Does enforcing anonymity mean decreasing data usefulness?

ABSTRACT

Preserving data privacy is becoming an urgent issue to cope with. Among different technologies, anonymization techniques offer many advantages, even if preliminary investigations suggest that they could deteriorate the usefulness of data. We carried out an empirical study in order to understand to which extent it is possible to enforce anonymization, and thus protect sensitive information, without degrading usefulness of data under unacceptable thresholds. Moreover, we analyzed also if re-writing queries could help reduce drawbacks of anonymization.

REFERENCES

• 1: Rakesh Agrawal , Jerry Kiernan , Ramakrishnan Srikant , Yirong Xu, Hippocratic databases, Proceedings of the 28th international conference on Very Large Data Bases, p.143-154, August 20-23, 2002, Hong Kong, China
• 2: V. R. Basili, G. Caldiera, and H. D. Rombach, "Goal/Question/Metric Paradigm." In Marciniak J. J., editor, Encyclopedia of Software Engineering, vol. 1, John Wiley & Sons, 1994, pp. 528--532.
• 3: Roberto J. Bayardo , Rakesh Agrawal, Data Privacy through Optimal k-Anonymization, Proceedings of the 21st International Conference on Data Engineering, p.217-228, April 05-08, 2005 [doi>10.1109/ICDE.2005.42]
• 4: Elisa Bertino , Beng Chin Ooi , Yanjiang Yang , Robert H. Deng, Privacy and Ownership Preserving of Outsourced Medical Data, Proceedings of the 21st International Conference on Data Engineering, p.521-532, April 05-08, 2005 [doi>10.1109/ICDE.2005.111]
• 5: Gerardo Canfora , Corrado Aaron Visaggio, Tuning anonymity level for assuring high data quality: an empirical study., Proceedings of the First International Symposium on Empirical Software Engineering and Measurement, p.91-98, September 20-21, 2007 [doi>10.1109/ESEM.2007.83]
• 6: Benjamin C. M. Fung , Ke Wang , Philip S. Yu, Top-Down Specialization for Information and Privacy Preservation, Proceedings of the 21st International Conference on Data Engineering, p.205-216, April 05-08, 2005 [doi>10.1109/ICDE.2005.143]
• 7: Gabriel Ghinita , Panagiotis Karras , Panos Kalnis , Nikos Mamoulis, Fast data anonymization with low information loss, Proceedings of the 33rd international conference on Very large data bases, September 23-27, 2007, Vienna, Austria
• 8: Vijay S. Iyengar, Transforming data to satisfy privacy constraints, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775089]
• 9: Grigorios Loukides , Jianhua Shao, Capturing data usefulness and privacy protection in K-anonymisation, Proceedings of the 2007 ACM symposium on Applied computing, March 11-15, 2007, Seoul, Korea [doi>10.1145/1244002.1244091]
• 10: Grigorios Loukides , Jianhua Shao, Data utility and privacy protection trade-off in k-anonymisation, Proceedings of the 2008 international workshop on Privacy and anonymity in information society, March 29-29, 2008, Nantes, France [doi>10.1145/1379287.1379296]
• 11: Daniel Kifer , Johannes Gehrke, Injecting utility into anonymized datasets, Proceedings of the 2006 ACM SIGMOD international conference on Management of data, June 27-29, 2006, Chicago, IL, USA [doi>10.1145/1142473.1142499]
• 12: Ashwin Machanavajjhala , Johannes Gehrke , Daniel Kifer , Muthuramakrishnan Venkitasubramaniam, \ell -Diversity: Privacy Beyond \kappa -Anonymity, Proceedings of the 22nd International Conference on Data Engineering, p.24, April 03-07, 2006 [doi>10.1109/ICDE.2006.1]
• 13: Ueli Maurer, The role of cryptography in database security, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, June 13-18, 2004, Paris, France [doi>10.1145/1007568.1007571]
• 14: M. Langheinrich, "Personal privacy in ubiquitous computing -Tools and System Support", PhD. Dissertation, ETH Zurich, Switzerland, May 2005.
• 15: L. Northrop, Ultra-Large-Scale System. The software Challenge of the Future, 2006, SEI Carnegie Mellon University.
• 16: L. Perusco, K. Michael, "Control, trust, privacy and security: evualting location-based services" Technology and Society Magazine, Vol. 26,(1), 2007, pp. 4--16.
• 17: Charles P. Pfleeger , Shari Lawrence Pfleeger, Security in Computing, Prentice Hall Professional Technical Reference, 2002
• 18: David Rosenblum, What Anyone Can Know: The Privacy Risks of Social Networking Sites, IEEE Security and Privacy, v.5 n.3, p.40-49, May 2007 [doi>10.1109/MSP.2007.75]
• 19: Stefan Sackmann , Jens Strüker , Rafael Accorsi, Personalization in privacy-aware highly dynamic systems, Communications of the ACM, v.49 n.9, September 2006 [doi>10.1145/1151030.1151052]
• 20: Latanya Sweeney, k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, v.10 n.5, p.557-570, October 2002 [doi>10.1142/S0218488502001648]
• 21: Latanya Sweeney, Datafly: A System for Providing Anonymity in Medical Data, Proceedings of the IFIP TC11 WG11.3 Eleventh International Conference on Database Securty XI: Status and Prospects, p.356-381, August 10-13, 1997
• 22: Brian Subirana , Malcolm Bain, Legal programming, Communications of the ACM, v.49 n.9, September 2006 [doi>10.1145/1151030.1151056]
• 23: Ke Wang , Philip S. Yu , Sourav Chakraborty, Bottom-Up Generalization: A Data Mining Solution to Privacy Protection, Proceedings of the Fourth IEEE International Conference on Data Mining, p.249-256, November 01-04, 2004
• 24: Jian Xu , Wei Wang , Jian Pei , Xiaoyuan Wang , Baile Shi , Ada Wai-Chee Fu, Utility-based anonymization using local recoding, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150504]
• 25: Platform for Privacy Preferences (P3P) Project, W3C, http://www.w3.org/P3P/ (last access on January 2007).

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.8 Metrics
          Subjects: Performance measures

General Terms:
Measurement, Security

Keywords:
anonymity, data privacy, data usefulness