Providing Security to Ensure Biometric Identification System in CloudAuthor : Bhuvaneswari Kotte and T. Sirisha Madhuri
Volume 8 No.3 Special Issue:June 2019 pp 128-132
Biometric identification has rapidly growing in recent years. With the development of cloud computing, database owners are incentivized to outsource the bulk size of biometric data and identification tasks to the cloud to liberate the costly storage and computation costs, which however brings potential attacks to users’ privacy. In this paper, we propose an adequate and security to keep biometric identification outsourcing scheme. Categorically, the biometric data is encrypted and outsourced to the cloud server. To get a biometric identification, the database owner encrypts the query data and submits it to the cloud. The cloud implements identification operations over the encrypted database and returns the result to the database owner. An exhaustive security analysis indicated the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with antecedent protocols, experimental results show the proposed scheme achieves a better performance in both preparation and identification procedures.
Biometric Identification, Cloud, Model and Design Goals, Security Analysis
 A. Jain, L. Hong and S. Pankanti, “Biometric identification,” Communications of the ACM,Vol. 43, no. 2, pp. 90-98, 2000.
 R. Allen, P. Sankar and S. Prabhakar, “Fingerprint identification technology, “Biometric Systems, pp. 22-61, 2005.
 J. de Mira, H. Neto, E. Neves, et al., “Biometric-oriented Iris Identification Based on Mathematical Morphology,” Journal of Signal Processing Systems, Vol. 80, No. 2, pp. 181-195, 2015.
 S. Romdhani, V. Blanz and T. Vetter, “Face identification by fitting a 3d morphable model using linear shape and texture error functions,” In European Conference on Computer Vision, pp. 3-19, 2002.
 Y. Xiao, V. Rayi, B. Sun, X. Du, F. Hu, and M. Galloway, “A survey of key management schemes in wireless sensor networks,” Journal of Computer Communications, Vol. 30, No.11-12, pp. 2314-2341, 2007.
 X. Du, Y. Xiao, M. Guizani, and H. H. Chen, “An effective key management scheme for heterogeneous sensor networks,” Ad Hoc Networks, Vol. 5, No.1, pp. 24-34, 2007.
 X. Du and H. H. Chen, “Security in wireless sensor networks,” IEEE Wireless Communications Magazine, Vol. 15, No. 4, pp. 60-66, 2008.
 X. Hei, and X. Du, “Biometric-based two-level secure access control for implantable medical devices during emergency,” In Proc. of IEEE INFOCOM 2011, pp. 346-350, 2011.
 X. Hei, X. Du, J. Wu, and F. Hu, “Defending resource depletion attacks on implantable medical devices,” In Proc. of IEEE GLOBECOM 2010, pp. 1-5, 2010.
 M. Barni, T. Bianchi, and D. Catalano, et al., “Privacy-preserving fingercode authentication,” In Proceedings of the 12th ACM workshop on Multimedia and security, pp. 231-240, 2010.
 M. Osadchy, B. Pinkas, and A. Jarrous, et al., “SCiFI-a system for secure face identification,” In Security and Privacy (SP), 2010 IEEE Symposium on, pp.239-254, 2010.
 D. Evans, Y. Huang, and J. Katz, et al., “Efficient privacy-preserving biometric identification,” In Proceedings of the 17th conference Network and Distributed System Security Symposium, NDSS, 2011.
 J. Yuan and S. Yu, “Efficient privacy-preserving biometric identification in cloud computing,” In Proc. of IEEE INFOCOM 2013, pp. 2652-2660, 2013.
 Q. Wang, S. Hu, and K. Ren, et al., “CloudBI: Practical privacy-preserving outsourcing of biometric identification in the cloud,” In European Symposium on Research in Computer Security, pp. 186-205, 2015.
 Y. Zhu, Z. Wang and J. Wang, “Collusion-resisting secure nearest neighbour query over encrypted data in cloud,” In Quality of Service (IWQoS), 2016 IEEE/ACM 24th International Symposium on, pp. 1-6, 2016.
 S. Pan, S. Yan, and W. Zhu, “Security analysis on privacy-preserving cloud aided biometric identification schemes,” In Australasian Conference on Information Security and Privacy, pp. 446-453, 2016.
 C. Zhang, L. Zhu and C. Xu, “PTBI: An efficient privacy-preserving biometric identification based on perturbed term in the cloud,” Information Sciences, Vol. 409, pp. 56-67, 2017.
 Y. Zhu, T. Takagi, and R. Hu, “Security analysis of collusion-resistant nearest neighbour query scheme on encrypted cloud data,” IEICE Transactions on Information and Systems, Vol. 97, No. 2, pp. 326-330, 2014.
 A. Jain, S. Prabhakar, and L. Hong, et al., “Filterbank-based fingerprint matching,” IEEE Transactions on Image Processing, Vol. 9, No. 5, pp. 846-859, 2000.
 H. Delfs, H. Knebl, and H. Knebl, “Introduction to cryptography,” Berlinetc: Springer, 2002.
 K. Liu, C. Giannella, and H. Kargupta, “An attacker’s view of distance preserving maps for privacy preserving data mining,” Knowledge Discovery in Databases, pp. 297-308, 2006.
 Y. Wang, and D. Hatzinakos, “Face recognition with enhanced privacy protection,” In IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 885-888, 2009.
 K. Wong, and M. Kim, “A privacy-preserving biometric matching protocol for iris codes verification,” In Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing (MUSIC), pp. 120-125, 2012.
 W. Wong, D. Cheung, and B. Kao, et al., “Secure kNN computation on encrypted databases,” In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pp. 139-152, 2009.