Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

Senekane, Makhamisa

doi:10.3390/make1010029

Cited by 30 publications

(25 citation statements)

References 22 publications

(23 reference statements)

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“…After a long time, Zhang et al (2019) have proposed a differentially private SVM based‐on dual variable perturbation. Recently, Senekane (2019) has performed differentially private image classification using SVM. For these differentially private SVM classification algorithms, Rubinstein et al (2009) and Zhang et al (2019) utilize output perturbation technique from differential privacy, while Senekane (2019) employs input perturbation technique.…”

Section: Differentially Private Classificationmentioning

confidence: 99%

“…Therefore, in this study, we focused on using input perturbation to perform differentially private classification task. To reach our goal, we adopted input perturbation technique of differential privacy as used in the studies of (Mivule et al, 2012; Sánchez et al, 2016; Sarwate & Chaudhuri, 2013; Senekane, 2019) to perform privacy preserving classification. We experimentally analyzed the performances of the well‐known classification algorithms that are C4.5, Naïve Bayes, Bayesian Networks, IBk, K*, One Rule, PART, Random tree, and Ripper for classification of the differentially private data, obtained by applying input perturbation to 8 widely used UCI datasets for various privacy levels by changing the ɛ values from 1 to 5, and also for small ɛ values (i.e., ɛ <1).…”

Section: Differentially Private Classificationmentioning

confidence: 99%

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Privacy preserving classification over differentially private data

Zorarpacı

Özel

2020

WIREs Data Min & Knowl

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Privacy preserving data classification is an important research area in data mining field. The goal of a privacy preserving classification algorithm is to protect the sensitive information as much as possible, while providing satisfactory classification accuracy. Differential privacy is a strong privacy guarantee that enables privacy of sensitive data stored in a database by determining the ratio of sensitive information leakage with respect to an ɛ parameter. In this study, our aim is to investigate the classification performance of the state‐of‐the‐art classification algorithms such as C4.5, Naïve Bayes, One Rule, Bayesian Networks, PART, Ripper, K*, IBk, and Random tree for performing privacy preserving classification. To preserve privacy of the data to be classified, we applied input perturbation technique coming from differential privacy, and observed the relationship between the ɛ parameter values and accuracy of the classifiers. To our best knowledge, this article is the first study that analyzes the performances of the well‐known classification algorithms over differentially private data, and discovers which datasets are more suitable for privacy preserving classification when input perturbation is applied to provide data privacy. The classification algorithms are compared by using the differentially private versions of the well‐known datasets from the UCI repository. According to the experimental results, we observed that, as ɛ parameter value increases, better classification accuracies are achieved with lower privacy levels. When the classifiers are compared, Naïve Bayes classifier is the most successful method. The ɛ parameter should be greater than or equal to 2 (i.e., ɛ ≥2) to achieve cloud server is malicious and untrusted, sensitive data will satisfactory classification accuracies. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Classification

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Section: Differentially Private Classificationmentioning

confidence: 99%

Section: Differentially Private Classificationmentioning

confidence: 99%

Privacy preserving classification over differentially private data

Zorarpacı

Özel

2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…It was expected to protect the small sample data and not to interfere with the classification effect of the model to the whole dataset. Makhamisa Senekane [17] reported a scheme for privacy-preserving image classification using Support Vector Machine and DP. SVM was chosen as a classification algorithm because unlike variants of artificial neural networks, it converged to a global optimum.…”

Section: Related Workmentioning

confidence: 99%

“…It can be seen from the analysis that the SVM classification algorithm based on the proposed differential privacy protection [10]- [17] has three types of problems: (1) When the training set was particularly large, the time consumption of the support vector machine prediction would be particularly large, and the noise would increase, and the accuracy would decrease. (2) The restriction on the objective function was overly strong, requiring it to remain convex and differentiable, so there was no universality.…”

Section: Related Workmentioning

confidence: 99%

“…When the optimization had not reached convergence and the number of iterations iter had reached the maximum maxIter, the algorithm would terminate the optimization process. Steps (4) to (17) endlessly switched between going through the entire dataset and going through the training points corresponding to the support vectors within the boundary, so as to select a sample point (x i , y i ) that violated the KKT conditions, and then the algorithm called innerLoop(i) of Algorithm 1 found another sample point (x j , y j ) that paired with it for optimization and updating. Steps (18) to (22) controlled the search for the first sample point that violated KKT conditions on that part of the dataset.…”

Section: ) Initialization Stagementioning

confidence: 99%

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A Differential Privacy Support Vector Machine Classifier Based on Dual Variable Perturbation

Zhang

Wang

2019

IEEE Access

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Data mining technology can be used to dig out potential and valuable information from massive data, and support vector machine (SVM) is one of the most widely used and most efficient methods in the field of data mining classification. However, the training set data often contains sensitive attributes, and the traditional training method of SVM reveals the individual privacy information. In view of the low prediction accuracy and poor versatility of the existing SVM classifiers with privacy protection, this paper proposed a new SVM training method for differential privacy protection. The algorithm first solved the dual problem of SVM by using SMO method and the difference E i between the estimated value and the real value for each support vector was recorded. Then the ratio of the E i of each support vector to the sum of the E i of all the support vectors was calculated. Next, different levels of Laplace random noise were added to the corresponding dual variables α i of each support vector to be released, according to the ratio of each support vector. According to the principle of differential privacy protection, the algorithm meets-differential privacy which can be used to effectively protect individual privacy. Experimental results on real datasets showed that the algorithm proposed in this paper could be used for classification prediction under a reasonable privacy budget.

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