Differential privacy based on importance weighting-Reference-Cited by-同舟云学术

Differential privacy based on importance weighting

Published:2013-06-28 Issue:1 Volume:93 Page:163-183
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Ji Zhanglong,Elkan Charles

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s10994-013-5396-x.pdf

Reference26 articles.

1. Blum, A., Ligett, K., & Roth, A. (2008). A learning theory approach to non-interactive database privacy. In C. Dwork (Ed.), STOC (pp. 609–618). New York: ACM.

2. Chaudhuri, K., Monteleoni, C., & Sarwate, A. D. (2011). Differentially private empirical risk minimization. Journal of Machine Learning Research, 12, 1069–1109.

3. Ding, B., Winslett, M., Han, J., & Li, Z. (2011). Differentially private data cubes: optimizing noise sources and consistency. In SIGMOD conference (pp. 217–228).

4. Lecture notes in computer science;C. Dwork,2006

5. Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In S. Halevi & T. Rabin (Eds.), Lecture notes in computer science (Vol. 3876, pp. 265–284). Berlin: Springer.

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