Abstract
In this chapter we consider the design of fingerprints for the purpose of authenticating a message. We begin with a background discussion of fingerprinting and related ideas, progressing to a communications point of view. Fingerprint embedding for message authentication is motivated by the desire to make an authentication tag less accessible to an eavesdropper. We consider metrics for good fingerprint design, and apply these to develop an embedding scheme for wireless communications. Wireless software defined radio experiments validate the theory and demonstrate the applicability of our approach.
Keywords
- Channel State Information
- False Alarm Probability
- Channel Estimation Error
- Perfect Channel State Information
- Physically Unclonable Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ettus Research—Product Detail (2013). https://www.ettus.com/product/details/USRP-PKG
Chen, M., Fridrich, J., Goljan, M., Lukas, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008)
Coldrey, M., Bohlin, P.: Training-based MIMO systems-part I: performance comparison. IEEE Trans. Signal Process. 55(11), 5464–5476 (2007). doi:10.1109/TSP.2007.896107
Cover, T., Thomas, J.: Elements of Information Theory. Wiley-Interscience (1991)
Danev, B., Luecken, H., Čapkun, S., Defrawy, K.: Attacks on physical-layer identification. In: WiSec’10: Proceedings of the 3th ACM Conference on Wireless Network Security, pp. 89–98. ACM (2010)
Danev, B., Čapkun, S.: Transient-based identification of wireless sensor nodes. In: Proceedings of ACM/IEEE IPSN (2009)
Gerdes, R., Mina, M., Russell, S., Daniels, T.: Physical-layer identification of wired ethernet devices. IEEE Trans. Inf. Forensics Secur. 7(4), 1339–1353 (2012)
Giannakis, G., Tepedelenlioglu, C.: Basis expansion models and diversity techniques for blind identification and equalization of time-varying channels. Proc. IEEE 86(10), 1969–1986 (1998). doi:10.1109/5.720248
Grimaldi, R.: Discrete and Combinatorial Mathematics: An Applied Introduction. Addison-Wesley (2004). http://books.google.com/books?id=aQkgAQAAIAAJ
Ivanov, V., Baras, J.: Authentication of fingerprint scanners. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1912–1915 (2011)
Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to Biometrics. Springer (2011)
Khanna, N., Mikkilineni, A.K., Delp, E.J.: Scanner identification using feature-based processing and analysis. IEEE Trans. Inf. Forensics Secur. 4(1), 123–139 (2009)
Khanna, N., Mikkilineni, A.K., Delp, E.J.: Texture based attacks on intrinsic signature based printer identification. In: Proceedings of the SPIE/IS&T Conference on Media Forensics and Security XIIConference on Media Forensics and Security XII (2010)
Khanna, N., Mikkilineni, A.K., Martone, A.F., Ali, G.N., Chiu, G.T.C., Allebach, J.P., Delp, E.J.: A survey of forensic characterization methods for physical devices. Digit. Invest. Int. J. Digit. Forensics Incident Response 3, 17–28 (2006). doi:10.1016/j.diin.2006.06.014
Lai, L., El Gamal, H., Poor, H.: Authentication over noisy channels. IEEE Trans. Inf. Theory 55(2), 906–916 (2009). doi:10.1109/TIT.2008.2009842
Li, X., Jin, S., Gao, X., Wong, K.K.: Near-optimal power allocation for MIMO channels with mean or covariance feedback. IEEE Trans. Commun. 58(1), 289–300 (2010). doi:10.1109/TCOMM.2010.01.070377
Maurer, U.: Authentication theory and hypothesis testing. IEEE Trans. Inf. Theory 46(4), 1350–1356 (2000). doi:10.1109/18.850674
Maurer, U., Renner, R., Wolf, S.: Unbreakable keys from random noise. In: P. Tuyls, B. Skoric, T. Kevenaar (eds.) Security with Noisy Data. Springer (2007)
Menezes, A.J., van Oorschot, P.C., Vanstone, S.A.: Handbook of Applied Cryptography. CRC Press (2001). http://www.cacr.math.uwaterloo.ca/hac/
Petitcolas, F.A.P., Anderson, R.J., Kuhn, M.G.: Information hiding—a survey. Proc. IEEE 87(7), 1062–1078 (1999)
Rasmussen, K., Čapkun, S.: Implications of radio fingerprinting on the security of sensor networks. In: Proceedings of IEEE SecureComm (2007)
Ruhrmair, U., Devadas, S., Koushanfar, F.: Security based on physical unclonability and disorder. In: M. Tehranipoor, C. Wang (eds.) Introduction to Hardware Security and Trust. Springer (2011)
Telatar, E.: Capacity of multi-antenna Gaussian channels. Eur. Trans. Telecommun. 10(6), 585–596 (1999)
Tuyls, P., Skoric, B., Kevenaar, T.: Security with Noisy Data; On Private Biometrics, Secure Key Storage and Anti-Counterfeiting. Springer (2007)
Venkatesan, S., Simon, S., Valenzuela, R.: Capacity of a Gaussian MIMO channel with nonzero mean. In: Vehicular Technology Conference (VTC), vol. 3, pp. 1767–1771 (2003). doi:10.1109/VETECF.2003.1285329
Verma, G., Yu, P.L.: A MATLAB Library for Rapid Prototyping of Wireless Communications Algorithms with the USRP radio family. Tech. rep., U.S. Army Research Laboratory (2013)
