research-article

K-AMOA: K-Anonymity Model for Multiple Overlapped Attributes

Authors:

Bhakti Maheshwarkar,

Nidhi MaheshwarkarAuthors Info & Claims

ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

Article No.: 83, Pages 1 - 6

https://doi.org/10.1145/2905055.2905142

Published: 04 March 2016 Publication History

Abstract

In today's internet world, information can be search easily. There are verities of search engines present now a day's who gives information into some milliseconds. As information is easily available security of this information is a big issue. K-anonymity is one of the popular privacy preserving data publishing technique, which uses generalization technique. Generalization gives better data protection but leads loss of corelations among attribute. In some cases it fails to protect against membership disclosure. When in a dataset single attributes shows corelations with multiple attributes, then it becomes a big security and utility issue. In this paper we proposed K-AMOA Model who overcomes these drawbacks and provide better data utility for multiple overlapped attributes.

References

[1]

Tiancheng Li, Ninghui Li, Jian Zhang, Member, Ian Molloy, "Slicing: A New Approach for Privacy Preserving Data Publishing", IEEE Transactions on KDE, vol. 24, no. 3, march 2012"

Digital Library

[2]

C. Aggarwal, "On k-Anonymity and the Curse of Dimensionality," Proc. Int'l Conf. Very Large Data Bases (VLDB), pp. 901--909, 2005.

Digital Library

[3]

A. Blum, C. Dwork, F. McSherry, and K. Nissim, "Practical Privacy: The SULQ Framework," Proc. ACM Symp. Principles of Database Systems (PODS), pp. 128--138, 2005.

Digital Library

[4]

J. Brickell and V. Shmatikov, "The Cost of Privacy: Destruction of Data-Mining Utility in Anonymized Data Publishing," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2008.

Digital Library

[5]

B.-C. Chen, K. LeFevre, and R. Ramakrishnan, "Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge," Proc. Int'l Conf. Very Large Data Bases (VLDB), pp. 770--781, 2007.

Digital Library

[6]

H. Cramt'er, Mathematical Methods of Statistics. Princeton Univ. Press, 1948.

[7]

I. Dinur and K. Nissim, "Revealing Information while Preserving Privacy," Proc. ACM Symp. Principles of Database Systems (PODS),pp. 202--210, 2003.

Digital Library

[8]

C. Dwork, "Differential Privacy," Proc. Int'l Colloquium Automata, Languages and Programming (ICALP), pp. 1--12, 2006.

Digital Library

[9]

C. Dwork, "Differential Privacy: A Survey of Results," Proc. Fifth Int'l Conf. Theory and Applications of Models of Computation (TAMC), pp. 1--19, 2008.

Digital Library

[10]

C. Dwork, F. McSherry, K. Nissim, and A. Smith, "Calibrating Noise to Sensitivity in Private Data Analysis," Proc. Theory of Cryptography Conf. (TCC), pp. 265--284, 2006.

Digital Library

[11]

J.H. Friedman, J.L. Bentley, and R.A. Finkel, "An Algorithm for Finding Best Matches in Logarithmic Expected Time," ACM Trans. Math. Software, vol. 3, no. 3, pp. 209--226, 1977.

Digital Library

[12]

B.C.M. Fung, K. Wang, and P.S. Yu, "Top-Down Specialization for Information and Privacy Preservation," Proc. Int'l Conf. Data Eng. (ICDE), pp. 205--216, 2005.

Digital Library

[13]

G. Ghinita, Y. Tao, and P. Kalnis, "On the Anonymization of Sparse High-Dimensional Data," Proc. IEEE 24th Int'l Conf. Data Eng. (ICDE), pp. 715--724, 2008.

Digital Library

[14]

Nidhi Maheshwarkar, Kshitij Pathak, Vivekanand Chourey "N-SA K-anonymity Model: A Model Exclusive of Tuple Suppression Technique", IEEE, 2012 Third Global Congress on Intelligent Systems, Wuhan, China. November 6-8 2012. pp 229-232.987-0-7695-4860-9/12 DOI 10.1109/GCIS.2012.77.

