Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata

Shivani Rohilla; Manish Bhardwaj

doi:doi:10.11648/j.ajdmkd.20170202.13

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Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata

Shivani Rohilla, Manish Bhardwaj

Published in American Journal of Data Mining and Knowledge Discovery (Volume 2, Issue 2)

Received: 11 January 2017 Accepted: 25 January 2017 Published: 22 February 2017

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Abstract

Nowadays, data and knowledge extracted by data mining techniques represent a key asset driving research, innovation, and policy-making activities. Many agencies and organizations have recognized the need of accelerating such trends and are therefore willing to release the data they collected to other parties, for purposes such as research and the formulation of public policies. However, the data publication processes are today still very difficult. Data often contains personally identifiable information and therefore releasing such data may result privacy breaches, this is the case for the examples of micro-data, e.g., census data and medical data. This thesis studies how we can publish and share micro data in privacy-preserving manner. This present a next ensive study of this problem along three dimensions: Designing a simple, intuitive, and robust privacy model, designing an effective anonymization technique that works on sparse and high-dimensional data and developing a methodology for evaluating privacy and utility tradeoffs. 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 Data Mining and Knowledge Discovery (Volume 2, Issue 2)
DOI	10.11648/j.ajdmkd.20170202.13
Page(s)	54-61
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), 2024. Published by Science Publishing Group

Keywords

Data Anonymization, Micro Data, PPDP, Slicing

References

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APA Style

Shivani Rohilla, Manish Bhardwaj. (2017). Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata. American Journal of Data Mining and Knowledge Discovery, 2(2), 54-61. https://doi.org/10.11648/j.ajdmkd.20170202.13

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ACS Style

Shivani Rohilla; Manish Bhardwaj. Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata. Am. J. Data Min. Knowl. Discov. 2017, 2(2), 54-61. doi: 10.11648/j.ajdmkd.20170202.13

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AMA Style

Shivani Rohilla, Manish Bhardwaj. Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata. Am J Data Min Knowl Discov. 2017;2(2):54-61. doi: 10.11648/j.ajdmkd.20170202.13

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@article{10.11648/j.ajdmkd.20170202.13,
  author = {Shivani Rohilla and Manish Bhardwaj},
  title = {Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {2},
  number = {2},
  pages = {54-61},
  doi = {10.11648/j.ajdmkd.20170202.13},
  url = {https://doi.org/10.11648/j.ajdmkd.20170202.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20170202.13},
  abstract = {Nowadays, data and knowledge extracted by data mining techniques represent a key asset driving research, innovation, and policy-making activities. Many agencies and organizations have recognized the need of accelerating such trends and are therefore willing to release the data they collected to other parties, for purposes such as research and the formulation of public policies. However, the data publication processes are today still very difficult. Data often contains personally identifiable information and therefore releasing such data may result privacy breaches, this is the case for the examples of micro-data, e.g., census data and medical data. This thesis studies how we can publish and share micro data in privacy-preserving manner. This present a next ensive study of this problem along three dimensions: Designing a simple, intuitive, and robust privacy model, designing an effective anonymization technique that works on sparse and high-dimensional data and developing a methodology for evaluating privacy and utility tradeoffs. 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 = {2017}
}

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TY  - JOUR
T1  - Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata
AU  - Shivani Rohilla
AU  - Manish Bhardwaj
Y1  - 2017/02/22
PY  - 2017
N1  - https://doi.org/10.11648/j.ajdmkd.20170202.13
DO  - 10.11648/j.ajdmkd.20170202.13
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 54
EP  - 61
PB  - Science Publishing Group
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UR  - https://doi.org/10.11648/j.ajdmkd.20170202.13
AB  - Nowadays, data and knowledge extracted by data mining techniques represent a key asset driving research, innovation, and policy-making activities. Many agencies and organizations have recognized the need of accelerating such trends and are therefore willing to release the data they collected to other parties, for purposes such as research and the formulation of public policies. However, the data publication processes are today still very difficult. Data often contains personally identifiable information and therefore releasing such data may result privacy breaches, this is the case for the examples of micro-data, e.g., census data and medical data. This thesis studies how we can publish and share micro data in privacy-preserving manner. This present a next ensive study of this problem along three dimensions: Designing a simple, intuitive, and robust privacy model, designing an effective anonymization technique that works on sparse and high-dimensional data and developing a methodology for evaluating privacy and utility tradeoffs. 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  - 2
IS  - 2
ER  -

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Author Information

Shivani Rohilla

Department of Computer Science and Engineering, HRIT, Ghaziabad, India
Manish Bhardwaj

Department of Computer Science and Engineering, SRM University, Modinagar, India

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APA Style

Shivani Rohilla, Manish Bhardwaj. (2017). Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata. American Journal of Data Mining and Knowledge Discovery, 2(2), 54-61. https://doi.org/10.11648/j.ajdmkd.20170202.13

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ACS Style

Shivani Rohilla; Manish Bhardwaj. Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata. Am. J. Data Min. Knowl. Discov. 2017, 2(2), 54-61. doi: 10.11648/j.ajdmkd.20170202.13

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AMA Style

Shivani Rohilla, Manish Bhardwaj. Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata. Am J Data Min Knowl Discov. 2017;2(2):54-61. doi: 10.11648/j.ajdmkd.20170202.13

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@article{10.11648/j.ajdmkd.20170202.13,
  author = {Shivani Rohilla and Manish Bhardwaj},
  title = {Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {2},
  number = {2},
  pages = {54-61},
  doi = {10.11648/j.ajdmkd.20170202.13},
  url = {https://doi.org/10.11648/j.ajdmkd.20170202.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20170202.13},
  abstract = {Nowadays, data and knowledge extracted by data mining techniques represent a key asset driving research, innovation, and policy-making activities. Many agencies and organizations have recognized the need of accelerating such trends and are therefore willing to release the data they collected to other parties, for purposes such as research and the formulation of public policies. However, the data publication processes are today still very difficult. Data often contains personally identifiable information and therefore releasing such data may result privacy breaches, this is the case for the examples of micro-data, e.g., census data and medical data. This thesis studies how we can publish and share micro data in privacy-preserving manner. This present a next ensive study of this problem along three dimensions: Designing a simple, intuitive, and robust privacy model, designing an effective anonymization technique that works on sparse and high-dimensional data and developing a methodology for evaluating privacy and utility tradeoffs. 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 = {2017}
}

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TY  - JOUR
T1  - Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata
AU  - Shivani Rohilla
AU  - Manish Bhardwaj
Y1  - 2017/02/22
PY  - 2017
N1  - https://doi.org/10.11648/j.ajdmkd.20170202.13
DO  - 10.11648/j.ajdmkd.20170202.13
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 54
EP  - 61
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20170202.13
AB  - Nowadays, data and knowledge extracted by data mining techniques represent a key asset driving research, innovation, and policy-making activities. Many agencies and organizations have recognized the need of accelerating such trends and are therefore willing to release the data they collected to other parties, for purposes such as research and the formulation of public policies. However, the data publication processes are today still very difficult. Data often contains personally identifiable information and therefore releasing such data may result privacy breaches, this is the case for the examples of micro-data, e.g., census data and medical data. This thesis studies how we can publish and share micro data in privacy-preserving manner. This present a next ensive study of this problem along three dimensions: Designing a simple, intuitive, and robust privacy model, designing an effective anonymization technique that works on sparse and high-dimensional data and developing a methodology for evaluating privacy and utility tradeoffs. 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  - 2
IS  - 2
ER  -

Copy | Download