Demonstration of Damson: Differential privacy for analysis of large data

Marianne Winslett, Yin Yang, Zhenjie Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We demonstrate Damson, a novel and powerful tool for publishing the results of biomedical research with strong privacy guarantees. Damson is developed based on the theory of differential privacy, which ensures that the adversary cannot infer the presence or absence of any individual from the published results, even with substantial background knowledge. Damson supports a variety of analysis tasks that are common in biomedical studies, including histograms, marginals, data cubes, classification, regression, clustering, and ad-hoc selection-counts. Additionally, Damson contains an effective query optimization engine, which obtains high accuracy for analysis results, while minimizing the privacy costs of performing such analysis.

Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE 18th International Conference on Parallel and Distributed Systems, ICPADS 2012
Pages840-844
Number of pages5
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event18th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2012 - Singapore, Singapore
Duration: 17 Dec 201219 Dec 2012

Other

Other18th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2012
CountrySingapore
CitySingapore
Period17/12/1219/12/12

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ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Winslett, M., Yang, Y., & Zhang, Z. (2012). Demonstration of Damson: Differential privacy for analysis of large data. In Proceedings of the 2012 IEEE 18th International Conference on Parallel and Distributed Systems, ICPADS 2012 (pp. 840-844). [6413595] https://doi.org/10.1109/ICPADS.2012.137