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
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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
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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
Differential Privacy for Deep and Federated Learning: A Survey
Ouadrhiri, A. E., & Abdelhadi, A. (2022) · IEEE Access · doi:10.1109/access.2022.3151670 · Cited by 445
Distributed Differential Privacy via Shuffling
Cheu, A., Smith, A., Ullman, J., Zeber, D., & Zhilyaev, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-17653-2_13 · Cited by 268
Self-citationAmplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., & Thakurta, A. (2019) · Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms · doi:10.1137/1.9781611975482.151 · Cited by 264
The Privacy Blanket of the Shuffle Model
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More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Zhu, T., Ye, D., Wang, W., Zhou, W., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3014246 · Cited by 219
Privacy Amplification by Iteration
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Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
Beimel, A., Nissim, K., & Stemmer, U. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40328-6_26 · Cited by 72
Self-citationInference under information constraints I: Lower bounds from chi-square contraction
Acharya, J., Canonne, C. L., & Tyagi, H. (2020) · IEEE Transactions on Information Theory · doi:10.1109/tit.2020.3028440 · Cited by 59
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Secure Federated Submodel Learning
Niu, C., Wu, F., Tang, S., Hua, L., Jia, R., Lv, C., Wu, Z., & Chen, G. (2019) · arXiv · doi:10.48550/arxiv.1911.02254 · Cited by 28
On the Power of Multiple Anonymous Messages
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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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