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1. A Ternary Fuzzy Extractor For Efficient Cryptographic Key Generation 3
2. A Ternary Fuzzy Extractor For Efficient Cryptographic Key Generation X
3. A Ternary Fuzzy Extractor For Efficient Cryptographic Key Generation 1
4. A Ternary Fuzzy Extractor For Efficient Cryptographic Key Generation 2

Fuzzy extractor structure using serially concatenated BCH-Polar codes is proposed to generate reproducible keys from a ReRAM-based ternary-state Physical Unclonable Functions (PUFs) for device authentication and secret key generation. The main concern in deploying PUF-based key generation methods is the leakage of information about. Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data∗ Yevgeniy Dodis† Rafail Ostrovsky‡ Leonid Reyzin§ Adam Smith¶ January 20, 2008 Abstract We provide formal definitions and efficient secure techniques for.turning noisy information into keys usable for any cryptographic application, and, in particular. A fuzzy extractor structure using serially concatenated BCH-Polar codes is proposed to generate reproducible keys from a ReRAM-based ternary-state Physical Unclonable Functions (PUFs) for device authentication and secret key generation. The main concern in deploying PUF-based key generation methods is the leakage of information about the secret keys from the publicly available helper data.

1. Barker, E., & Kelsey, J. (2012). Recommendation for random number generation using deterministic random bit generators. NIST special publication 800-90A. http://csrc.nist.gov/publications/nistpubs/800-90A/SP800-90A.pdf.
2. Berlekamp, E. (1965). On decoding binary Bose-Chadhuri-Hocquenghem codes. IEEE Transactions on Information Theory, 11(4), 577–579.MathSciNetCrossRefzbMATHGoogle Scholar
3. Bogdanov, A., Knežević, M., Leander, G., Toz, D., Varici, K., & Verbauwhede, I. (2011). SPONGENT: a lightweight hash function. In Lecture notes in computer science (LNCS): Vol.6917. Workshop on cryptographic hardware and embedded systems—CHES 2011 (pp. 312–325). Berlin: Springer.CrossRefGoogle Scholar
4. Bösch, C., Guajardo, J., Sadeghi, A.-R., Shokrollahi, J., & Tuyls, P. (2008). Efficient helper data key extractor on FPGAs. In Lecture notes in computer science (LNCS): Vol.5154. Workshop on cryptographic hardware and embedded systems—CHES 2008 (pp. 181–197). Berlin: Springer.CrossRefGoogle Scholar
5. Burton, H. (1971). Inversionless decoding of binary BCH codes. IEEE Transactions on Information Theory, 17(4), 464–466.CrossRefzbMATHGoogle Scholar
6. Carter, J. L., & Wegman, M. N. (1977). Universal classes of hash functions. In ACM symposium on theory of computing—STOC 1977 (pp. 106–112). New York: ACM.Google Scholar
7. Chien, R. (1964). Cyclic decoding procedures for Bose-Chaudhuri-Hocquenghem codes. IEEE Transactions on Information Theory, 10(4), 357–363.MathSciNetCrossRefzbMATHGoogle Scholar
8. Dodis, Y., Reyzin, L., & Smith, A. (2004). Fuzzy extractors: how to generate strong keys from biometrics and other noisy data. In Lecture notes in computer science (LNCS): Vol.3027. Advances in cryptology—EUROCRYPT 2004 (pp. 523–540). Berlin: Springer.CrossRefGoogle Scholar
9. Dodis, Y., Ostrovsky, R., Reyzin, L., & Smith, A. (2008). Fuzzy extractors: how to generate strong keys from biometrics and other noisy data. SIAM Journal on Computing, 38(1), 97–139.MathSciNetCrossRefzbMATHGoogle Scholar
10. Eastlake, D., Schiller, J., & Crocker, S. (2005). Randomness requirements for security. IETF RFC 4086. http://www.ietf.org/rfc/rfc4086.txt.
11. Ferguson, N., & Schneier, B. (2003). Practical cryptography. New York: Wiley.Google Scholar
12. Gallager, R. G. (1962). Low density parity-check codes. IRE Transactions on Information Theory, 8, 21–28.MathSciNetCrossRefzbMATHGoogle Scholar
13. Guajardo, J., Kumar, S. S., Schrijen, G. J., & Tuyls, P. (2007). FPGA intrinsic PUFs and their use for IP protection. In Lecture notes in computer science (LNCS): Vol.4727. Workshop on cryptographic hardware and embedded systems—CHES 2007 (pp. 63–80). Berlin: Springer.CrossRefGoogle Scholar
14. Gutmann, P. (2004). Cryptographic security architecture. Berlin: Springer.zbMATHGoogle Scholar
15. Kelsey, J., Schneier, B., & Ferguson, N. (1999). Yarrow-160: notes on the design and analysis of the Yarrow cryptographic pseudorandom number generator. In Lecture notes in computer science (LNCS): Vol.1758. International workshop on selected areas in cryptography—SAC 1999 (pp. 13–33). Berlin: Springer.CrossRefGoogle Scholar
16. Lenstra, A. K., Hughes, J. P., Augier, M., Bos, J. W., Kleinjung, T., & Wachter, C. (2012). Ron was wrong, Whit is right. Cryptology ePrint Archive, Report 2012/064.Google Scholar
17. Linnartz, J.-P., & Tuyls, P. (2003). New shielding functions to enhance privacy and prevent misuse of biometric templates. In Lecture notes in computer science (LNCS): Vol.2688. International conference on audio- and video-based biometric person authentication—AVBPA 2003 (pp. 393–402). Berlin: Springer.CrossRefGoogle Scholar
18. Maes, R., Tuyls, P., & Verbauwhede, I. (2009). Low-overhead implementation of a soft decision helper data algorithm for SRAM PUFs. In Lecture notes in computer science (LNCS): Vol.5747. Workshop on cryptographic hardware and embedded systems—CHES 2009 (pp. 332–347). Berlin: Springer.Google Scholar
19. Maes, R., Tuyls, P., & Verbauwhede, I. (2009). Soft decision helper data algorithm for SRAM PUFs. In IEEE international symposium on information theory—ISIT 2009 (pp. 2101–2105). New York: IEEE.CrossRefGoogle Scholar
20. Maes, R., Van Herrewege, A., & Verbauwhede, I. (2012). PUFKY: a fully functional PUF-based cryptographic key generator. In Lecture notes in computer science (LNCS): Vol.7428. Workshop on cryptographic hardware and embedded systems—CHES 2012. Berlin: Springer.Google Scholar
21. Maiti, A., Casarona, J., McHale, L., & Schaumont, P. (2010). A large scale characterization of RO-PUF. In IEEE international symposium on hardware-oriented security and trust—HOST 2010 (pp. 94–99). New York: IEEE.CrossRefGoogle Scholar
22. Massey, J. (1969). Shift-register synthesis and BCH decoding. IEEE Transactions on Information Theory, 15(1), 122–127.MathSciNetCrossRefzbMATHGoogle Scholar
23. Nisan, N., & Zuckerman, D. (1996). Randomness is linear in space. Journal of Computer and System Sciences, 52(1), 43–52.MathSciNetCrossRefzbMATHGoogle Scholar
24. Schnabl, G., & Bossert, M. (1995). Soft-decision decoding of Reed-Muller codes as generalized multiple concatenated codes. IEEE Transactions on Information Theory, 41(1), 304–308.CrossRefzbMATHGoogle Scholar
25. Silverman, R., & Balser, M. (1954). Coding for constant-data-rate systems-part I. A new error-correcting code. Proceedings of the IRE, 42(9), 1428–1435.CrossRefGoogle Scholar
26. Tarnovsky, C. (2010). Deconstructing a ‘Secure’ processor. Talk at Black Hat Federal 2010. http://www.blackhat.com/presentations/bh-dc-10/Tarnovsky_Chris/BlackHat-DC-2010-Tarnovsky-DASP-slides.pdf.
27. Torrance, R., & James, D. (2009). The state-of-the-art in IC reverse engineering. In Lecture notes in computer science (LNCS): Vol.5747. Workshop on cryptographic hardware and embedded systems—CHES 2009 (pp. 363–381). Berlin: Springer.CrossRefGoogle Scholar
28. Tuyls, P., & Batina, L. (2006). RFID-tags for anti-counterfeiting. In Lecture notes in computer science (LNCS): Vol.3860. Topics in cryptology: cryptographers’ track of the RSA conference—CT-RSA 2006 (pp. 115–131). Berlin: Springer.CrossRefGoogle Scholar
29. Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2), 260–269.CrossRefzbMATHGoogle Scholar
30. Yu, M.-D. M., M’Raihi, D., Sowell, R., & Devadas, S. (2011). Lightweight and secure PUF key storage using limits of machine learning. In Lecture notes in computer science (LNCS): Vol.6917. Workshop on cryptographic hardware and embedded systems—CHES 2011 (pp. 358–373). Berlin: Springer.CrossRefGoogle Scholar

