The DOI matches its OpenAlex record; 1 of the 1 references it cites were found in a registry or an index.
Concentrated Differential Privacy
Open accessAbstract
Steinke [2025] recently asked the following intriguing open question: Can we solve the differentially private selection problem with nearly-optimal error by only (adaptively) invoking Gaussian mechanism on low-sensitivity queries? We resolve this question positively. In particular, for a candidate set 𝒴, we achieve error guarantee of Õ(log |𝒴|), which is within a factor of (log log |𝒴|)^{O(1)} of the exponential mechanism [McSherry and Talwar, 2007]. This improves on Steinke’s mechanism which achieves an error of O(log^{3/2} |𝒴|).
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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
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Cited by
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377
Mironov, I. (2017) · 2017 IEEE 30th Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2017.11 · Cited by 1,255
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
Dong, J., Roth, A., & Su, W. J. (2019) · arXiv · doi:10.48550/arxiv.1905.02383 · Cited by 389
Scalable Private Learning with PATE
Papernot, N., Song, S., Mironov, I., Raghunathan, A., Talwar, K., & Erlingsson, Ú. (2018) · arXiv · doi:10.48550/arxiv.1802.08908 · Cited by 373
Personalized Federated Learning With Differential Privacy
Hu, R., Guo, Y., Li, H., Pei, Q., & Gong, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991416 · Cited by 350
Evaluating Differentially Private Machine Learning in Practice
Jayaraman, B., & Evans, D. (2019) · arXiv · doi:10.48550/arxiv.1902.08874 · Cited by 265
Exposed! A Survey of Attacks on Private Data
Dwork, C., Smith, A., Steinke, T., & Ullman, J. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054123 · Cited by 259
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Wang, Y.-X., Balle, B., & Kasiviswanathan, S. (2018) · arXiv · doi:10.48550/arxiv.1808.00087 · Cited by 224
Rodríguez-Barroso, N., Stipcich, G., Jiménez-López, D., Ruiz-Millán, J. A., Martínez-Cámara, E., González-Seco, G., Luzón, M. V., Veganzones, M. A., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.07.009 · Cited by 148
The distributed discrete gaussian mechanism for federated learning with secure aggregation
Kairouz, P., Liu, Z., & Steinke, T. (2021) · arXiv · doi:10.48550/arxiv.2102.06387 · Cited by 117
Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning
Ma, C., Li, J., Wei, K., Liu, B., Ding, M., Yuan, L., Han, Z., & Vincent Poor, H. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3306773 · Cited by 96
Privacy Amplification by Iteration
Feldman, V., Mironov, I., Talwar, K., & Thakurta, A. (2018) · 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) · doi:10.1109/focs.2018.00056 · Cited by 80
Survey: Leakage and Privacy at Inference Time
Jegorova, M., Kaul, C., Mayor, C., O'Neil, A. Q., Weir, A., Murray-Smith, R., & Tsaftaris, S. A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3229593 · Cited by 77
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy
Behnia, R., Ebrahimi, M. R., Pacheco, J., & Padmanabhan, B. (2022) · 2022 IEEE International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw58026.2022.00078 · Cited by 60
CoinPress: Practical Private Mean and Covariance Estimation
Biswas, S., Dong, Y., Kamath, G., & Ullman, J. (2020) · arXiv · doi:10.48550/arxiv.2006.06618 · Cited by 29
Integrating Technical and Legal Concepts of Privacy
Sokolovska, A., & Kocarev, L. (2018) · IEEE Access · doi:10.1109/access.2018.2836184 · Cited by 24
A comprehensive survey and taxonomy on privacy-preserving deep learning
Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21
Minimax Rates of Estimating Approximate Differential Privacy
Liu, X., & Oh, S. (2019) · arXiv · doi:10.48550/arxiv.1905.10335 · Cited by 4
Metadata
- Authors
- Leeman, E., & Manurangsi, P.
- Venue
- arXiv (Cornell University)
- Publisher
- Cornell University
- Published
- 2016-03-06
- ISSN
- 2331-8422 (ISSN-L)
- OpenAlex
- W2294904676
- Cited by
- 271
- References
- 1
- Type
- posted content
- Language
- en
- Open access
- green
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