Achieving security, robust cheating resistance, and high-efficiency for outsourcing large matrix multiplication computation to a malicious cloud

Lei, Xinyu; Liao, Xiaofeng; Huang, Tingwen; Heriniaina, Feno

doi:10.1016/j.ins.2014.05.014

Cited by 94 publications

(43 citation statements)

References 25 publications

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“…Securely outsourcing large-scale matrix multiplication has been researched for many years (Atallah et al 2002;Lei et al 2014), which is commonly used as the building block in scientific and engineering fields. Next, we will introduce a Algorithm 1 Mini-batch SLP training algorithm Input:…”

Section: Privacy-preserving Methods For Outsourcing Matrix Multiplicationmentioning

confidence: 99%

“…Furthermore, based on the basic idea in Mohassel and Zhang (2017), we propose a mini-batch SLP training scheme. In the following, we will introduce the privacypreserving method for large-scale matrix multiplication in cloud computing Lei et al (2014). Finally, we will give a secure dot-secure technique by using trace functions Malek and Miri (2006).…”

Section: Preliminariesmentioning

confidence: 99%

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Verifiable privacy-preserving single-layer perceptron training scheme in cloud computing

et al. 2018

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With the advent of artificial intelligence, machine learning has been well explored and extensively applied into numerous fields, such as pattern recognition, image processing and cloud computing. Very recently, machine learning hosted in a cloud service has gained more attentions due to the benefits from the outsourcing paradigm. Based on cloud-aided computation techniques, the heavy computation tasks involved in machine learning process can be off-loaded into the cloud server in a pay-per-use manner, whereas outsourcing large-scale collection of sensitive data risks privacy leakage since the cloud server is semi-honest. Therefore, privacy preservation for the client and verification for the returned results become two challenges to be dealt with. In this paper, we focus on designing a novel privacy-preserving single-layer perceptron training scheme which supports batch patterns training and verification for the training results on the client side. In addition, adopting classical secure two-party computation method, we design a novel lightweight privacy-preserving predictive algorithm. Both two participants learns nothing about other's inputs, and the calculation result is only known by one party. Detailed security analysis shows that the proposed scheme can achieve the desired security properties. We also demonstrate the efficiency of our scheme by providing the experimental evaluation on two different real datasets.

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Section: Privacy-preserving Methods For Outsourcing Matrix Multiplicationmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Verifiable privacy-preserving single-layer perceptron training scheme in cloud computing

et al. 2018

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show abstract

“…Some related works [18][19][20][21][22] focus on verifiable secure outsourcing of two matrix computation. These solutions exploit specific properties of matrix multiplication and design special protocols for secure outsourced matrix multiplication.…”

Section: Related Workmentioning

confidence: 99%

Secure Outsourced Computation of Multiple Matrix Multiplication Based on Fully Homomorphic Encryption

2019

KSII TIIS

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Fully homomorphic encryption allows a third-party to perform arbitrary computation over encrypted data and is especially suitable for secure outsourced computation. This paper investigates secure outsourced computation of multiple matrix multiplication based on fully homomorphic encryption. Our work significantly improves the latest Mishra et al.'s work. We improve Mishra et al.'s matrix encoding method by introducing a column-order matrix encoding method which requires smaller parameter. This enables us to develop a binary multiplication method for multiple matrix multiplication, which multiplies pairwise two adjacent matrices in the tree structure instead of Mishra et al.'s sequential matrix multiplication from left to right. The binary multiplication method results in a logarithmic-depth circuit, thus is much more efficient than the sequential matrix multiplication method with linear-depth circuit. Experimental results show that for the product of ten 32×32 (64×64) square matrices our method takes only several thousand seconds while Mishra et al.'s method will take about tens of thousands of years which is astonishingly impractical. In addition, we further generalize our result from square matrix to non-square matrix. Experimental results show that the binary multiplication method and the classical dynamic programming method have a similar performance for ten non-square matrices multiplication.

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“…The researcher could not find any cloud computing theories related to the topic. However, the cloud has been described as a method of computational "outsourcing" (Lei, Liao, Huang, & Heriniaina, 2014) and the latest trend to outsource part or complete IT operations to run a business from the public cloud (Dhar, 2012).…”

Section: Cloud Computing Theoriesmentioning

confidence: 99%

IT controls in the public cloud: Success factors for allocation of roles and responsibilities

Khan

Nicho

Takruri

2016

Journal of Information Technology Case and Application Research

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Achieving security, robust cheating resistance, and high-efficiency for outsourcing large matrix multiplication computation to a malicious cloud

Cited by 94 publications

References 25 publications

Verifiable privacy-preserving single-layer perceptron training scheme in cloud computing

Verifiable privacy-preserving single-layer perceptron training scheme in cloud computing

Secure Outsourced Computation of Multiple Matrix Multiplication Based on Fully Homomorphic Encryption

IT controls in the public cloud: Success factors for allocation of roles and responsibilities

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