An Introduction to Privacy-Preserving Data Mining

  • Charu C. Aggarwal
  • Philip S. Yu
Part of the Advances in Database Systems book series (ADBS, volume 34)

The field of privacy has seen rapid advances in recent years because of the increases in the ability to store data. In particular, recent advances in the data mining field have lead to increased concerns about privacy. While the topic of privacy has been traditionally studied in the context of cryptography and information-hiding, recent emphasis on data mining has lead to renewed interest in the field. In this chapter, we will introduce the topic of privacy-preserving data mining and provide an overview of the different topics covered in this book.


Privacy-preserving data mining privacy randomization k-anonymity 


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  1. 1.
    Agrawal R., Srikant R. Privacy-Preserving Data Mining. ACM SIGMOD Conference, 2000.Google Scholar
  2. 2.
    Aggarwal C. C., Yu P. S.: A Condensation approach to privacy preserving data mining. EDBT Conference, 2004.Google Scholar
  3. 3.
    Aggarwal C. C., Yu P. S. On Variable Constraints in Privacy Preserving Data Mining. ACM SIAM Data Mining Conference, 2005.Google Scholar
  4. 4.
    Agrawal D. Aggarwal C. C. On the Design and Quantification of Privacy Preserving Data Mining Algorithms. ACM PODS Conference, 2002.Google Scholar
  5. 5.
    Aggarwal C. C. On k-anonymity and the curse of dimensionality. VLDB Conference, 2004.Google Scholar
  6. 6.
    Aggarwal C. C. On Randomization, Public Information, and the Curse of Dimensionality. ICDE Conference, 2007.Google Scholar
  7. 7.
    Bayardo R. J., Agrawal R. Data Privacy through optimal k-anonymization. ICDE Conference, 2005.Google Scholar
  8. 8.
    Blum A., Dwork C., McSherry F., Nissim K.: Practical Privacy: The SuLQ Framework. ACM PODS Conference, 2005.Google Scholar
  9. 9.
    Kenthapadi K.,Mishra N., Nissim K.: Simulatable Auditing, ACM PODS Conference, 2005.Google Scholar
  10. 10.
    Li F., Sun J., Papadimitriou S. Mihaila G., Stanoi I.: Hiding in the Crowd: Privacy Preservation on Evolving Streams through Correlation Tracking. ICDE Conference, 2007.Google Scholar
  11. 11.
    Machanavajjhala A., Gehrke J., Kifer D. -diversity: Privacy beyond k-anonymity. IEEE ICDE Conference, 2006.Google Scholar
  12. 12.
    Meyerson A., Williams R. On the complexity of optimal k-anonymity. ACM PODS Conference, 2004.Google Scholar
  13. 13.
    Nabar S., Marthi B., Kenthapadi K., Mishra N., Motwani R.: Towards Robustness in Query Auditing. VLDB Conference, 2006.Google Scholar
  14. 14.
    Pinkas B.: Cryptographic Techniques for Privacy-Preserving Data Mining. ACM SIGKDD Explorations, 4(2), 2002.Google Scholar
  15. 15.
    Rizvi S., Haritsa J. Maintaining Data Privacy in Association Rule Mining. VLDB Conference, 2002.Google Scholar
  16. 16.
    Samarati P., Sweeney L. Protecting Privacy when Disclosing Information: k-Anonymity and its Enforcement Through Generalization and Suppression. IEEE Symp. on Security and Privacy, 1998.Google Scholar
  17. 17.
    Verykios V. S., Elmagarmid A., Bertino E., Saygin Y.,, Dasseni E.: Association Rule Hiding. IEEE Transactions on Knowledge and Data Engineering, 16(4), 2004.Google Scholar
  18. 18.
    Xiao X., Tao Y.. Personalized Privacy Preservation. ACM SIGMOD Conference, 2006.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Charu C. Aggarwal
    • 1
  • Philip S. Yu
    • 2
  1. 1.IBM Thomas J. Watson Research CenterHawthorneUSA
  2. 2.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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