Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers

@inproceedings{Pathak2010MultipartyDP,
  title={Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers},
  author={Manas A. Pathak and Shantanu Rane and Bhiksha Raj},
  booktitle={NIPS},
  year={2010}
}
As increasing amounts of sensitive personal information finds its way into data repositories, it is important to develop analysis mechanisms that can derive aggregate information from these repositories without revealing information about individual data instances. Though the differential privacy model provides a framework to analyze such mechanisms for databases belonging to a single party, this framework has not yet been considered in a multi-party setting. In this paper, we propose a privacy… CONTINUE READING
Highly Cited
This paper has 54 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 42 extracted citations

55 Citations

01020'11'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 55 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 16 references

Scott Patterson . Privacy - preserving decision trees over vertically partitioned data

  • Jaideep Vaidya, Murat Kantarcioglu, Chris Clifton
  • TKDD
  • 2008

Similar Papers

Loading similar papers…