Privacy-preserving back-propagation and extreme learning machine algorithms

Samet, Saeed; Miri, Ali

doi:10.1016/j.datak.2012.06.001

Cited by 51 publications

(18 citation statements)

References 25 publications

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“…Wang 3 -network) to meet the challenge of the so-called big data [15]. In addition, ELM has been put into diverse applications such as speaker recognition [16], neuroimage data classification [17], security assessment [18], data privacy [19], EEG and seizure detection [20], image quality assessment [21], image super-resolution [22], FPGA [23], face recognition [24], and human action recognition [25].…”

Section: Introductionmentioning

confidence: 99%

Discriminative graph regularized extreme learning machine and its application to face recognition

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Discriminative graph regularized extreme learning machine and its application to face recognition

et al. 2015

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“…The essence of ELM is that there is no need of tuning the hidden layer of SLFNs. The growing popularity of ELM [3,4,10,14,22,27,28,33,34,38,39,42] is because of its better generalization performance with much faster learning speed in comparison to traditional computational intelligence techniques [19].…”

Section: Introductionmentioning

confidence: 99%

On optimization based extreme learning machine in primal for regression and classification by functional iterative method

Balasundaram

Gupta

2014

Int. J. Mach. Learn. & Cyber.

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In this paper, the recently proposed extreme learning machine in the aspect of optimization method by Huang et al. (Neurocomputing, 74: 155-163, 2010) has been considered in its primal form whose solution is obtained by solving an absolute value equation problem by a simple, functional iterative algorithm. It has been proved under sufficient conditions that the algorithm converges linearly. The pseudo codes of the algorithm for regression and classification are given and they can be easily implemented in MATLAB. Experiments were performed on a number of real-world datasets using additive and radial basis function hidden nodes. Similar or better generalization performance of the proposed method in comparison to support vector machine (SVM), extreme learning machine (ELM), optimally pruned extreme learning machine (OP-ELM) and optimization based extreme learning machine (OB-ELM) methods with faster learning speed than SVM and OB-ELM demonstrates its effectiveness and usefulness.

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“…Samet et al 25 proposed new privacy-preserving algorithms for both back-propagation training and ELM classification between several sites. Their proposed algorithms are applied to the perceptron learning algorithm and presented for only single-layer models.…”

Section: Related Workmentioning

confidence: 99%

CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

Çatak¹,

Mustaçoğlu²

2018

IJCIS

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The training techniques of the distributed machine learning approach replace the traditional methods with a cloud computing infrastructure and provide flexible computing services to clients. Moreover, machine learning-based classification methods are used in many diverse applications such as medical predictions, speech/face recognition, and financial applications. Most of the application areas require security and confidentiality for both the data and the classifier model. In order to prevent the risk of confidential data disclosure while outsourcing the data analysis, we propose a privacy-preserving protocol approach for the extreme learning machine algorithm and give private classification protocols. The proposed protocols compute the hidden layer output matrix H in an encrypted form by using a distributed multi-party computation (or cloud computing model) approach. This paper shows how to build a privacy-preserving classification model from encrypted data.

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Privacy-preserving back-propagation and extreme learning machine algorithms

Cited by 51 publications

References 25 publications

Discriminative graph regularized extreme learning machine and its application to face recognition

Discriminative graph regularized extreme learning machine and its application to face recognition

On optimization based extreme learning machine in primal for regression and classification by functional iterative method

CPP-ELM: Cryptographically Privacy-Preserving Extreme Learning Machine for Cloud Systems

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