Microdata publishing should be privacy preserved as it may contain some sensitive information about an individual. Various anonymization techniques, generalization and bucketization, have been designed for privacy preserving microdata publishing. Generalization does not work better for high dimensional data. Bucketization failed to prevent membership disclosure and does not show a clear separation between quasi-identifiers and sensitive attributes. There are number of attributes in each record which can be categorized as 1) Identifiers such as Name or Social Security Number are the attributes that can be uniquely identify the individuals. 2)Some attributes may be Sensitive Attributes(SAs) such as disease and salary and 3) Some may be Quasi Identifiers (QI) such as zipcode, age, and sex whose values, when taken together, can potentially identify an individual. Data anonymization enables the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization. Here, we present a novel technique called slicing which partitions the data both horizontally and vertically. It preserves better data utility than generalization and is more effective than bucketization in terms of sensitive attribute.
| Published in |
American Journal of Networks and Communications (Volume 4, Issue 3-1)
This article belongs to the Special Issue Ad Hoc Networks |
| DOI | 10.11648/j.ajnc.s.2015040301.18 |
| Page(s) | 45-53 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
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APA Style
Shivani Rohilla, Megha Sharma, A. Kulothungan, Manish Bhardwaj. (2015). Privacy Preserving Data Publishing through Slicing. American Journal of Networks and Communications, 4(3-1), 45-53. https://doi.org/10.11648/j.ajnc.s.2015040301.18
ACS Style
Shivani Rohilla; Megha Sharma; A. Kulothungan; Manish Bhardwaj. Privacy Preserving Data Publishing through Slicing. Am. J. Netw. Commun. 2015, 4(3-1), 45-53. doi: 10.11648/j.ajnc.s.2015040301.18
AMA Style
Shivani Rohilla, Megha Sharma, A. Kulothungan, Manish Bhardwaj. Privacy Preserving Data Publishing through Slicing. Am J Netw Commun. 2015;4(3-1):45-53. doi: 10.11648/j.ajnc.s.2015040301.18
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Download@article{10.11648/j.ajnc.s.2015040301.18,
author = {Shivani Rohilla and Megha Sharma and A. Kulothungan and Manish Bhardwaj},
title = {Privacy Preserving Data Publishing through Slicing},
journal = {American Journal of Networks and Communications},
volume = {4},
number = {3-1},
pages = {45-53},
doi = {10.11648/j.ajnc.s.2015040301.18},
url = {https://doi.org/10.11648/j.ajnc.s.2015040301.18},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.s.2015040301.18},
abstract = {Microdata publishing should be privacy preserved as it may contain some sensitive information about an individual. Various anonymization techniques, generalization and bucketization, have been designed for privacy preserving microdata publishing. Generalization does not work better for high dimensional data. Bucketization failed to prevent membership disclosure and does not show a clear separation between quasi-identifiers and sensitive attributes. There are number of attributes in each record which can be categorized as 1) Identifiers such as Name or Social Security Number are the attributes that can be uniquely identify the individuals. 2)Some attributes may be Sensitive Attributes(SAs) such as disease and salary and 3) Some may be Quasi Identifiers (QI) such as zipcode, age, and sex whose values, when taken together, can potentially identify an individual. Data anonymization enables the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization. Here, we present a novel technique called slicing which partitions the data both horizontally and vertically. It preserves better data utility than generalization and is more effective than bucketization in terms of sensitive attribute.},
year = {2015}
}
TY - JOUR T1 - Privacy Preserving Data Publishing through Slicing AU - Shivani Rohilla AU - Megha Sharma AU - A. Kulothungan AU - Manish Bhardwaj Y1 - 2015/02/12 PY - 2015 N1 - https://doi.org/10.11648/j.ajnc.s.2015040301.18 DO - 10.11648/j.ajnc.s.2015040301.18 T2 - American Journal of Networks and Communications JF - American Journal of Networks and Communications JO - American Journal of Networks and Communications SP - 45 EP - 53 PB - Science Publishing Group SN - 2326-8964 UR - https://doi.org/10.11648/j.ajnc.s.2015040301.18 AB - Microdata publishing should be privacy preserved as it may contain some sensitive information about an individual. Various anonymization techniques, generalization and bucketization, have been designed for privacy preserving microdata publishing. Generalization does not work better for high dimensional data. Bucketization failed to prevent membership disclosure and does not show a clear separation between quasi-identifiers and sensitive attributes. There are number of attributes in each record which can be categorized as 1) Identifiers such as Name or Social Security Number are the attributes that can be uniquely identify the individuals. 2)Some attributes may be Sensitive Attributes(SAs) such as disease and salary and 3) Some may be Quasi Identifiers (QI) such as zipcode, age, and sex whose values, when taken together, can potentially identify an individual. Data anonymization enables the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization. Here, we present a novel technique called slicing which partitions the data both horizontally and vertically. It preserves better data utility than generalization and is more effective than bucketization in terms of sensitive attribute. VL - 4 IS - 3-1 ER -
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