A genetic TDS and BUG with pseudo-identifier for privacy preservation over incremental data sets

Sreedhar, K. C.; Faruk, M. N.; Venkateswarlu, B.

doi:10.3233/jifs-169229

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…• Subtree generalization: In this method, if a value of an attribute is generalized to its parent node, all other child nodes of that parent node need to be replaced with the parent node as well [17], [18].…”

Section: Generalization Techniquementioning

confidence: 99%

Privacy Preserving Location Data Publishing: A Machine Learning Approach

Shaham

Ding

Liu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to TDrive and Geolife location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

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Section: Generalization Techniquementioning

confidence: 99%

Privacy Preserving Location Data Publishing: A Machine Learning Approach

Shaham

Ding

Liu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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“…In [8], a genetic Bottom-Up Generalization (BUG) and Top-Down Specialization (TDS) model with pseudo-identity is presented to accomplish privacy preservation over incremental data sets in cloud storage. The data sets (DS) are partitioned in the preprocessing stage then clustered as groups.…”

mentioning

confidence: 99%

Special issue on soft computing and intelligent systems: Tools, techniques and applications

Thampi

El-Alfy

2017

IFS

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This special issue is a selected collection of papers submitted to the Second International Symposium on Intelligent Systems Technologies and Applications (ISTA'16), September 21-24, 2016, Jaipur, India. These papers have been reviewed and accepted for presentation at the symposium and for publication in the Journal of Intelligent & Fuzzy Systems (JIFS). In this special issue there are 44 papers covering a wide range of tools, techniques and applications of soft computing and intelligent systems.The International Symposium on Intelligent Systems Technologies and Applications aims to bring together researchers in related fields to explore and discuss various aspects of intelligent systems technologies and their applications. It provides excellent opportunities for the presentation of interesting new research results and discussion about them, leading to knowledge transfer and generation of new ideas.There are seven papers addressing research problems in bioinfomratics and medical research. In c-means (FCM) algorithm is proposed and evaluated on publicly available gene expression datasets. The results show that even with a few labeled samples, accurate prediction of cancer subtypes can be attained. In [2], a new texture-based feature extraction algorithm is proposed for extracting relevant and informative features from brain MR images having tumor. It is based on finding the texture description using nine different variants of texture objects and forming an intermediate texture index matrix using texture objects with high-pass and low-pass spiral filters. The resultant two-index matrix is used to generate the Texture Co-occurrence Matrix which helps to extract spatial and spectral domain features for brain MRI classification. Experiments on a dataset of 660 T1-weighted post contrast brain MR images with five different types of malignant tumors show that the proposed method can lead to significant results in abnormality classification when compared with the state-of-the-art GLCM and Run-length algorithms. In [3], a novel method is proposed for early detection and diagnosis of breast cancer in digital mammograms. The proposed technique uses Particle Swarm Optimization (PSO), Cellular Neural Network (CNN) and Probabilistic Neural Network (PNN). The breast mass texture feature extraction is carried out using Gray-Level Co-occurrence Matrix (GLCM)

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