Efficient near-optimal t-closeness with low information loss
Thammanna Gowda, Vikas Bagai, Rajiv Spilinek, Gerald Vitalapura, Spandana Siddaramanagowd
Thammanna Gowda, Vikas
Bagai, Rajiv
Spilinek, Gerald
Vitalapura, Spandana Siddaramanagowd
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2021-09-22
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Conference paper
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Keywords
t-Closeness,Earth mover’s distance,Generalization,Information loss
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Citation
V. T. Gowda, R. Bagai, G. Spilinek and S. Vitalapura, "Efficient Near-Optimal t-Closeness With Low Information Loss," 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2021, pp. 494-498, doi: 10.1109/IDAACS53288.2021.9661004
Abstract
The t-closeness technique is one of the most popular techniques for preserving individual privacy in data.
It involves generalizing and suppressing some attributes of a given table, after partitioning the set of all records of
that table into equivalence classes that satisfy a certain constraint. We present a new method to partition the set of
records of a table into such equivalence classes. Our method has several advantages over the existing methods for this
task. Firstly, the classes generated by our method are nearoptimal, in that they satisfy the t-closeness constraint for even the “smallest” t value for which t-closeness is achievable and useful for the given table, thereby providing the highest
amount of privacy. Secondly, our method generates classes of approximately the same size, resulting in low information
loss caused by generalization of attribute values. Lastly, while generating classes with minimum information loss
is known to be NP-hard, our classes with reasonably low information loss can be generated in just polynomial time.
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IEEE
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2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS);2021
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2770-4254
2770-4262
2770-4262