文章基本信息

标题：Professionally Providing Security to Cloud using Nonlinear Programming
本地全文：下载
作者：Chakrapani Avala ; Raja Sekhar. M
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：4
页码：577-580
语种：English
出版社：Ayushmaan Technologies
摘要：Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. However, how to protect customers’ confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of non linear equations (NLE) in cloud. It provides a practical mechanism design which fulfils input/output privacy, cheating resilience, and efficiency. In the proposed approach practical efficiency is achieved by explicit decomposition of NLP into NLP solvers running on the cloud and private NLP parameters owned by the customer. When compared to the general circuit representation the resulting flexibility allows exploring appropriate security/efficiency trade-off via higher-level abstraction of NLP computations. It is possible to construct a set of effective privacy-preserving transformation techniques for any problem, by framing a private data possessed by the client for NLP problem as a combination of matrices and vectors, which allow customers to transform original NLP problem into some arbitrary value while defending sensitive input or output information. To confirm the computational result, the fundamental duality theorem of NLP computation should be explored and then derive the essential and adequate constraints that a accurate result must satisfy.
关键词：Confidential Data;System of Non Linear Equation;Cloud Computing;Computation Outsourcing