[ACG+16] Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In CCS, pages 308-318, 2016.
[AMS+22] Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam Smith, and Salil Vadhan. Differentially private simple linear regression. PETS, 2:184-204, 2022.
[BLM20] Mark Bun, Roi Livni, and Shay Moran. An equivalence between private classification and online prediction. In FOCS, 2020.
[BMS22] Raef Bassily, Mehryar Mohri, and Ananda Theertha Suresh. Differentially private learning with margin guarantees. In NeurIPS, 2022.
[BNS16] Amos Beimel, Kobbi Nissim, and Uri Stemmer. Private learning and sanitization: Pure vs. approximate differential privacy. ToC, 12(1):1-61, 2016.
[BNS18] Mark Bun, Jelani Nelson, and Uri Stemmer. Heavy hitters and the structure of local privacy. In PODS, pages 435-447, 2018.
[Kamalika Chaudhuri and Daniel Hsu. Sample complexity bounds for differentially private learning. In  $COLT$ , pages 155-186, 2011.]
[CJG21] Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. Data poisoning attacks to local differential privacy protocols. In USEnIX Security, pages 947-964, 2021.
[CMS11] Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. Differentially private empirical risk minimization. JMLR, 12(3), 2011.
$\left[\mathrm{DBH}^{+}22\right]$  Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle. Unlocking high-accuracy differentially private image classification through scale. arXiv preprint arXiv:2204.13650, 2022.
[DHS15] Ilias Diakonikolas, Moritz Hardt, and Ludwig Schmidt. Differentially private learning of structured discrete distributions. In NIPS, pages 2566-2574, 2015.
[DMNS06] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith. Calibrating noise to sensitivity in private data analysis. In TCC, pages 265-284, 2006.
[DR14] Cynthia Dwork and Aaron Roth. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211–407, 2014.
[EGS03] Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant. Limiting privacy breaches in privacy preserving data mining. In PODS, pages 211-222, 2003.
[EMSV22] Hossein Esfandiari, Vahab Mirrokni, Umar Syed, and Sergei Vassilvitskii. Label differential privacy via clustering. In AISTATS, pages 7055-7075, 2022.
$\left[\mathrm{GGK}^{+}21\right]$  Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, and Chiyuan Zhang. Deep learning with label differential privacy. NeurIPS, pages 27131-27145, 2021.
$\left[\mathrm{GKK}^{+}23\right]$  Badih Ghazi, Prish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangi, Avinash Varadarajan, and Chiyuan Zhang. Regression with label differential privacy. In ICLR, 2023.
$\left[\mathrm{GKM}^{+}21\right]$  Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Amer Sinha. Differentially private aggregation in the shuffle model: Almost central accuracy in almost a single message. In ICML, pages 3692-3701, 2021.
[GR21] Alexandre Gilotte and David Rohde. Learning a logistic model from aggregated data. AdKDD Workshop, 2021.
[GV14] Quan Geng and Pramod Viswanath. The optimal mechanism in differential privacy. In ISIT, pages 2371-2375, 2014.
[Har23] Charlie Harrison. Consider a randomized-response-like privacy mechanism, April 2023. github.com/patcg-individual-drafts/ipa/issues/60.
[Haz22] Elad Hazan. Introduction to Online Convex Optimization. MIT Press, 2022.
$\left[\mathrm{HLY}^{+}23\right]$  Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, and Jiang Bian. Exploring the limits of differentially private deep learning with group-wise clipping. In ICLR, 2023.