Efficient k -Anonymization Using Clustering Techniques

  title={Efficient k -Anonymization Using Clustering Techniques},
  author={Ji-Won Byun and Ashish Kamra and Elisa Bertino and Ninghui Li},
k-anonymization techniques are a key component of any comprehensive solution to data privacy and have been the focus of intense research in the last few years. An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications such as generalization and suppression. Current solutions, however, suffer from one or more of the following limitations: reliance on pre-defined generalization… CONTINUE READING
Highly Influential
This paper has highly influenced 27 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 240 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 146 extracted citations

Utility enhanced anonymization for incomplete microdata

2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) • 2016
View 5 Excerpts
Highly Influenced

Novel Approaches for Privacy Preserving Data Mining in K-Anonymity Model P

View 10 Excerpts
Highly Influenced

Electronic Medical Records privacy preservation through k-anonymity clustering method

The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems • 2012
View 7 Excerpts
Highly Influenced

A Hybrid Method for k-Anonymization

2008 IEEE Asia-Pacific Services Computing Conference • 2008
View 10 Excerpts
Highly Influenced

240 Citations

Citations per Year
Semantic Scholar estimates that this publication has 240 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 30 references

Mondrian Multidimensional K-Anonymity

22nd International Conference on Data Engineering (ICDE'06) • 2006
View 6 Excerpts
Highly Influenced

Transforming data to satisfy privacy constraints

KDD • 2002
View 5 Excerpts
Highly Influenced

Anonymizing Tables

ICDT • 2005
View 9 Excerpts
Highly Influenced

Data privacy through optimal k-anonymization

21st International Conference on Data Engineering (ICDE'05) • 2005
View 6 Excerpts
Highly Influenced

On the Complexity of Optimal K-Anonymity

View 8 Excerpts
Highly Influenced

k-Anonymity: A Model for Protecting Privacy

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems • 2002
View 6 Excerpts
Highly Influenced

Protecting respondent’s privacy in microdata release

P. Samarati
IEEE Transactions on Knowledge and Data Engineering, • 2001
View 5 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…