An Effective Data Transformation Approach for Privacy Preserving Clustering

Rajalaxmi, R. R.; Natarajan, A. M.

doi:10.3844/jcssp.2008.320.326

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…This is because the geometric transformations are applied to the clusters so as to preserve their object membership and the orientation of the objects within the clusters. Other methods for privacy preserving clustering like [4] and [9] offer only a non-zero misclassification error. Therefore, data perturbation by cluster rotation does not affect the quality of the data.…”

Section: F Experimental Resultsmentioning

confidence: 99%

“…A set of hybrid data transformations have been introduced to preserve the confidentiality of categorical data in clustering [9].…”

Section: Motivationmentioning

confidence: 99%

“…In [9], a set of hybrid transformations has been introduced to ensure privacy of categorical data in clustering. The misclassification errors obtained after applying the hybrid data transformation techniques for various noise levels are computed and they are found to be the least for a noise level of 75%.…”

Section: Misclassification Error (M E )mentioning

confidence: 99%

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Privacy preservation in k-means clustering by cluster rotation

Dhiraj

Khan

et al. 2009

TENCON 2009 - 2009 IEEE Region 10 Conference

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The use of clustering as a data analysis tool has raised concerns about the violation of individual privacy. This paper proposes a data perturbation technique for privacy preservation in k-means clustering. Data objects that have been partitioned into clusters using k-means clustering are perturbed by performing geometric transformations on the clusters in such a way that the object membership of each cluster and orientation of objects within a cluster remain the same. This geometric transformation is achieved through cluster rotation, i.e., every cluster is rotated about its own centroid. The clusters are first displaced away from the mean of the entire dataset so that no two clusters overlap after the subsequent cluster rotation. We analyze the privacy measure offered by this data perturbation technique and prove that a dataset perturbed by this method cannot be easily reverse engineered, yet is still relevant for cluster analysis.

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Section: F Experimental Resultsmentioning

confidence: 99%

“…A set of hybrid data transformations have been introduced to preserve the confidentiality of categorical data in clustering [9].…”

Section: Motivationmentioning

confidence: 99%

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Privacy preservation in k-means clustering by cluster rotation

Dhiraj

Khan

et al. 2009

TENCON 2009 - 2009 IEEE Region 10 Conference

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“…In [22], a set of hybrid transformations has been introduced to ensure privacy of categorical data in clustering. The misclassification errors obtained after applying the hybrid data transformation techniques for various noise levels are computed and they are found to be the least for a noise level of 75%.…”

Section: B Misclassification Errormentioning

confidence: 99%

Privacy Preserving Outlier Detection Using Hierarchical Clustering Methods

Challagalla

Dhiraj

Somayajulu

et al. 2010

2010 IEEE 34th Annual Computer Software and Applications Conference Workshops

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Data objects which do not comply with the general behavior or model of the data are called Outliers. Outlier Detection in databases has numerous applications such as fraud detection, customized marketing, and the search for terrorism. However, the use of Outlier Detection for various purposes has raised concerns about the violation of individual privacy. Therefore, Privacy Preserving Outlier Detection must ensure that privacy concerns are addressed and balanced, so that the data analyst can get the benefits of outlier detection without being thwarted by legal counter-measures by privacy advocates. In this paper, we propose a technique for detecting outliers while preserving privacy, using hierarchical clustering methods. We analyze our technique to quantify the privacy preserved by this method and also prove that reverse engineering the perturbed data is extremely difficult.

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“…Hybrid transformations HDTTR and HDSTR were introduced in [5] for the privacy preservation of clustered data that ensured effectiveness of clustering of sensitive categorical data before and after transformation. The techniques present the results based on the mathematical characteristics of data.…”

Section: Iiliterature Surveymentioning

confidence: 99%

A cryptographic approach for achieving privacy in data mining

Abitha

Sarada

Manikandan

et al. 2015

2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]

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Data governs the present world. Data mining has become a vital tool among many industries and organizations for effective decision making. Data mining involves extracting patterns from large data sets to form an understandable structure for future use. In recent years, sharing of data between organizations has taken place for their development. Even though sharing of data has an advantage, data which contains sensitive attributes pose a threat of exposing private information. Thus privacy of the sensitive attributes is of a greater concern. This work aims at preserving privacy of sensitive attributes by using two modified cryptographic techniques namely, Rail Fence and Vigenere Cipher algorithms. The unique feature of this approach is that the encryption matrix is formed from the original data itself. The modified data is generated based on this key matrix. From our experimental results it is evident that the original data cannot be inferred from the modified data.

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An Effective Data Transformation Approach for Privacy Preserving Clustering

Cited by 17 publications

References 17 publications

Privacy preservation in k-means clustering by cluster rotation

Privacy preservation in k-means clustering by cluster rotation

Privacy Preserving Outlier Detection Using Hierarchical Clustering Methods

A cryptographic approach for achieving privacy in data mining

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