Training Support Vector Machines with privacy-protected data

Gonzalez-Serrano, F.J.; Navia-Vázquez, Ángel; Amor-Martín, Adrián

doi:10.1016/j.patcog.2017.06.016

Cited by 46 publications

(32 citation statements)

References 28 publications

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“…The operations in multilinear mode product are multiplication and addition. Therefore, the secure multilinear mode product protocol (SMMP) using the collaborative cloud model can be implemented by exploiting secure multiplication protocol (SM) [17] and the Paillier addition property. Formally, the protocol is defined as follows:…”

Section: Secure Multilinear Mode Product and Secure Tensor Additionmentioning

confidence: 99%

“…Another popular line of research uses partially homomorphic cryptosystem based secure cloud model to securely process data. In such an approach, the cloud offloads majority, or all, computations of the data users in applications such as secure facial expression recognition [24], secure back-propagation neural network learning [25] over unencrypted data in the cloud, and secure query processing [26], secure large-scale similarity search [27] and secure support vector machine [17] over encrypted data in the cloud.…”

Section: Related Workmentioning

confidence: 99%

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Secure Outsourced Principal Eigentensor Computation for Cyber-Physical-Social Systems

Feng

Yang

Zhu

et al. 2021

IEEE Trans. Sustain. Comput.

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Cyber-physical-social systems (CPSS) are revolutionizing the relationships between humans, computers and things. Outsourcing computation to cloud can offer resources-constrained enterprises and consumers sustainable computing in CPSS. However, ensuring the security of data in such an outsourced environment remains a research challenge. Principal eigentensor computation has emerged as a powerful tool dealing with multidimensional cyber-physical-social systems data. In this paper, we present two novel secure principal eigentensor computation (SPEC) schemes for sustainable CPSS. To the best of our knowledge, this is the first effort to address SPEC over encrypted data in cloud without the interaction need between multiple users and cloud. More specifically, we leverage cloud server and trusted hardware component to design a collaborative cloud model. Using the model, we propose (1) a basic SPEC scheme based on homomorphic computing and (2) an efficient SPEC scheme that combines the advantages of homomorphic computing and garbled circuits, and exploits packing technology to reduce computational cost. Finally, we theoretically and empirically analyze the security and efficiency of our SPEC schemes. Findings demonstrate that the proposed schemes provide a secure and efficient way of outsourcing computation for CPSS. In addition, from the cloud user's perspective, our proposal is lightweight.

show abstract

Section: Secure Multilinear Mode Product and Secure Tensor Additionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Secure Outsourced Principal Eigentensor Computation for Cyber-Physical-Social Systems

Feng

Yang

Zhu

et al. 2021

IEEE Trans. Sustain. Comput.

View full text Add to dashboard Cite

show abstract

“…ELM was proposed in 2004 by Huang [51]. Different from traditional algorithms (such as BP algorithm), ELM randomly generates the weights between the input layer, the hidden layer, and the thresholds of the hidden layer node.…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

Yang

Dey

Sherratt

et al. 2020

IFS

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AI based Speech Recognition has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. Smart home assistants that could detect the user's emotion would improve the communication between a user and the assistant enabling the assistant to offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable algorithm considering performance verses complexity for deployment in smart home devices. The four emotional speeches were selected from the Berlin Emotional Database (EMO-DB) as experimental data, 26 MFCC features were extracted from each type of emotional speech to identify the emotions of happiness, anger, sadness and neutrality. Then, speaker-independent experiments for our Speech emotion Recognition (SER) were conducted by using the Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). Synthesizing the recognition accuracy and processing time, this work shows that the performance of SVM was the best among the four methods as a good candidate to be deployed for SER in smart home devices. SVM achieved an overall accuracy of 92.4% while offering low computational requirements when training and testing. We conclude that the MFCC features and the SVM classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion.

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“…Later, Salinas et al [53] presented a transformed quadratic program and its solver, namely Gauss-Seidel algorithm, for securely outsourcing SVM training while reducing the client's computation. According to a primal estimated sub-gradient solver and replacing the SVs with data prototypes, the most recent work [54] gives a solution of training SVM model with data encrypted by homomorphic encryption.…”

Section: Related Workmentioning

confidence: 99%

Maximized Privacy-Preserving Outsourcing on Support Vector Clustering

et al. 2020

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Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a reformative SVC with elementary operations (RSVC-EO, the core of MPPSVC), in which a series of designs make selective outsourcing phase possible. In the training phase, we develop a dual coordinate descent solver, which avoids interactions before getting the encrypted coefficient vector. In the labeling phase, we design a fresh convex decomposition cluster labeling, by which no iteration is required by convex decomposition and no sampling checks exist in connectivity analysis. Afterward, we customize secure protocols to match these operations for essential interactions in the encrypted domain. Considering the privacy-preserving property and efficiency in a semi-honest environment, we proved MPPSVC's robustness against adversarial attacks. Our experimental results confirm that MPPSVC achieves comparable accuracies to RSVC-EO, which outperforms the state-of-the-art variants of SVC.

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Training Support Vector Machines with privacy-protected data

Cited by 46 publications

References 28 publications

Secure Outsourced Principal Eigentensor Computation for Cyber-Physical-Social Systems

Secure Outsourced Principal Eigentensor Computation for Cyber-Physical-Social Systems

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

Maximized Privacy-Preserving Outsourcing on Support Vector Clustering

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