The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
What Can We Learn Privately?
Open accessAbstract
Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in the contexts where aggregate information is released about a database containing sensitive information about individuals. We present several basic results that demonstrate general feasibility of private learning and relate several models previously studied separately in the contexts of privacy and standard learning.
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Citations
Cites
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
Self-citationValiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Our Data, Ourselves: Privacy Via Distributed Noise Generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835
An Introduction to Computational Learning Theory
Kearns, M. J., & Vazirani, U. (1994) · doi:10.7551/mitpress/3897.001.0001 · Cited by 1,697
Noise-tolerant learning, the parity problem, and the statistical query model
Blum, A., Kalai, A., & Wasserman, H. (2003) · Journal of the ACM · doi:10.1145/792538.792543 · Cited by 598
Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation
Kearns, M., & Ron, D. (1999) · Neural Computation · doi:10.1162/089976699300016304 · Cited by 407
Cited by
Differential Privacy: A Survey of Results
Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
(2011) · PubMed · Cited by 990
Boosting and Differential Privacy
Dwork, C., Rothblum, G. N., & Vadhan, S. (2010) · 2010 IEEE 51st Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2010.12 · Cited by 784
Datta, A., Sen, S., & Zick, Y. (2016) · 2016 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2016.42 · Cited by 728
Metadata
- Authors
- Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., & Smith, A.
- Venue
- 2008 49th Annual IEEE Symposium on Foundations of Computer Science
- Publisher
- IEEE
- Published
- 2008-10
- Pages
- 531-540
- ISSN
- 0272-5428 (ISSN-L)
- OpenAlex
- W2097272254
- Cited by
- 334
- References
- 6
- FWCI
- 12.41
- Type
- conference paper
- Language
- en
- Open access
- green
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- Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., & Smith, A. (2008). What Can We Learn Privately? 2008 49th Annual IEEE Symposium on Foundations of Computer Science. https://doi.org/10.1109/focs.2008.27
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