k m -Anonymity for Continuous Data Using Dynamic Hierarchies

@inproceedings{Gkountouna2014kM,
  title={k m -Anonymity for Continuous Data Using Dynamic Hierarchies},
  author={Olga Gkountouna and Sotiris Angeli and Athanasios Zigomitros and Manolis Terrovitis and Yannis Vassiliou},
  booktitle={Privacy in Statistical Databases},
  year={2014}
}
Many organizations, enterprises or public services collect and manage personal data of individuals. These data contain knowledge that is of substantial value for scientists and market experts, but carelessly disseminating them can lead to significant privacy breaches, as they might reveal financial, medical or other personal information. Several anonymization methods have been proposed to allow the privacy preserving sharing of datasets with personal information. Anonymization techniques… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 40 REFERENCES

The Role of Inference in the Anonymization of Medical Records

  • 2014 IEEE 27th International Symposium on Computer-Based Medical Systems
  • 2014
VIEW 1 EXCERPT

Privacy against aggregate knowledge attacks

  • 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW)
  • 2013
VIEW 1 EXCERPT

Flash: Efficient, Stable and Optimal K-Anonymity

  • 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
  • 2012