Securely and efficiently perform large matrix rank decomposition computation via cloud computing

详细信息    查看全文

• 作者：Xinyu Lei ; Xiaofeng Liao ; Xiaoxi Ma ; Liping Feng
• 关键词：Cloud computing ; Secure outsourcing ; Rank decomposition ; Outsourcing software system
• 刊名：Cluster Computing
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：18
• 期：2
• 页码：989-997
• 全文大小：763 KB
• 参考文献：1.Kessaci, Y., Melab, N., Talbi, E.-G.: A pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation. Clust. Comput. 1鈥?8 (2012)
  2.Zheng, W., Xu, P., Huang, X., Wu, N.: Design a cloud storage platform for pervasive computing environments. Clust. Comput. 13(2), 141鈥?51 (2010)View Article
  3.Brunette, G., Mogull, R.: Security Guidance for Critical Areas of Focus in Cloud Computing, vol. 2. 1, pp. 1鈥?6. Cloud Security Alliance (2009)
  4.Ryan, M.D.: Cloud computing security: the scientific challenge, and a survey of solutions. J. Syst. Softw. (2013)
  5.Cheng, Y.-Q., Zhuang, Y.-M., Yang, J.-Y.: Optimal fisher discriminant analysis using the rank decomposition. Pattern Recognit. 25(1), 101鈥?11 (1992)View Article MathSciNet
  6.Webb, A.R.: Statistical Pattern Recognition. Wiley (2003)
  7.Parks-Gornet, J., Imam, I.N.: Using rank factorization in calculating the moore-penrose generalized inverse. In: Southeastcon鈥?9. Proceedings. Energy and Information Technologies in the Southeast, IEEE, pp. 427鈥?31. IEEE (1989)
  8.Courrieu, P.: Fast computation of moore-penrose inverse matrices. arXiv preprint arXiv:鈥?804.鈥?809 (2008)
  9.Gro\(\beta \) , J., Trenkler, G.: Generalized and hypergeneralized projectors. In: Linear Algebra and its Applications, vol. 264, pp. 463鈥?74 (1997)
  10.Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. J. Priv. Confid. 1(1), 5 (2009)
  11.Meyer, C.: Matrix analysis and applied linear algebra book and solutions manual. Soc Ind. Appl. Math. 2 (2000)
  12.Lyndon, R.C., Schupp, P.E., Lyndon, R., Schupp, P.: Combinatorial Group Theory, vol. 177. Springer, Berlin (1977)
  13.Durstenfeld, R.: Algorithm 235: random permutation. Commun. ACM 7(7), 420 (1964)View Article
  14.Knuth, D.E.: The art of Computer Programming. Addison-Wesley (2006)
  15.Storjohann, A.: Integer matrix rank certification. In: Proceedings of the 2009 International Symposium on Symbolic and Algebraic Computation, pp. 333鈥?40. ACM (2009)
  16.Chen, Y., Nguyen, P.: Faster algorithms for approximate common divisors: breaking fully-homomorphic-encryption challenges over the integers. Adv. Cryptol. EUROCRYPT 2012, 502鈥?19 (2012)
  17.Papadimitriou, C.H.: Computational Complexity. John Wiley and Sons Ltd. (2003)
  18.Gentry, C.: A fully homomorphic encryption scheme. Ph.D. dissertation, Stanford University (2009)
  19.Gennaro, R., Gentry, C., Parno, B.: Non-interactive verifiable computing: outsourcing computation to untrusted workers. Adv. Cryptol. CRYPTO 2010, 465鈥?82 (2010)MathSciNet
  20.Chung, K., Kalai, Y., Vadhan, S.: Improved delegation of computation using fully homomorphic encryption. Adv. Cryptol. CRYPTO 2010, 483鈥?01 (2010)MathSciNet
  21.Atallah, M., Pantazopoulos, K., Rice, J., Spafford, E.: Secure outsourcing of scientific computations. Adv. Comput. 54, 215鈥?72 (2002)View Article
  22.Benjamin, D., Atallah, M.J.:Private and cheating-free outsourcing of algebraic computations. In: Sixth Annual Conference on Privacy, Security and Trust: PST鈥?8, pp. 240鈥?45. IEEE (2008)
  23.Atallah, M., Frikken, K.: Securely outsourcing linear algebra computations. In: Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security, pp. 48鈥?9. ACM (2010)
  24.Shamir, A.: How to share a secret. Commun. ACM 22(11), 612鈥?13 (1979)View Article MATH MathSciNet
  25.Wang, C., Ren, K., Wang, J.: Secure and practical outsourcing of linear programming in cloud computing. IEEE Trans. Cloud Comput. (2011)
  26.Wang, C., Ren, K., Wang, J., Wang, Q.: Harnessing the cloud for securely outsourcing large-scale systems of linear equations. IEEE Trans. Parallel Distrib. Syst. (2012)
  27.Lei, X., Liao, X., Huang, T., Li, H., Hu, C.: Outsourcing large matrix inversion computation to a public cloud. IEEE Trans. Cloud Comput. 1(1), 78鈥?7 (2013)
  28.Wang, C., Zhang, B., Ren, K., Wang, J.: Privacy-assured outsourcing of image reconstruction service in cloud. IEEE Trans. Emerg. Top. Comput. 1(1), 166鈥?77 (2013)View Article MathSciNet
  29.Chen, F., Xiang, T., Yang, Y.: Privacy-preserving and verifiable protocols for scientific computation outsourcing to the cloud. J. Parallel Distrib. Comput. 74(3), 2141鈥?151 (2014)
  30.Yao, A.: Protocols for secure computations. In: Proceedings of the 23rd Annual Symposium on Foundations of Computer Science, pp. 160鈥?64 (1982)
  31.Goldwasser, S., Kalai, Y., Rothblum, G.: Delegating computation: interactive proofs for muggles. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, pp. 113鈥?22. ACM (2008)
  32.Hohenberger, S., Lysyanskaya, A.: How to securely outsource cryptographic computations. Theory Cryptogr. 264鈥?82 (2005)
  33.Kawamura, S., Shimbo, A.: Fast server-aided secret computation protocols for modular exponentiation. IEEE J. Sel. Areas Commun. 11(5), 778鈥?84 (1993)View Article
  
