Protecting data: a fuzzy approach

Abstract

Privacy issues represent a longstanding problem nowadays. Measures such as k-anonymity, l-diversity and t-closeness are among the most used ways to protect released data. This work proposes to extend these three measures when the data are protected using fuzzy sets instead of intervals or representative elements. The proposed approach is then tested using Energy Information Authority data set and different fuzzy partition methods. Results shows an improvement in protecting data when data are encoded using fuzzy sets.

Keywords:

• k-anonymity
• l-diversity
• t-closeness
• fuzzy partition
• data privacy

2010 AMS Subject Classifications:

• 08A72
• 28E10
• 68T01
• 68T27
• 68T35

Acknowledgements

Authors acknowledge financial support Grant TEC2012-38142-C04-04 from Ministry of Education and Science, Government of Spain and Grant UNOV-13-EMERG-GIJON-10 from University of Oviedo.