A generic and distributed privacy preserving classification method with a worst-case privacy guarantee

Banerjee, Madhushri; Chen, Zhiyuan; Gangopadhyay, Aryya

doi:10.1007/s10619-013-7126-6

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…R is considered as the orthonormal rotation matrix. Ultimately, the perturbed data are the resolution of product X by R. Many advances are brought out in this kind of techniques using classification (Chen, 2005;Banerjee, 2014) and transformation techniques such as SVM, KNN, K-means, Inner product and kernel methods.…”

Section: Random Rotation Based Perturbationmentioning

confidence: 99%

Privacy Preserving Through Fireworks Algorithm Based Model for Image Perturbation in Big Data

Rahmani

Amine

Hamou

et al. 2015

International Journal of Swarm Intelligence Research

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Nowadays, Social networks and cloud services contain billions of users over the planet. Instagram, Facebook and other networks give the opportunity to share images. Users upload millions of pictures each day, including personal images. Another domain, which concerns medical studies, requires a highly sensitive medical images that retain personal details close to patients. Image perturbation have attracted a great deal of attention in the last few years. Many works concerning image ciphering and perturbing have been published. This paper deals with the problem of image perturbation for privacy preserving. The authors build three new systems that consist of hiding small details in pictures by rotating some pixels. Their models use two algorithms: the first one involves a simulation of the firework algorithm in which they place fireworks on selected pixels then represents sparks as rotation processes. The second system consists of a model of rotation based perturbation using iterated local search algorithm (ILS) with 2 optimization stages. Meanwhile, the third one consists of using the same principle of the previous system except by using the ILS algorithm with 3 optimization stages.

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Section: Random Rotation Based Perturbationmentioning

confidence: 99%

Privacy Preserving Through Fireworks Algorithm Based Model for Image Perturbation in Big Data

Rahmani

Amine

Hamou

et al. 2015

International Journal of Swarm Intelligence Research

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“…Most existing privacy‐preserving methods for distance‐based mining are transformation based; in the worst case, the transformation may be recovered by attackers, and the original data will be compromised . In addition, another significant drawback with existing methods is that they are often quite expensive because of the use of encryption operations . In view of the limitations posed by the present approaches, there is a need to propose reliable and efficient privacy‐preserving approaches that achieve the tradeoff between data privacy and mining quality.…”

Section: Related Workmentioning

confidence: 99%

A DCT‐based privacy‐preserving approach for efficient data mining

Tian

Gui

et al. 2015

Security Comm Networks

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With the rapid growth of various data collected by companies and organizations, there is an increasing need for the data owners to share their data with the third party for the purpose of data mining. Therefore, protecting the privacy of the shared data has received considerable attention from academia and industry. However, existing methods do not work well for distance‐based mining algorithms. This paper proposes a novel privacy‐preserving approach to support distance‐based mining algorithms while reducing the shared data size. This approach transforms the shared data to discrete cosine transformation (DCT) coefficients, and adaptively selects DCT coefficients so as to form the compressed coefficient matrix. To enhance data privacy, this approach employs rotation‐based transformation with modified constraints to process the compressed coefficient matrix. Extensive experiments show that the proposed approach achieves the best tradeoff between data privacy and mining quality comparing with other existing ones. Copyright © 2015 John Wiley & Sons, Ltd.

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“…In this algorithm, homomorphism encryption, digital envelope and difference comparison method was used to protect the data privacy. In Part 1, the computational overhead mainly includes: O (1) for key generation [14], O (1) for encryption process, O (1) for generate t random numbers, O ( nlog(n) ) for the sorting of n numbers [15], O (n) for communication complexity, so the complexity of the whole is O ( nlog(n) ) + O (n). In Part 2, the communication complexity is O (t), computational complexity, including: O (1) for generating random numbers, O (n) for calculation process; the overall complexity is O (t) + O (n).…”

Section: Analysis Of Performanmentioning

confidence: 99%

Research on Privacy Preserving Classification Algorithm

Ren

2015

AMM

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In the process of data mining, how to operate the data mining as well as protect the private data is a problem must be solved. This paper proposed an improvement of decision tree classification algorithm. Homomorphism encryption system, digital envelopes technology and secret sorting are applied to protect the data privacy. Our contribution is a privacy preserving protocol consist of homomorphism encryption system and secret sorting. Analysis shows that this algorithm can get right results as well as protect the privacy of the private data.

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A generic and distributed privacy preserving classification method with a worst-case privacy guarantee

Cited by 5 publications

References 27 publications

Privacy Preserving Through Fireworks Algorithm Based Model for Image Perturbation in Big Data

Privacy Preserving Through Fireworks Algorithm Based Model for Image Perturbation in Big Data

A DCT‐based privacy‐preserving approach for efficient data mining

Research on Privacy Preserving Classification Algorithm

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