Tuning anonymity level for assuring high data quality: an empirical study.

Preserving data privacy is posing new challenges to software engineering researchers. Current technologies can be too cumbersome, pervasive or costly to be successfully applied in dynamic and complex scenarios where data exchange occurs among a large number of applications. Anonymization techniques seem to be a promising candidate, even if preliminary investigations suggest that they could deteriorate the quality of data. An empirical study has been carried out in order to understand the relationship between the anonymization level and the degradation of data quality.

[1]  ASHWIN MACHANAVAJJHALA,et al.  L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[2]  Ashwin Machanavajjhala,et al.  l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.

[3]  Vijay S. Iyengar,et al.  Transforming data to satisfy privacy constraints , 2002, KDD.

[4]  Rafael Accorsi,et al.  Personalization in privacy-aware highly dynamic systems , 2006, CACM.

[5]  J Thibault,et al.  Statistical data validation methods for large cheese plant database. , 2002, Journal of dairy science.

[6]  Douglas C. Schmidt,et al.  Ultra-large-scale systems , 2006, OOPSLA '06.

[7]  Charles P. Pfleeger,et al.  Security in computing , 1988 .

[8]  Roberto J. Bayardo,et al.  Technological Solutions for Protecting Privacy , 2003, Computer.

[9]  Ramakrishnan Srikant,et al.  Hippocratic Databases , 2002, VLDB.

[10]  Beng Chin Ooi,et al.  Privacy and ownership preserving of outsourced medical data , 2005, 21st International Conference on Data Engineering (ICDE'05).

[11]  Latanya Sweeney,et al.  Datafly: A System for Providing Anonymity in Medical Data , 1997, DBSec.

[12]  Philip S. Yu,et al.  Bottom-up generalization: a data mining solution to privacy protection , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[13]  Marc Langheinrich,et al.  Personal Privacy in Ubiquitous Computing , 2005 .

[14]  Brian Subirana,et al.  Legal programming , 2004, CACM.

[15]  Philip S. Yu,et al.  Top-down specialization for information and privacy preservation , 2005, 21st International Conference on Data Engineering (ICDE'05).

[16]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[17]  Ueli Maurer The role of cryptography in database security , 2004, SIGMOD '04.

[18]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[19]  Stephen G. MacDonell,et al.  Software Metrics Data Analysis—Exploring the Relative Performance of Some Commonly Used Modeling Techniques , 1999, Empirical Software Engineering.