Privacy Preserving Support Vector Machine Using Non-linear Kernels on Hadoop Mahout

Teo, Sin G.; Han, Shi‐Kui; Lee, Vincent C. S.

doi:10.1109/cse.2013.200

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…More recently, Vaidya et al [51] showed how to train a SVM classifier, in a privacy-preserving manner, based on vertically, horizontally, and arbitrarily partitioned training data. In follow-up work, Teo et al [48] improved upon the efficiency of the solution of Vaidya et al [51], and showed that their approach scales well to address the challenges of data mining on big data. Chase et al [9] combine MPC techniques with differential privacy techniques to achieve private neural network learning.…”

Section: Related Workmentioning

confidence: 99%

EPIC: Efficient Private Image Classification (or: Learning from the Masters)

Makri

Rotaru

Smart

et al. 2019

Topics in Cryptology – CT-RSA 2019

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Outsourcing an image classification task raises privacy concerns, both from the image provider's perspective, who wishes to keep their images confidential, and from the classification algorithm provider's perspective, who wishes to protect the intellectual property of their classifier. We propose EPIC, an efficient private image classification system based on support vector machine (SVM) learning, secure against malicious adversaries. EPIC builds upon transfer learning techniques known from the Machine Learning (ML) literature and minimizes the load on the privacy-preserving part. Our solution is based on Secure Multiparty Computation (MPC), it is 34 times faster than Gazelle (USENIX 2018) -the state-of-the-art in private image classification-and it improves the communication cost by 50 times, with a 7% higher accuracy on CIFAR-10 dataset. For the same accuracy as Gazelle reaches on CIFAR-10, EPIC is 700 times faster and the communication cost is reduced by 500 times.

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Section: Related Workmentioning

confidence: 99%

EPIC: Efficient Private Image Classification (or: Learning from the Masters)

Makri

Rotaru

Smart

et al. 2019

Topics in Cryptology – CT-RSA 2019

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“…It is assumed that these parties are not willing to share their data directly, but would like to work together to learn the output of any agreed mining task. Various SMC techniques have been proposed to serve different mining tasks, such as decision tree [5], Naïve Bayes [27], Support Vector Machine [6], and Singular Value Decomposition [28], However, most of these techniques [29], [1] only consider secure addition and secure multiplication operations; they cannot be applied to the cases that include more complicated operations such as secure division. In addition, these techniques are ad-hoc, and thus difficult to be directly applied to mining tasks other than those they target.…”

Section: B Related Workmentioning

confidence: 99%

“…Such a property is very effective in protecting personal privacy. Thus, SMC has been extensively applied in privacy-preserving computation, such as decision tree [5] and Support Vector Machine [6] of the PPDM, and privacy preserving approaches on the cloud [7], [8]. However, most proposed techniques are ad-hoc and specific to tasks.…”

Section: Introductionmentioning

confidence: 99%

DAG: A Model for Privacy Preserving Computation

Teo

Cao

Lee

2015

2015 IEEE International Conference on Web Services

Self Cite

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Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. It has been extensively applied in privacy-preserving computation, such as privacy-preserving data mining (PPDM), to protect data privacy. However, most SMC-based solutions are ad-hoc. They are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed Acyclic Graph) that consists of a set of secure operators (e.g., multiplication and division). Our DAG model is general -its operators, if pipelined together, can implement various functions. It is also extendable -secure operators can be defined to add new features to the model. As an application study, we have applied our DAG to kernel regression. Experiments on datasets of more than 680,000 tuples show that our DAG model is effective and its running time is nearly thrice that of non-privacy setting, where parties directly disclose data.

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“…As a machine learning algorithm with high computational efficiency and nice predictive accuracy, the support vector machine (SVM) has achieved high classification accuracy and efficiency in the medical field [6,7]. However, the existing privacy preserving SVM schemes mainly implement secure prediction [8][9][10][11], and there are few privacy preserving SVM schemes for secure training. Most of the existing privacy preserving SVM schemes are designed for binary classification, which can only determine whether the patient has the disease [12], but cannot deal with the multiclass of the disease.…”

Section: Introductionmentioning

confidence: 99%

Practical Privacy Preserving-Aided Disease Diagnosis with Multiclass SVM in an Outsourced Environment

Zhao

Xie

Jia

et al. 2022

Security and Communication Networks

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With the rapid development of cloud computing and machine learning, using outsourced stored data and machine learning model for training and online-aided disease diagnosis has a great application prospect. However, training and diagnosis in an outsourced environment will cause serious challenges to the privacy of data. At present, many scholars have proposed privacy preserving machine learning schemes and made a lot of progress, but there are still great challenges in security and low client load. In this paper, we propose a complete privacy preserving outsourced multiclass SVM training and aided disease diagnosis scheme. We design some efficient basic operation algorithms for encrypted data. Then, we design an efficient and privacy preserving SVM model training protocol using the basic operation algorithms. We propose a secure maximum finding algorithm and secure comparison algorithm. Then, we design an efficient online-aided disease diagnosis scheme based on the BFV cryptosystem and blinding technique. Detailed security analysis proves that our scheme can protect the privacy of each participant. The experimental results illustrate that our proposed scheme significantly reduces the computation overhead compared with the previous similar works. Our proposed scheme completes most of the operations of aided disease diagnosis by the cloud servers and the client only needs to complete a small amount of encryption and decryption operations. The overall computation overhead is 0.175 s, and the efficiency of online aided disease diagnosis is improved by 85.4%. At the same time, our proposed scheme provides multiclass diagnosis results, which can better assist doctors in their treatment.

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Privacy Preserving Support Vector Machine Using Non-linear Kernels on Hadoop Mahout

Cited by 11 publications

References 14 publications

EPIC: Efficient Private Image Classification (or: Learning from the Masters)

EPIC: Efficient Private Image Classification (or: Learning from the Masters)

DAG: A Model for Privacy Preserving Computation

Practical Privacy Preserving-Aided Disease Diagnosis with Multiclass SVM in an Outsourced Environment

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