An improved Framework for Biometric Database’s privacy

Ahmed EL-YAHYAOUI(1*), Fouzia OMARY(2)
(1) 
(2) 
(*) Corresponding Author
DOI : 10.54039/ijcnis.v13i3.5143

Abstract

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language.

Keywords


Performance; Biometrics; physiological traits; improved FHE; privacy; database security; noise-free; encryption

References


A. EL-YAHYAOUI and M. ECH-CHRIF EL KETTANI, “An Efficient Fully Homomorphic Encryption Scheme,” International Journal of Network Security, vol. 21, no. 1, pp. 91-99, 2019.

A. EL-YAHYAOUI and M. ECH-CHERIF EL KETTANI, “A Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security,” Technologies, vol. 7, no. 1, p. 21, 2019.

A. EL-YAHYOUI and M. ECH-CHERIF EL KETTANI, “Fully homomorphic encryption: Searching over encrypted cloud data,” in 2nd international Conference on Big Data, Cloud and Applications, Tetouan, Morocco, 2017.

J. Bringer, H. Chabanne, M. Izabachène, D. Pointcheval, Q. Tang and S. Zimmer, “An Application of the Goldwasser-Micali Cryptosystem to Biometric Authentication,” in The 12th Australasian Conference on Information Security and Privacy (ACISP ’07), Townsville, Queensland, Australia, 2007.

S. Goldwasser and S. Micali, “Probabilistic encryption and how to play mental poker keeping secret all partial information,” in Fourteenth Annual ACM Symposium on Theory of Computing, San Francisco, California, USA, 5-7 May 1982.

M. Yasuda, T. Shimoyama, J. Kogure, K. Yokoyama and T. Koshiba, “Packed Homomorphic Encryption Based on Ideal Lattices and Its Application to Biometrics,” in International Conference on Availability, Reliability, and Security, Regensburg, Germany, 2013.

P. Drozdowski, N. Buchmann, C. Rathgeb, M. Margraf and C. Busch, “On the Application of Homomorphic Encryption to Face Identification,” in International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 2019.

F. Catak, S. Yayilgan and M. Abomhara, “A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching,” Preprints, p. 16, 2020.

N. Owano, “Engineers unleash car-seat identifier that reads your rear end,” 25 12 2011. [Online]. Available: https://phys.org/news/2011-12-unleash-car-seat-rear.html. [Accessed 16 04 2019].

S. Jadhav and R. Shriram, “DENTAL BIOMETRICS USED IN FORENSIC SCIENCE,” Journal of Engineering Research and Studies, vol. 3, no. 1, 2012.

Hofer and A. Marana, “Dental Biometrics: Human Identification Based On Dental Work Information,” in XX Brazilian Symposium on Computer Graphics and Image Processing, Minas Gerais, Brazil, 2007.

H.-K. Lammi, “Ear biometrics,” Lappeenranta University of Technology, Lappeenranta, Finland, 2004.

N. Grabham, M. Swabey, P. Chambers, M. Lutman, N. White and J. Chad, “An Evaluation of Otoacoustic Emissions as a Biometric,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 1, pp. 174 - 183, 2013.

R. Bilger, M. Matthies, D. Hammel and M. Demorest, “Genetic implications of gender differences in the prevalence of spontaneous otoacoustic emissions,” J Speech Hear Res, vol. 33, no. 3, pp. 418-432, 1990.

M. Whitehead, N. Kamal, B. Lonsbury-Martin and G. Martin, “Spontaneous otoacoustic emissions in different racial groups,” Scand Audiol, vol. 22, no. 1, pp. 3-10, 1993.

M. Swabey, S. Beeby, A. Brown and J. Chad, “Using Otoacoustic Emissions as a Biometric,” in International Conference on Biometric Authentication, Hong Kong, China, 2004.

M. Swabey, P. Chambers, M. Lutman, N. White, J. Chad, A. Brown and S. Beeby, “The biometric potential of transient otoacoustic emissions,” International Journal of Biometrics, vol. 1, no. 3, pp. 349-364 , 2009.

J. Gao, F. Agrafioti, S. Wang and D. Hatzinakos, “Transient Otoacoustic Emissions for biometric recognition,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, 2012.

J. Daugman, “How Iris Recognition Works,” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, vol. 14, no. 1, pp. 21-30, 2004.

