Interactive anonymization of sensitive data

ABSTRACT

There has been much recent work on algorithms for limiting disclosure in data publishing, however they have not been put to use in any toolkit for practicioners. We will demonstrate CAT, the Cornell Anonymization Toolkit, designed for interactive anonymization. CAT has an interface that is easy to use; it guides users through the process of preparing a dataset for publication while limiting disclosure through the identification of records that have high risk under various attacker models.

REFERENCES

• 1: Kristen LeFevre , David J. DeWitt , Raghu Ramakrishnan, Incognito: efficient full-domain K-anonymity, Proceedings of the 2005 ACM SIGMOD international conference on Management of data, June 14-16, 2005, Baltimore, Maryland [doi>10.1145/1066157.1066164]
• 2: Ashwin Machanavajjhala , Daniel Kifer , Johannes Gehrke , Muthuramakrishnan Venkitasubramaniam, L-diversity: Privacy beyond k-anonymity, ACM Transactions on Knowledge Discovery from Data (TKDD), v.1 n.1, p.3-es, March 2007 [doi>10.1145/1217299.1217302]
• 3: Ashwin Machanavajjhala , Daniel Kifer , John Abowd , Johannes Gehrke , Lars Vilhuber, Privacy: Theory meets Practice on the Map, Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, p.277-286, April 07-12, 2008 [doi>10.1109/ICDE.2008.4497436]
• 4: P. Samarati, Protecting Respondents' Identities in Microdata Release, IEEE Transactions on Knowledge and Data Engineering, v.13 n.6, p.1010-1027, November 2001 [doi>10.1109/69.971193]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.7 Database Administration
          Subjects: Security, integrity, and protection

General Terms:
Design, Security

Keywords:
data anonymization, l-diversity