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6 results on '"Kuang, Liwei"'

5. Secure Tensor Decomposition Using Fully Homomorphic Encryption Scheme

Author

KUANG Liwei, YANG Lawrence T., FENG Jun, DONG Mianxiong, KUANG Liwei, YANG Lawrence T., FENG Jun, and DONG Mianxiong

Abstract

As the rapidly growing volume of data are beyond the capabilities of many computing infrastructures, to securely process them on cloud has become a preferred solution which can both utilize the powerful capabilities provided by cloud and protect data privacy. This paper puts forward a new approach to securely decompose tensor, the mathematical model widely used in data-intensive applications, to a core tensor and some truncated orthogonal bases. The structured, semi-structured as well as unstructured data are all transformed to low-order sub-tensors which are then encrypted using the fully homomorphic encryption scheme. A unified high-order cipher tensor model is constructed by collecting all the cipher sub-tensors and embedding them to a base tensor space. The cipher tensor is decomposed through a proposed secure algorithm, in which the square root operations are eliminated during the Lanczos procedure. The paper makes an analysis of the secure algorithm in terms of time consumption, memory usage and decomposition accuracy. Experimental results reveals that this approach can securely decompose tensor models. With the advancement of fully homomorphic encryption scheme, the proposed secure tensor decomposition method is expected to be widely applied on cloud for privacy-preserving data processing.

Published
2015

6. A Tensor-Based Big-Data-Driven Routing Recommendation Approach for Heterogeneous Networks.

Author

Wang, Xiaokang, Yang, Laurence T., Kuang, Liwei, Liu, Xingang, Zhang, Qingxia, and Deen, M. Jamal

Subjects
BIG data, ROUTING (Computer network management), RECOMMENDER systems, DATA libraries, COMPUTER network architectures
Abstract

Telecommunication networks are evolving toward a data-center-based architecture, which includes physical network functions, virtual network functions, as well as various types of management and orchestration systems. The primary purpose of this type of heterogeneous network is to provide efficient and convenient communication services for users. However, the diverse factors of a heterogeneous network such as bandwidth, delay, and communication protocol, bring great challenges for routing recommendations. In addition, the growing volume of big data and the explosive deployment of heterogeneous networks have started a new era of applying big data technologies to implement routing recommendations. In this article, a tensor-based big-data-driven routing recommendation framework, including the edge plane, fog plane, cloud plane, and application plane, is proposed. In this framework, a tensor-based, holistic, hierarchical approach is introduced to generate efficient routing paths using tensor decomposition methods. Also, a tensor matching method including the controlling tensor, seed tensor, and orchestration tensor is employed to realize routing recommendation. Finally, a case study is used to demonstrate the key processing procedures of the proposed framework. [ABSTRACT FROM AUTHOR]

Published
2018
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