• 作者单位：Xinyu Lei (1)
  Xiaofeng Liao (1)
  Xiaoxi Ma (2)
  Liping Feng (3)
  
  1. College of Electronics and Information Engineering, Southwest University, Chongqing, 400715, China
  2. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore
  3. Department of Computer Science, Xinzhou Normal University, Xinzhou, 034000, Shanxi, China
  
• 刊物类别：Computer Science
• 刊物主题：Processor Architectures
  Operating Systems
  Computer Communication Networks
  
• 出版者：Springer Netherlands
• ISSN：1573-7543

文摘

Cloud computing enables resource-constrained clients to economically outsource their huge computation workloads to a powerful cloud server. This promising computing paradigm is able to realize client-cloud cooperative computations. It also brings in new security concerns and challenges, such as input/output privacy and efficiency. Since large matrix rank decomposition computation (RDC) is ubiquitous in the fields of science and engineering, a first step is taken forward to design a protocol that enables clients to securely and efficiently outsource RDC to a public cloud in this paper. It is analytically shown that the proposed protocol is correct and secure. Extensive theoretical analysis and experimental evaluation also show its high-efficiency and immediate practicability. It is hoped that the proposed protocol can shed light on designing other novel secure outsourcing protocols, and inspire powerful companies and working groups to finish the programming of the demanded all-inclusive scientific computations outsourcing software system. It is believed that such software system can be profitable by means of providing large-scale scientific computation services for so many potential clients. The proposed RDC outsourcing protocol is a step forward to realize such integrated software system.