C. Simon and I. Goldstein, “A New Scientific Method of Identification,” New York State Journal of Medicine, vol. 35, no. 18, pp. 901-906, 1935.

I. Nigam, M. Vatsa and R. Singh, “Ocular biometrics: A survey of modalities and fusion approaches,” Information Fusion, vol. 26, pp. 1-35, 2015.

M. Uzair, A. Mahmood, A. Mian and C. McDonald, “Periocular biometric recognition using image sets,” in 2013 IEEE Workshop on Applications of Computer Vision (WACV), Tampa, FL, USA, 2013.

F. Alonso-Fernandeza and J. Bigun, “A Survey on Periocular Biometrics Research,” Pattern Recognition Letters, vol. 85, no. 2, pp. 92-105, 2016.

G. Santosa and E. Hoyle, “A fusion approach to unconstrained iris recognition,” Pattern Recognition Letters, vol. 33, no. 18, pp. 984-990, 2012.

Z. Zhou, E. Du, N. Thomas and E. Delp, “A New Human Identification Method: Sclera Recognition,” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 42, no. 3, pp. 571 - 583, 2012.

B. Gajic and K. Paliwal, “Robust speech recognition using features based on zero crossings with peak amplitudes,” in IEEE International Conference on Acoustics, Speech, and Signal Processing,, 2003.

N. Houmani, A. Mayoue, S. Garcia-Salicetti, B. Dorizzi, M. Khalil, M. Moustafa, H. Abbas, D. Muramatsu, B. Yanikoglu, A. Kholmatov, M. Martinez-Diaz, J. Fierrez, J. Ortega-Garcia, J. Roure Alcobé, J. Fabregas, M. Faundez-Zanuy, J. Pascual-Gaspar, V. Cardenoso-Payo and C. Vivaracho-Pascual, “BioSecure signature evaluation campaign (BSEC’2009): Evaluating online signature algorithms depending on the quality of signatures,” Pattern Recognition, vol. 45, pp. 993-1003, 2012.

N. Dahiya and C. Kant, “Biometrics Security Concerns,” in Second International Conference on Advanced Computing & Communication Technologies, 2012.

P. Campisi, “Security and Privacy in Biometrics: Towards a Holistic Approach,” in Security and Privacy in Biometrics, London, Springer-Verlag , 2013, pp. 1-23.

R. Jain and C. Kant, “Attacks on Biometric Systems: An Overview,” International Journal of Advances in Scientific Research, vol. 1, no. 7, pp. 283-288, 2015.

J. Mwema, M. Kimwele and S. Kimwele, “A Simple Review of Biometric Template Protection Schemes Used in Preventing Adversary Attacks on Biometric Fingerprint Templates,” International Journal of Computer Trends and Technology (IJCTT), vol. 20, no. 1, pp. 12-18, 2015.

R. Rivest, L. Adleman and M. Dertouzos, “On data banks and privacy homomorphisms,” Foundations of secure computation, vol. 4, no. 11, pp. 169-180, 1978.

C. Gentry, “Fully homomorphic encryption using ideal lattices,” in 41st Annual ACM Symposium on Theory of Computing, (STOC 2009), Bethesda, MD, USA, 2009.

N. Smart and F. Vercautern, “Fully Homomorphic Encryption with Relatively Small Key and Ciphertext Sizes,” Cryptology ePrint Archive, Report 2009/571, IACR, 2009.

M. van Dijk, C. Gentry, S. Halevi and V. Vaikuntanathan, “Fully Homomorphic Encryption over the Integers,” Cryptology ePrint Archive, Report 2009/616, 2009.

Z. Brakerski and V. Vaikuntanathan, “Efficient Fully Homomorphic Encryption from (Standard) LWE,” IACR, 2011.

Z. Brakerski, “Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP,” IACR, 2012.

C. Gentry, A. Sahai and B. Waters, “Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based,” IACR, 2013.

I. Chillotti , N. Gama , M. Georgieva and M. Izabachène, “Faster Fully Homomorphic Encryption: Bootstrapping in less than 0.1 Seconds,” IACR, 2016.

L. Xiao, O. Bastani and L. Yen, “An efficient homomorphic encryption protocol for multi-user systems,” Cryptology ePrint Archive, Report 2012/193, 2012.

A. Kipnis and E. Hibshoosh, “Efficient Methods for Practical Fully Homomorphic Symmetric-key Encrypton, Randomization and Verification,” Cryptology ePrint Archive, Report 2012/637, 2012.

