of-Control as a Theme in Political Thought. MIT Press, Cambridge (1977)Google Scholar
  32. 32.
    Zeitz, C., et al.: Security issues of internet-based biometric authentication systems: risks of man-in-the-middle and BioPhishing on the example of BioWebAuth. In: Security, Forensics, Steganography, and Watermarking of Multimedia Contents, p. 68190R (2008)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  • Giuseppe Garofalo
    • 1
    Email author
  • Davy Preuveneers
    • 1
  • Wouter Joosen
    • 1
  1. 1.imec - DistriNet, KU LeuvenHeverleeBelgium

Personalised recommendations