Julien Bringer
ABSTRACT
At WAHC'13, Bringer et al. introduced a protocol called SHADE for secure and efficient Hamming distance computation using oblivious transfer only. In this paper, we introduce a generalization of the SHADE protocol, called GSHADE, that enables privacy-preserving computation of several distance metrics, including (normalized) Hamming distance, Euclidean distance, Mahalanobis distance, and scalar product. GSHADE can be used to efficiently compute one-to-many biometric identification for several traits (iris, face, fingerprint) and benefits from recent optimizations of oblivious transfer extensions. GSHADE allows identification against a database of 1000 Eigenfaces in 1.28 seconds and against a database of 10000 IrisCodes in 17.2 seconds which is more than 10 times faster than previous works.
- G. Asharov, Y. Lindell, T. Schneider, and M. Zohner. More efficient oblivious transfer and extensions for faster secure computation. In Computer and Communications Security (CCS) , pages 535--548. ACM, 2013. Code available at http://encrypto.de/code/OTExtension. Google Scholar
Digital Library
- AT&T Laboratories Cambridge. The database of faces. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.Google Scholar
- M. Barni, T. Bianchi, D. Catalano, M. Di Raimondo, R. Donida Labati, P. Failla, D. Fiore, R. Lazzeretti, V. Piuri, F. Scotti, and A. Piva. Privacy-preserving fingercode authentication. In ACM workshop on Multimedia and Security (MMSEC), pages 231--240. ACM, 2010. Google ScholarDigital Library
- P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In European Conference on Computer Vision (ECCV), volume 1064 of LNCS, pages 43--58. Springer, 1996. Google ScholarDigital Library
- M. Blanton and P. Gasti. Secure and efficient protocols for iris and fingerprint identification. In European Symposium on Research in Computer Security (ESORICS) , volume 6879 of LNCS, pages 190--209. Springer, 2011. Google ScholarDigital Library
- D. Bogdanov, R. Talviste, and J. Willemson. Deploying secure multi-party computation for financial data analysis. In Financial Cryptography (FC), volume 7397 of LNCS, pages 57--64. Springer, 2012.Google Scholar
- P. Bogetoft, D. L. Christensen, I. Damgård, M. Geisler, T. P. Jakobsen, M. Króigaard, J. D. Nielsen, J. B. Nielsen, K. Nielsen, J. Pagter, M. I. Schwartzbach, and T. Toft. Secure multiparty computation goes live. In Financial Cryptography (FC), volume 5628 of LNCS, pages 325--343. Springer, 2009. Google ScholarDigital Library
- J. Bringer, H. Chabanne, and A. Patey. Privacy-preserving biometric identification using secure multiparty computation: An overview and recent trends. IEEE Signal Processing Magazine, 30(2):42--52, 2013.Google ScholarCross Ref
- J. Bringer, H. Chabanne, and A. Patey. SHADE: Secure HAmming DistancE computation from oblivious transfer. In Workshop on Applied Homomorphic Cryptography (WAHC), volume 7862 of LNCS, pages 164--176. Springer, 2013.Google ScholarCross Ref
- J. Bringer, M. Favre, H. Chabanne, and A. Patey. Faster secure computation for biometric identification using filtering. In IAPR International Conference on Biometrics (ICB), pages 257--264. IEEE, 2012.Google ScholarCross Ref
- Carnegie Mellon University. The CMU Multi-PIE face database. http://www.multipie.org.Google Scholar
- S. G. Choi, K.-W. Hwang, J. Katz, T. Malkin, and D. Rubenstein. Secure multi-party computation of Boolean circuits with applications to privacy in on-line marketplaces. In Cryptographers' Track at the RSA Conference (CT-RSA), volume 7178 of LNCS, pages 416--432. Springer, 2012. Code available at http://www.ee.columbia.edu/~kwhwang/projects/gmw.html. Google ScholarDigital Library
- R. Cramer, I. Damgård, and J. B. Nielsen. Multiparty computation from threshold homomorphic encryption. In EUROCRYPT, volume 2045 of LNCS, pages 280--300. Springer, 2001. Google ScholarDigital Library
- E. D. Cristofaro and G. Tsudik. Practical private set intersection protocols with linear complexity. In Financial Cryptography (FC), volume 6052 of LNCS, pages 143--159. Springer, 2010. Google ScholarDigital Library
- I. Damgård, M. Geisler, and M. Krøigaard. Efficient and secure comparison for on-line auctions. In Australasian Conference on Information Security and Privacy (ACISP), volume 4586 of LNCS, pages 416--430. Springer, 2007. Google ScholarDigital Library
- J. Daugman. How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):21--30, 2004. Google ScholarDigital Library
- C. Dong, L. Chen, and Z. Wen. When private set intersection meets big data: An efficient and scalable protocol. In Computer and Communications Security (CCS), pages 789--800. ACM, 2013. Google ScholarDigital Library
- Z. Erkin, M. Franz, J. Guajardo, S. Katzenbeisser, I. Lagendijk, and T. Toft. Privacy-preserving face recognition. In Privacy Enhancing Technologies Symposium (PETS), volume 5672 of LNCS, pages 235--253. Springer, 2009. Google ScholarDigital Library
- S. Even, O. Goldreich, and A. Lempel. A randomized protocol for signing contracts. In CRYPTO, pages 205--210. Springer, 1982.Google Scholar
- C. Gentry. Fully homomorphic encryption using ideal lattices. In Symposium on Theory of Computing (STOC), pages 169--178. ACM, 2009. Google ScholarDigital Library
- O. Goldreich, S. Micali, and A. Wigderson. How to play any mental game or a completeness theorem for protocols with honest majority. In Symposium on Theory of Computing (STOC), pages 218--229. ACM, 1987. Google ScholarDigital Library
- R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker. Multi-PIE. Image Vision and Computing, 28(5):807--813, 2010. Google ScholarDigital Library
- M. Günther, R. Wallace, and S. Marcel. An open source framework for standardized comparisons of face recognition algorithms. In Benchmarking Facial Image Analysis Technologies (BeFIT), volume 7585 of LNCS, pages 547--556. Springer, 2012. Google ScholarDigital Library
- C. Hazay and Y. Lindell. Efficient Secure Two-Party Protocols - Techniques and Constructions. Information Security and Cryptography. Springer, 2010. Google ScholarDigital Library
- W. Henecka, S. Kögl, A.-R. Sadeghi, T. Schneider, and I. Wehrenberg. TASTY: Tool for Automating Secure Two-partY computations. In Computer and Communications Security (CCS), pages 451--462, 2010. Google ScholarDigital Library
- Y. Huang, D. Evans, and J. Katz. Private set intersection: Are garbled circuits better than custom protocols? In Network and Distributed System Security Symposium (NDSS). The Internet Society, 2012.Google Scholar
- Y. Huang, D. Evans, J. Katz, and L. Malka. Faster secure two-party computation using garbled circuits. In USENIX Security Symposium. USENIX Association, 2011. Google ScholarDigital Library
- Y. Huang, L. Malka, D. Evans, and J. Katz. Efficient privacy-preserving biometric identification. In Network and Distributed System Security Symposium (NDSS). The Internet Society, 2011.Google Scholar
- Idiap Research Institute. Face recognition library. https://pypi.python.org/pypi/facereclib.Google Scholar
- Y. Ishai, J. Kilian, K. Nissim, and E. Petrank. Extending oblivious transfers efficiently. In CRYPTO, volume 2729 of LNCS, pages 145--161. Springer, 2003.Google Scholar
- A. K. Jain, S. Prabhakar, L. Hong, and S. Pankanti. FingerCode: A filterbank for fingerprint representation and matching. In Computer Vision and Pattern Recognition (CVPR) , pages 187--193. IEEE, 1999.Google ScholarCross Ref
- V. Kolesnikov and R. Kumaresan. Improved OT extension for transferring short secrets. In CRYPTO, volume 8043 of LNCS, pages 54--70. Springer, 2013.Google Scholar
- V. Kolesnikov and T. Schneider. Improved garbled circuit: Free XOR gates and applications. In International Colloquium on Automata, Languages and Programming (ICALP) , volume 5126 of LNCS, pages 486--498. Springer, 2008. Google ScholarDigital Library
- S. Z. Li and A. K. Jain, editors. Encyclopedia of Biometrics. Springer, 2009. Google ScholarDigital Library
- Y. Luo, S.-C. S. Cheung, T. Pignata, R. Lazzeretti, and M. Barni. An efficient protocol for private iris-code matching by means of garbled circuits. In International Conference on Image Processing (ICIP), pages 2653--2656. IEEE, 2012.Google ScholarCross Ref
- M. Naor and B. Pinkas. Efficient oblivious transfer protocols. In Symposium On Discrete Algorithms (SODA), pages 448--457. ACM/SIAM, 2001. Google ScholarDigital Library
- M. Naor, B. Pinkas, and R. Sumner. Privacy preserving auctions and mechanism design. In Conference on Electronic Commerce (EC), pages 129--139. ACM, 1999. Google ScholarDigital Library
- M. Osadchy, B. Pinkas, A. Jarrous, and B. Moskovich. SCiFI - a system for secure face identification. In IEEE Symposium on Security and Privacy (S&P), pages 239--254. IEEE, 2010. Google ScholarDigital Library
- P. Paillier. Public-key cryptosystems based on composite degree residuosity classes. In EUROCRYPT, volume 1592 of LNCS, pages 223--238. Springer, 1999. Google ScholarDigital Library
- M. O. Rabin. How to exchange secrets with oblivious transfer, TR-81 edition, 1981. Aiken Computation Lab, Harvard University.Google Scholar
- A.-R. Sadeghi, T. Schneider, and I. Wehrenberg. Efficient privacy-preserving face recognition. In International Conference on Information Security and Cryptology (ICISC), volume 5984 of LNCS, pages 229--244. Springer, 2009. Google ScholarDigital Library
- T. Schneider and M. Zohner. GMW vs. Yao? Efficient secure two-party computation with low depth circuits. In Financial Cryptography (FC), volume 7859 of LNCS, pages 275--292. Springer, 2013.Google Scholar
- S. F. Shahandashti, R. Safavi-Naini, and P. Ogunbona. Private fingerprint matching. In Australasian Conference on Information Security and Privacy (ACISP), volume 7372 of LNCS, pages 426--433. Springer, 2012. Google ScholarDigital Library
- M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71--86, 1991. Google ScholarDigital Library
- A. C.-C. Yao. How to generate and exchange secrets (extended abstract). In Foundations of Computer Science (FOCS), pages 162--167. IEEE, 1986. Google ScholarDigital Library
Index Terms
- GSHADE: faster privacy-preserving distance computation and biometric identification
Recommendations
Boosting GSHADE Capabilities: New Applications and Security in Malicious Setting
SACMAT '16: Proceedings of the 21st ACM on Symposium on Access Control Models and TechnologiesThe secure two-party computation (S2PC) protocols SHADE and GSHADE have been introduced by Bringer et al. in the last two years. The protocol GSHADE permits to compute different distances (Hamming, Euclidean, Mahalanobis) quite efficiently and is one of ...
A privacy-preserving cancelable iris template generation scheme using decimal encoding and look-up table mapping
Biometric-based recognition systems have overcome passive issues of traditional human authentication systems. However, security theft and privacy invasion are two passive issues that still persist in the effective deployment of biometric-based ...
Blind authentication: a secure crypto-biometric verification protocol
Concerns on widespread use of biometric authentication systems are primarily centered around template security, revocability, and privacy. The use of cryptographic primitives to bolster the authentication process can alleviate some of these concerns as ...
Comments