Cryptographic Key Generation from Physically Unclonable Functions Data

The following code is implemented in MatLab.

The generation procedure takes the PUF Data1 as input and generates the Key1 and helper data(which is totally random).The reproduction procedure takes the PUF Data2 and helper data as input and generates Key2.If Key1=Key2 both of the PUF's have been generated from the same device.

The input is taken to be random for now, but you should add your own PUF inputs here.

A Ternary Fuzzy Extractor For Efficient Cryptographic Key Generation 3

For the full project with test data input and analysis using PUF's, and more explanation and detailed working e-mail me atkaranahujax@gmail.com .

REFERENCES

Y. Dodis, R. Ostrovsky, L. Reyzin and A. Smith, “Fuzzy Extractors:How to Generate Strong Keys from Biometrics and Other Noisy Data,”(A preliminary version of this paper appeared in Eurocrypt 2004) SIAMJ. Comput., 38(1), pp. 97–139, 2008.

Hyunho Kang, Yohei Hori, Toshihiro Katashita, Manabu Hagiwara, Keiichi Iwamura,'Cryptographic Key Generation from PUF Data Using Efficient Fuzzy Extractors',2014

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A Ternary Fuzzy Extractor For Efficient Cryptographic Key Generation X

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A Ternary Fuzzy Extractor For Efficient Cryptographic Key Generation 1

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