J. Li and L. Wang, “Noise-free Symmetric Fully Homomorphic Encryption based on noncommutative rings,” Cryptology eprint report 2015/641, 2015.

K. Lauter, M. Naehrig and V. Vaikuntanathan, “Can homomorphic encryption be practical?,” Cryptology eprint report 405/2011, 2011.

ITU-T Technology Watch Report, “Biometrics and Standards,” Telecommunication Standardization Policy Division, Geneva, Switzerland, December 2009.

Daon, “Biometric Standards overview,” Daon, Virginia USA, 2009.

W. Zhao, R. Chellappa, A. Rosenfeld and P. Phillips, “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399-458, 2003.

M. P. BEHAM and S. M. M. ROOMI, “A REVIEW OF FACE RECOGNITION METHODS,” International Journal of Pattern Recognition and Arti¯cial Intelligence, vol. 27, no. 4, p. 1356005 (35 pages), 2013.

M. Black and Y. Yacoob, “Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion,” in IEEE International Conference on Computer Vision, Cambridge, MA, USA, 1995.

F. Prokoski, R. Riedel and J. Coffin, “Identification of individuals by means of facial thermography,” in International Carnahan Conference on Security Technology: Crime Countermeasures, Atlanta, GA, USA, 1992.

J. Pierrard and T. Vetter, “Skin Detail Analysis for Face Recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007.

M. Kirby and L. Sirovich, “Application of the Karhunen-Loeve procedure for the characterization of human faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 103-108, 1990.

L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” Journal of the Optical Society of America A: Optics, Image Science, and Vision, vol. 4, no. 3, pp. 519-524, 1987.

A. Kumar, “Personal identification using finger knuckles,” SPIE Newsroom, 20 3 2009.

K. Usha and M. Ezhilarasan, “Finger knuckle biometrics – A review,” Computers and Electrical Engineering, vol. 45, pp. 249-259, 2015.

J. Godsell, “Fingerprint Techniques,” Journal of the Forensic Science Society, vol. 3, no. 2, pp. 79-87, 1963.

R. Kennedy, “Uniqueness of bare feet and its use as a possible means of identification,” Forensic Science International, vol. 82, no. 1, pp. 81-87, 1996.

K. Nakajima, Y. Mizukami, K. Tanaka and T. Tamura, “Footprint-based personal recognition,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 1, pp. 1534 - 1537, 2000.

J. Jung, K. Park and Z. Bien, “Unconstrained person recognition method using static and dynamic footprint,” in 18th Hungarian-Korean Seminar, 2002.

J. Jung, T. Sato and Z. Bien, “Dynamic footprintbased person recognition method using a hidden markov model and a neural network,” International Journal of Intelligent Systems, vol. 19, no. 11, p. 1127–1141, 2004.

A. Uhl and P. Wild, “Footprint-based biometric verification,” J. of Electronic Imaging, vol. 17, no. 1, 2008.

R. Zunkel, “Hand Geometry Based Verification,” in Biometrics, Boston, MA, Springer, 1996, pp. 87-101.

A. Singh, A. Agrawal and C. Pal, “Hand geometry verification system: A review,” in International Conference on Ultra Modern Telecommunications & Workshops, St. Petersburg, Russia, 2009.

Z. Liu, J. Guo and . L. Bruton, “A Knowledge-based System For Hair Region Segmentation,” in Fourth International Symposium on Signal Processing and Its Applications, Gold Coast, Queensland, Australia, 1996.

U. Muhammad, M. Svaneraa, R. Leonardia and S. Benini, “Hair detection, segmentation, and hairstyle classification in the wild,” Image and Vision Computing, vol. 71, pp. 25-37, 2018.

H. Su and A. Kong, “A Study on Low Resolution Androgenic Hair Patterns for Criminal and Victim Identification,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 4, pp. 666 - 680, 2014.

J. Roth and X. Liu, “On Hair Recognition in the Wild by Machine,” in Twenty-Eighth AAAI Conference on Artificial Intelligence, Québec City, Québec, Canada, 2014.

L. Shamir, S. Ling, S. Rahimi, L. Ferrucci and I. Goldberg, “Biometric identification using knee X-rays,” Int. J. of Biometrics, vol. 1, no. 3, p. 365–370, 2009.

