Checking anonymity levels for anonymized data

Authors:
V. Valli Kumari;N. Sandeep Varma;A. Sri Krishna;K. V. Ramana;K. V. S. V. N. Raju
Affiliations:
Dept of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India;Dept of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India;Dept of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India;Dept of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India;Dept of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India
Venue:
ICDCIT'11 Proceedings of the 7th international conference on Distributed computing and internet technology
Year:
2011

Citing 11
Cited 0

A data distortion by probability distribution

ACM Transactions on Database Systems (TODS)
Data mining: concepts and techniques

Data mining: concepts and techniques
k-anonymity: a model for protecting privacy

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Personalized privacy preservation

Proceedings of the 2006 ACM SIGMOD international conference on Management of data
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Handicapping attacker's confidence: an alternative to k-anonymization

Knowledge and Information Systems
Providing k-anonymity in data mining

The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-preserving data publishing: A survey of recent developments

ACM Computing Surveys (CSUR)
An ad omnia approach to defining and achieving private data analysis

PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD

Quantified Score

Hi-index	0.00

Visualization

Abstract

Privacy Preserving Publication has become one of the most prominent research topics in the recent years. Several techniques like kanonymity, l-diversity and (α, k) anonymity were proposed to preserve privacy. Most of the published work focuses on anonymizing the microdata for preserving privacy and now the focus towards the verification of the anonymity levels of the microdata before publishing is the need of the day. Many publishers claim having anonymized the data. Verification of the claim on a large anonymized dataset is a herculean task. This paper focuses on providing simple approach for checking the anonymity levels for an anonymized dataset using frequent itemset generation. A GUI based tool named PRUDENT was developed to demonstrate the practicality of the solution. PRUDENT deals with numerical, categorical and multiple sensitive attributes. Results show that the algorithm is feasible and practical. A comparison with the existing methods is shown.