Abstract
Biometric data is an inherent representation of a human user, and it would be highly desirable to derive a private key of a public-key cryptographic scheme from a user’s biometric input such that the user does not need to remember any password or carry any device to store the private key and is able to enjoy all benefits of the public-key cryptographic scheme. In this paper, we introduce a notion called fuzzy public-key encryption (FPKE), which is a public-key encryption (PKE) scheme that accepts a piece of fuzzy data (i.e., a noisy version of the original biometric data) as the private key to decrypt the ciphertext. Compared to the traditional PKE scheme where a private key is usually stored in a device (e.g., a USB token), an FPKE scheme does not need to use any device for the storage of the private key. We first define a formal security model for FPKE, and then give generic constructions of FPKE based on the cryptographic primitives of linear sketch and PKE with some special properties.
B. Qin–State Key Laboratory of Cryptology, P.O.Box 5159, Beijing 100878, China.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
For details on the limitations of helper strings, please refer to [9].
References
Connaughton, R., Bowyer, K.W., Flynn, P.J.: Fusion of face and iris biometrics. In: Handbook of Iris Recognition, pp. 219–237. Springer, Heidelberg (2007)
Dodis, Y., Ostrovsky, R., Reyzin, L., Smith, A.D.: Fuzzy extractors: how to generate strong keys from biometrics and other noisy data. SIAM J. Comput. 38(1), 97–139 (2008)
Ellison, C., Schneier, B.: Ten risks of PKI: what you’re not being told about public key infrastructure. Comput. Secur. J. 16(1), 1–7 (2000)
Gamal, T.E.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theor. 31(4), 469–472 (1985)
Kiltz, E.: Chosen-ciphertext security from tag-based encryption. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 581–600. Springer, Heidelberg (2006). doi:10.1007/11681878_30
MacKenzie, P., Reiter, M.K., Yang, K.: Alternatives to non-malleability: definitions, constructions, and applications. In: Naor, M. (ed.) TCC 2004. LNCS, vol. 2951, pp. 171–190. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24638-1_10
Matsuda, T., Takahashi, K., Murakami, T., Hanaoka, G.: Fuzzy signatures: relaxing requirements and a new construction. In: Manulis, M., Sadeghi, A.-R., Schneider, S. (eds.) ACNS 2016. LNCS, vol. 9696, pp. 97–116. Springer, Cham (2016). doi:10.1007/978-3-319-39555-5_6
Sahai, A., Waters, B.: Fuzzy identity-based encryption. In: Cramer, R. (ed.) EUROCRYPT 2005. LNCS, vol. 3494, pp. 457–473. Springer, Heidelberg (2005). doi:10.1007/11426639_27
Takahashi, K., Matsuda, T., Murakami, T., Hanaoka, G., Nishigaki, M.: A signature scheme with a fuzzy private key. In: Malkin, T., Kolesnikov, V., Lewko, A.B., Polychronakis, M. (eds.) ACNS 2015. LNCS, vol. 9092, pp. 105–126. Springer, Cham (2015). doi:10.1007/978-3-319-28166-7_6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Cui, H., Au, M.H., Qin, B., Deng, R.H., Yi, X. (2017). Fuzzy Public-Key Encryption Based on Biometric Data. In: Okamoto, T., Yu, Y., Au, M., Li, Y. (eds) Provable Security. ProvSec 2017. Lecture Notes in Computer Science(), vol 10592. Springer, Cham. https://doi.org/10.1007/978-3-319-68637-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-68637-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68636-3
Online ISBN: 978-3-319-68637-0
eBook Packages: Computer ScienceComputer Science (R0)