On the Anonymization of Sparse High-Dimensional Data

@article{Ghinita2008OnTA,
  title={On the Anonymization of Sparse High-Dimensional Data},
  author={Gabriel Ghinita and Yufei Tao and Panos Kalnis},
  journal={2008 IEEE 24th International Conference on Data Engineering},
  year={2008},
  pages={715-724}
}
Existing research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to enforce privacy-preserving paradigms, such as k- anonymity and lscr-diversity, while minimizing the information loss incurred in the anonymizing process (i.e. maximize data utility). However, existing techniques adopt an indexing- or clustering- based approach, and work well for fixed-schema data, with low dimensionality. Nevertheless, certain applications require privacy… CONTINUE READING
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