Wyner, A.D.: The wire-tap channel. Bell Syst. Tech. J. 54, 1355–1387 (1975)
Xiao, L., Greenstein, L., Mandayam, N., Trappe, W.: Using the physical layer for wireless authentication in time-variant channels. IEEE Trans. Wirel. Commun. 7(7), 2571–2579 (2008). doi:10.1109/TWC.2008.070194
Yu, P., Baras, J., Sadler, B.: Multicarrier authentication at the physical layer. In: International Symposium on a World of Wireless, Mobile and Multimedia Networks WoWMoM, pp. 1–6 (2008). doi:10.1109/WOWMOM.2008.4594926
Yu, P., Baras, J., Sadler, B.: Physical-layer authentication. IEEE Trans. Inf. Forensics Secur. 3(1), 38–51 (2008). doi:10.1109/TIFS.2007.916273
Yu, P., Sadler, B.: MIMO Authentication via deliberate fingerprinting at the physical layer. IEEE Trans. Inf. Forensics Secur. 6(3), 606–615 (2011). doi:10.1109/TIFS.2011.2134850
Zhou, Y., Fang, Y.: Scalable and deterministic key agreement for large scale networks. IEEE Trans. Wirel. Commun. 6(12), 4366–4373 (2007). doi:10.1109/TWC.2007.06088
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: Precoding and Power-Allocation with CSI
Appendix: Precoding and Power-Allocation with CSI
Alice can improve the performance of the system by shaping her transmissions based on her available CSI. Generally, the frame can be decomposed as
where \({{\mathbf {F}_\text {S}}}\) is an \(M \times M\) unitary matrix, \({{\mathbf {P}_\text {S}}}\) is an \(M \times M\) diagonal matrix that allocates power between the columns of \({{\mathbf {F}_\text {S}}}\), and \(\mathbf {S}\) is the modulated and possibly coded \(M \times L\) data matrix. In general, to achieve optimality as described below, Alice allocates energy among the eigenvectors of either the channel covariance or its expectation. That is, the columns of \({{\mathbf {F}_\text {S}}}\) are the channel eigenvectors, and the entries of \({{\mathbf {P}_\text {S}}}\) allocate the transmission energy between them. The total power budget is constrained by
In the following we consider three cases where Alice has (1) no CSI, (2) perfect CSI, or (3) knowledge of the statistics of the channel. We briefly review the capacity-optimal precoding and power allocation strategies for each case.
1.1 No CSI
When the transmitter has no CSI, e.g., in the absence of feedback from the receiver, then there are no preferred transmission modes and transmission is isotropic, so that
resulting in \(\mathbf {\Phi } = \mathbf {I}\).
1.2 Perfect CSI
In this case the transmitter has knowledge of the realization of \(\mathbf H\), and the capacity-achieving channel input covariance \(\mathbf {\Phi }\) has eigenvectors equal to those of \(\mathbf {H}^\dagger \mathbf {H}\). Because the eigenvectors are orthogonal, the optimal power allocation is given by the water-filling solution [23]. That is, the transmissions are shaped using
Here \(P_\text {S}(i) \) (resp., D(i)) is the ith element on the diagonal of \({{\mathbf {P}_\text {S}}}\) (resp., \(\mathbf {D}\)), \(n(i) = \sigma _w^2/D(i)\) is the ith channel noise component, and \(\nu \) is chosen to satisfy the power constraint
In Rayleigh fading (\(K=0 \Rightarrow {\bar{\mathbf {H}} = 0}\)), we have \({{\mathbf {F}_\text {S}}}= \mathbf {U}_T\).
1.3 Statistical CSI
Although not as good as precise knowledge of the realization of \(\mathbf {H}\), when the transmitter has knowledge of the Gaussian channel statistics (mean and covariance), she is still able to improve beyond isotropic transmissions. Conditioned on the knowledge of the channel statistics, the capacity-achieving channel input has eigenvectors equal to those of \(E[\mathbf {H}^\dagger \mathbf {H}]\) [25]. That is, the transmissions are shaped using
In Rayleigh fading (\(K=0\)), we have \({{\mathbf {F}_\text {S}}}= \mathbf {U}_T\).
We note that this power allocation does not correspond to a water-filling solution. When the transmitter does not know \(\mathbf {H}\), precoding the input with \({{\mathbf {F}_\text {S}}}\) does not yield orthogonal channels because energy spills across eigenmodes. Thus in the case of statistical CSI application of water-filling does not yield an optimal solution. An efficient iterative algorithm to determine the optimal \({{\mathbf {P}_\text {S}}}\) is given in [16].
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this chapter
Cite this chapter
Yu, P.L., Sadler, B.M., Verma, G., Baras, J.S. (2016). Fingerprinting by Design: Embedding and Authentication. In: Wang, C., Gerdes, R., Guan, Y., Kasera, S. (eds) Digital Fingerprinting. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6601-1_5
Download citation
DOI: https://doi.org/10.1007/978-1-4939-6601-1_5
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-6599-1
Online ISBN: 978-1-4939-6601-1
eBook Packages: Computer ScienceComputer Science (R0)