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Space adaptation: privacy-preserving multiparty collaborative mining with geometric perturbation

Published: 12 August 2007 Publication History

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No abstract available.

References

[1]
CHEN, K., AND LIU, L. A random rotation perturbation approach to privacy preserving data classification. Proc. of Intl. Conf. on Data Mining (ICDM) (2005).
[2]
CHEN, K., AND LIU, L. Towards attack-resilient geometric data perturbation. SIAM Data Mining Conference (2007).
[3]
ZHANG, N., WANG, S., AND ZHAO, W. A new scheme on privacy-preserving data classification. Proc. of ACM SIGKDD Conference (2005).

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  1. Space adaptation: privacy-preserving multiparty collaborative mining with geometric perturbation

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      cover image ACM Conferences
      PODC '07: Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
      August 2007
      424 pages
      ISBN:9781595936165
      DOI:10.1145/1281100
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 12 August 2007

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