Document Clustering with Semantic Analysis

Wang, Yong; Hodges, Julia E.

doi:10.1109/hicss.2006.129

Cited by 21 publications

(1 citation statement)

References 24 publications

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“…[41] [42][43] and data clustering[44] [45][46] [47] are applied. Moreover, the proposed algorithm is based on the assumption that all relatedly released data versions of D Γj ∪∆j are released from the timestamp 1 to j − 1, i.e., D Γ1∪∆1 , D Γ2∪∆2 , .…”

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confidence: 99%

Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Riyana

2023

Preprint

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A major challenge is when datasets are released to utilize in the outside scope of data-collecting organizations, it is how to balance data utilities and data privacies. To achieve this aim in data collection (datasets), there are several privacy preservation models that have been proposed such as k-Anonymity and l-Diversity. Unfortunately, these privacy preservation models can be sufficient to address privacy violation issues in datasets that do not have high-dimensional attributes. For this reason, a privacy preservation model, LKC-Privacy, can address privacy violation issues in high-dimensional datasets to be proposed. With this privacy preservation model, datasets cannot have any concern of privacy violation issues when all L-size distinct quasi-identifier values are distorted (suppressed or generalized) to be at least K indistinguishable tuples. Moreover, every protected sensitive value relates to each group of indistinguishable quasi-identifier values, it must have the confidence of re-identifications to be at most C. Although LKC-Privacy is more efficient and effective than k-Anonymity and l-Diversity, it is generally efficient and effective to address privacy violation issues in location-based datasets. Moreover, we see that datasets satisfy LKC-Privacy constraints, they still have privacy violation issues from using data comparison attacks and they further have data utility issues that must be addressed. Therefore, a new privacy preservation model can address privacy violation issues in high-dimensional datasets such that its satiable released datasets do not have any concern of privacy violation issues from using data comparison attacks and are highly efficient and effective in data maintenance to be proposed in this work. Furthermore, we show that the proposed model is more efficient and effective by using extensive experiments.

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Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Riyana

2023

Preprint

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Survey of Text Mining II

Berry¹,

Castellanos²

2008

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transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made.

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Enhancing Text Clustering Performance Using Semantic Similarity

Gad¹,

Kamel²

2009

Enterprise Information Systems

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Document Clustering with Semantic Analysis

Cited by 21 publications

References 24 publications

Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Survey of Text Mining II

Enhancing Text Clustering Performance Using Semantic Similarity

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