HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

PrivacyFrost2: A Efficient Data Anonymization Tool Based on Scoring Functions

Abstract : In this paper, we propose an anonymization scheme for generating a k-anonymous and l-diverse (or t-close) table, which uses three scoring functions, and we show the evaluation results for two different data sets. Our scheme is based on both top-down and bottom-up approaches for full-domain and partial-domain generalization, and the three different scoring functions automatically incorporate the requirements into the generated table. The generated table meets users’ requirements and can be employed in services provided by users without any modification or evaluation.
Document type :
Conference papers
Complete list of metadata

Cited literature [32 references]  Display  Hide  Download

Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Monday, November 28, 2016 - 11:26:17 AM
Last modification on : Thursday, March 5, 2020 - 4:47:11 PM
Long-term archiving on: : Monday, March 20, 2017 - 7:08:28 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Shinsaku Kiyomoto, Yutaka Miyake. PrivacyFrost2: A Efficient Data Anonymization Tool Based on Scoring Functions. International Cross-Domain Conference and Workshop on Availability, Reliability, and Security (CD-ARES), Sep 2014, Fribourg, Switzerland. pp.211-225, ⟨10.1007/978-3-319-10975-6_16⟩. ⟨hal-01403997⟩



Record views


Files downloads