Digital Library

[15]

Nidhi Maheshwarkar, Kshitij Pathak and Narendra S Choudhari. Article: K-anonymity Model for Multiple Sensitive Attributes. IJCA Special Issue on Optimization and On-chip Communication ooc (1):51--56, February 2012. Published by Foundation of Computer Science, New York, USA.

[16]

Nidhi Maheshwarkar Kshitij Pathak and Vivekanand Chourey "Performance Evaluation of various K-anonymity Techniques", Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technology 83501Y (); http://dx.doi.org/10.1117/12.921002.

[17]

Nidhi Maheshwarkar, Kshitij Pathak, Vivekanand Chourey "Privacy Issues for K-anonymity Model", International Journal of Engineering Research and Application, 2011, Vol. 1, Issue 4, pp. 1857--1861 ISSN No. 2248-9622.

[18]

Nidhi Maheshwarkar, Kshitij Pathak, Vivekanand Chourey "Performance Issues of Various K-anonymity Strategies", International Journal of Computer Technology and Electronics Engineering (IJCTEE), 2011, ISSN No. 2249-6343.

Cited By

Chourey NSoni R(2022)Privacy Preservation Technique Based on Sensitivity Levels for Multiple Numerical Sensitive Overlapped AttributesHybrid Intelligent Systems10.1007/978-3-030-96305-7_5(38-55)Online publication date: 4-Mar-2022
https://doi.org/10.1007/978-3-030-96305-7_5
Kalunge VDeepika S(2021)Data Mining Techniques for Privacy Preservation in Social Network Sites Using SVMTechno-Societal 202010.1007/978-3-030-69921-5_73(733-743)Online publication date: 20-May-2021
https://doi.org/10.1007/978-3-030-69921-5_73
Zhang LXuan JSi RWang R(2017)An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive AttributesWireless Personal Communications: An International Journal10.1007/s11277-016-3922-495:3(2003-2020)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s11277-016-3922-4

K-AMOA: K-Anonymity Model for Multiple Overlapped Attributes

Recommendations

Generalized bucketization scheme for flexible privacy settings

Bucketization is an anonymization technique for publishing sensitive data. The idea is to group records into small buckets to obscure the record-level association between sensitive information and identifying information. Compared to the traditional ...
An efficient privacy mechanism for electronic health records

Electronic health records (EHRs), digitization of patients' health record, offer many advantages over traditional ways of keeping patients' records, such as easing data management and facilitating quick access and real-time treatment. EHRs are a rich ...
N-SA K-anonymity Model: A Model Exclusive of Tuple Suppression Technique
GCIS '12: Proceedings of the 2012 Third Global Congress on Intelligent Systems

N-SA K-anonymity Model: A Model Exclusive of Tuple Suppression Technique is used to protect released data which contains multiple sensitive attributes. On the basis of K-anonymity model sensitive information cannot be distinguished from at least K-1 ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

March 2016

843 pages

ISBN:9781450339629

DOI:10.1145/2905055

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICTCS '16

ICTCS '16: Second International Conference on Information and Communication Technology for Competitive Strategies

March 4 - 5, 2016

Udaipur, India

Acceptance Rates

Overall Acceptance Rate 97 of 270 submissions, 36%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
81
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chourey NSoni R(2022)Privacy Preservation Technique Based on Sensitivity Levels for Multiple Numerical Sensitive Overlapped AttributesHybrid Intelligent Systems10.1007/978-3-030-96305-7_5(38-55)Online publication date: 4-Mar-2022
https://doi.org/10.1007/978-3-030-96305-7_5
Kalunge VDeepika S(2021)Data Mining Techniques for Privacy Preservation in Social Network Sites Using SVMTechno-Societal 202010.1007/978-3-030-69921-5_73(733-743)Online publication date: 20-May-2021
https://doi.org/10.1007/978-3-030-69921-5_73
Zhang LXuan JSi RWang R(2017)An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive AttributesWireless Personal Communications: An International Journal10.1007/s11277-016-3922-495:3(2003-2020)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s11277-016-3922-4

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten