On the use of spectral filtering for privacy preserving data mining

ABSTRACT

Randomization has been a primary tool to hide sensitive private information during privacy preserving data mining. The previous work based on spectral filtering, show the noise may be separated from the perturbed data under some conditions and as a result privacy can be seriously compromised. In this paper, we explicitly assess the effects of perturbation on the accuracy of the estimated value and give the explicit relation on how the estimation error varies with perturbation. In particular, we derive one upper bound for the Frobenius norm of reconstruction error. This upper bound may be exploited by attackers to determine how close their estimates are from the original data using spectral filtering technique, which imposes a serious threat of privacy breaches.

REFERENCES

• 1: Dakshi Agrawal , Charu C. Aggarwal, On the design and quantification of privacy preserving data mining algorithms, Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, p.247-255, May 2001, Santa Barbara, California, United States [doi>10.1145/375551.375602]
• 2: Rakesh Agrawal , Ramakrishnan Srikant, Privacy-preserving data mining, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.439-450, May 15-18, 2000, Dallas, Texas, United States
• 3: S. Guo and X. Wu. On the use of spectral filtering for privacy preserving data mining. In Technical Report, UNC Charlotte, June 2005.
• 4: Zhengli Huang , Wenliang Du , Biao Chen, Deriving private information from randomized data, Proceedings of the 2005 ACM SIGMOD international conference on Management of data, June 14-16, 2005, Baltimore, Maryland [doi>10.1145/1066157.1066163]
• 5: Hillol Kargupta , Souptik Datta , Qi Wang , Krishnamoorthy Sivakumar, On the Privacy Preserving Properties of Random Data Perturbation Techniques, Proceedings of the Third IEEE International Conference on Data Mining, p.99, November 19-22, 2003
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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.7 Database Administration
          Subjects: Security, integrity, and protection
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

  K. Computing Milieux
  K.6 MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS
      K.6.5 Security and Protection (D.4.6, K.4.2)

General Terms:
Algorithms, Design, Management, Security

Keywords:
disclosure analysis, privacy preserving, spectral filtering