L. Shamir, “MRI-based knee image for personal identification,” Int. J. of Biometrics, vol. 5, no. 2, pp. 113-125, 2013.

K. Suzuki and Y. Tsuchihashi, “A new attempt of personal identification by means of lip print,” Canadian Society of Forensic Science Journal, vol. 4, no. 4, pp. 154-158, 1971.

T. Williams, “Lip Prints - Another Means of Identification,” Journal of Forensic Identification, vol. 41, no. 3, pp. 190-194, 1991.

J. Ball, “The current status of lip prints and their use for identification,” The Journal of Forensic Odonto-stomatology, vol. 20, no. 2, pp. 43-46, 2002.

R. Prabhu, A. Dinkar and V. Prabhu, “Collection of lip prints as a forensic evidence at the crime scene – an insight,” J Oral Health Res, vol. 1, no. 4, p. 129–135, 2010.

C. Travieso , C. Travieso-Gonzalez, E. Gutierrez , M. Ballester and J. Brice, “Biometric Identification System By Lip Shape,” in 36th Annual 2002 International Carnahan Conference on Security Technology, Atlantic City, NJ, USA, 2002.

I. Barbosa, T. Theoharis, C. Schellewald and C. Athwal, “Transient biometrics using finger nails,” in IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), Arlington, VA, USA, 2013.

S. Garg, A. Kumar and M. Hanmandlu, “Biometric authentication using finger nail surface,” in 12th International Conference on Intelligent Systems Design and Applications (ISDA), Kochi, India, 2012.

S. Song, K. Ohnuma, L. Zhi, M. Liangmo, A. Kawada and T. Monma, “Novel biometrics based on nose pore recognition,” Optical Engineering, vol. 48, no. 5, 2009.

S. Song, K. Ohnuma, Z. Liu and L. , “Nose pore recognition based on discriminant locality preserving projections,” in Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, Yichang, China, 2009.

A. Konga, D. Zhang and M. Kamel , “A survey of palmprint recognition,” Pattern Recognition, vol. 42, pp. 1408 -- 1418, 2009.

X. Wua, D. Zhangb, K. Wanga and B. Huang, Pattern Recognition, vol. 37, p. 1987 – 1998, 2004.

“Palmprint Classification,” in 6 IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan, October 8-11, 2006.

A. Jain, Y. Chen and M. Demirkus, “Pores and Ridges: Fingerprint Matching Using Level 3 Features,” in 18th International Conference on Pattern Recognition (ICPR'06), Hong Kong, China, 2006.

A. Gupta, “The reliability of fingerprint pore area in personal identification,” Thesis presented for the degree of Master of Philosophy, University of Wolverhampton, 2008.

J. Lee, M. Pyo, S. Lee, J. Kim, M. Ra, W. Kim, B. Park, C. Lee and J. Kim, “Hydrochromic conjugated polymers for human sweat pore mapping,” Nature Communications, vol. 5, 2014.

M. Drahanský, O. Kanich, E. Březinová and K. Shinoda, “Experiments with Optical Properties of Skin on Fingers,” International Journal of Optics and Applications, vol. 6, no. 2, pp. 37-46, 2016.

C. LI, Y. Benezeth, K. Nakamura, R. Gomez and F. Yang, “Evaluation of Skin Spectral Features for Biometric,” in IEEE 2nd International Conference on Signal and Image Processing, Singapore, Singapore, 2017.

C. Cornelius, J. Sorber, R. Peterson, J. Skinner, R. Halter and D. Kotz, “Who wears me? bioimpedance as a passive biometric,” in 3rd USENIX Workshop on Health Security and Privacy, Bellevue, WA, August 2012.

I. Martinovic, K. Rasmussen, M. Roeschlin and G. Tsudik, “Authentication Using Pulse-Response Biometrics,” Communications of the ACM, vol. 60, no. 2, pp. 108-115, February 2017.

H. Noh, C. Ahn, H. Kon and J. Sim, “Ratiometric Impedance Sensing of Fingers for Robust Identity Authentication,” Scientific Reports, 2019.

B. Gajic and K. Paliwal, “Robust speech recognition using features based on zero crossings with peak amplitudes,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP '03)., Hong Kong, China, 2003.


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International Journal of Communication Networks and Information Security (IJCNIS)               ISSN: 2073-607X (Online)