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dc.contributor.authorSalinas Monroy, Sergio A.
dc.contributor.authorLuo, Changqing
dc.contributor.authorLiao, Weixian
dc.contributor.authorLi, Pan
dc.date.accessioned2017-01-21T22:39:44Z
dc.date.available2017-01-21T22:39:44Z
dc.date.issued2016
dc.identifier.citationSalinas Monroy, Sergio A.; Luo, Changqing; Liao, Weixian; Li, Pan. 2016. Efficient secure outsourcing of large-scale quadratic programs. ASIA CCS '16 Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, pp 281-292en_US
dc.identifier.isbn978-1-4503-4233-9
dc.identifier.otherWOS:000390302800025
dc.identifier.urihttp://dx.doi.org/10.1145/2897845.2897862
dc.identifier.urihttp://hdl.handle.net/10057/12817
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThe massive amount of data that is being collected by today's society has the potential to advance scientific knowledge and boost innovations. However, people often lack sufficient computing resources to analyze their large-scale data in a cost-effective and timely way. Cloud computing offers access to vast computing resources on an on-demand and pay-per-use basis, which is a practical way for people to analyze their huge data sets. However, since their data contain sensitive information that needs to be kept secret for ethical, security, or legal reasons, many people are reluctant to adopt cloud computing. For the first time in the literature, we propose a secure outsourcing algorithm for large-scale quadratic programs (QPs), which is one of the most fundamental problems in data analysis. Specifically, based on simple linear algebra operations, we design a low-complexity QP transformation that protects the private data in a QP. We show that the transformed QP is computationally indistinguishable under a chosen plaintext attack (CPA), i.e., CPA-secure. We then develop a parallel algorithm to solve the transformed QP at the cloud, and efficiently find the solution to the original QP at the user. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop. We find that our proposed algorithm offers significant time savings for the user and is scalable to the size of the QP.en_US
dc.language.isoen_USen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofseriesASIA CCS '16 Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security;
dc.subjectSecure outsourcingen_US
dc.subjectQuadratic programsen_US
dc.subjectParallel computingen_US
dc.subjectBig dataen_US
dc.titleEfficient secure outsourcing of large-scale quadratic programsen_US
dc.typeConference paperen_US
dc.rights.holder©2016 Copyright held by the owner/author(s).en_US


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