[JKT12] Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta. Differentially private online learning. In  $COLT$ , pages 24:1-24:34, 2012.
[KCS+22] Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta. Toward training at imagenet scale with differential privacy. CoRR, abs/2201.12328, 2022.
$\left[\mathrm{KLN}^{+}11\right]$  Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith. What can we learn privately? SIAM J. Comput., 40(3):793-826, 2011.
[KOV16] Peter Kairouz, Sewoong Oh, and Pramod Viswanath. Extremal mechanisms for local differential privacy. JMLR, 17:17:1-17:51, 2016.
[KST12] Daniel Kifer, Adam Smith, and Abhradeep Thakurta. Private convex empirical risk minimization and high-dimensional regression. In  $COLT$ , pages 25.1-25.40, 2012.
[LSY+21] Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, and Chong Wang. Label leakage and protection in two-party split learning. In ICLR, 2021.
$\left[\mathrm{MEMP}^{+}21\right]$  Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, and Florian Tramer. Antipodes of label differential privacy: PATE and ALIBI. NeurIPS, 34:6934-6945, 2021.
[PAE+17] Nicolas Papernot, Martin Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar. Semi-supervised knowledge transfer for deep learning from private training data. In ICLR, 2017.
[PS22] Nicolas Papernot and Thomas Steinke. Hyperparameter tuning with Rényi differential privacy. In ICLR, 2022.
[PSM+18] Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ülfar Erlingsson. Scalable private learning with PATE. In ICLR, 2018.
[RE19] Carey Radebaugh and Ulfar Erlingsson. Introducing TensorFlow Privacy: Learning with Differential Privacy for Training Data, March 2019. blog.tensorflow.org.
[SAZL18] Michael Thomas Smith, Mauricio A. Álvarez, Max Zwiessele, and Neil Lawrence. Differentially private regression with Gaussian processes. In AISTATS, 2018.
[SCS13] Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate. Stochastic gradient descent with differentially private updates. In GlobalSIP, pages 245-248, 2013.
[SSS23] Tom Sander, Pierre Stock, and Alexandre Sablayrolles. TAN without a burn: Scaling laws of DP-SGD. In ICML, pages 29937-29949, 2023.
[TM20] Davide Testuggine and Ilya Mironov. PyTorch Differential Privacy Series Part 1: DP-SGD Algorithm Explained, August 2020. medium.com.
[TNM+22] Xinyu Tang, Milad Nasr, Saeed Mahloujifar, Virat Shejwalkar, Liwei Song, Amir Houmansadr, and Prateek Mittal. Machine learning with differentially private labels: Mechanisms and frameworks. PETS, 4:332-350, 2022.
[TY18] Marcelo Tallis and Pranjul Yadav. Reacting to variations in product demand: An application for conversion rate (CR) prediction in sponsored search. In IEEE BigData, pages 1856-1864, 2018.
[Wan18] Yu-Xiang Wang. Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. In UAI, pages 93-103, 2018.
[War65] Stanley L Warner. Randomized response: A survey technique for eliminating evasive answer bias. JASA, 60(309):63-69, 1965.
[WDZ+19] Tianhao Wang, Bolin Ding, Jingren Zhou, Cheng Hong, Zhicong Huang, Ninghui Li, and Somesh Jha. Answering multi-dimensional analytical queries under local differential privacy. In SIGMOD, pages 159-176, 2019.
[WX19] Di Wang and Jinhui Xu. On sparse linear regression in the local differential privacy model. In ICML, pages 6628-6637, 2019.