Non-Asymptotic Lower Bounds For Training Data Reconstruction
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[1] Zhili Chen,et al. SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing , 2022, ArXiv.
[2] Edward Raff,et al. A General Framework for Auditing Differentially Private Machine Learning , 2022, NeurIPS.
[3] C. Canonne. A short note on an inequality between KL and TV , 2022, 2202.07198.
[4] Alexandre Sablayrolles,et al. Defending against Reconstruction Attacks with Rényi Differential Privacy , 2022, ArXiv.
[5] Kamalika Chaudhuri,et al. Bounding Training Data Reconstruction in Private (Deep) Learning , 2022, International Conference on Machine Learning.
[6] Borja Balle,et al. Reconstructing Training Data with Informed Adversaries , 2022, 2022 IEEE Symposium on Security and Privacy (SP).
[7] Gilad Yehudai,et al. On the Optimal Memorization Power of ReLU Neural Networks , 2021, ICLR.
[8] Sivaraman Balakrishnan,et al. Heavy-tailed Streaming Statistical Estimation , 2021, AISTATS.
[9] Annabelle McIver,et al. The Laplace Mechanism has optimal utility for differential privacy over continuous queries , 2021, 2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS).
[10] Alec Radford,et al. Zero-Shot Text-to-Image Generation , 2021, ICML.
[11] Dan Boneh,et al. Differentially Private Learning Needs Better Features (or Much More Data) , 2020, ICLR.
[12] F. Kerschbaum,et al. Differentially Private Learning Does Not Bound Membership Inference , 2020, ArXiv.
[13] Salil Vadhan,et al. Differentially Private Simple Linear Regression , 2020, Proc. Priv. Enhancing Technol..
[14] Jonathan Ullman,et al. Auditing Differentially Private Machine Learning: How Private is Private SGD? , 2020, NeurIPS.
[15] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[16] G. A. Young,et al. High‐dimensional Statistics: A Non‐asymptotic Viewpoint, Martin J.Wainwright, Cambridge University Press, 2019, xvii 552 pages, £57.99, hardback ISBN: 978‐1‐1084‐9802‐9 , 2020, International Statistical Review.
[17] Guy Bresler,et al. A Corrective View of Neural Networks: Representation, Memorization and Learning , 2020, COLT.
[18] Annabelle McIver,et al. Generalised Differential Privacy for Text Document Processing , 2018, POST.
[19] R. Sarpong,et al. Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.
[20] Vitaly Feldman,et al. Privacy Amplification by Iteration , 2018, 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS).
[21] Somesh Jha,et al. Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting , 2017, 2018 IEEE 31st Computer Security Foundations Symposium (CSF).
[22] C. Dwork,et al. Exposed! A Survey of Attacks on Private Data , 2017, Annual Review of Statistics and Its Application.
[23] Ilya Mironov,et al. Rényi Differential Privacy , 2017, 2017 IEEE 30th Computer Security Foundations Symposium (CSF).
[24] Ian Goodfellow,et al. Deep Learning with Differential Privacy , 2016, CCS.
[25] Thomas Steinke,et al. Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds , 2016, TCC.
[26] Guy N. Rothblum,et al. Concentrated Differential Privacy , 2016, ArXiv.
[27] George J. Pappas,et al. Gradual Release of Sensitive Data under Differential Privacy , 2015, J. Priv. Confidentiality.
[28] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[29] Anand D. Sarwate,et al. Stochastic gradient descent with differentially private updates , 2013, 2013 IEEE Global Conference on Signal and Information Processing.
[30] Catuscia Palamidessi,et al. Broadening the Scope of Differential Privacy Using Metrics , 2013, Privacy Enhancing Technologies.
[31] L. Deng,et al. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web] , 2012, IEEE Signal Processing Magazine.
[32] Aleksandar Nikolov,et al. Optimal private halfspace counting via discrepancy , 2012, STOC '12.
[33] Jim Hefferon,et al. Linear Algebra , 2012 .
[34] Pravesh Kothari,et al. 25th Annual Conference on Learning Theory Differentially Private Online Learning , 2022 .
[35] Anand D. Sarwate,et al. Differentially Private Empirical Risk Minimization , 2009, J. Mach. Learn. Res..
[36] Kamalika Chaudhuri,et al. Privacy-preserving logistic regression , 2008, NIPS.
[37] Cynthia Dwork,et al. New Efficient Attacks on Statistical Disclosure Control Mechanisms , 2008, CRYPTO.
[38] Sofya Raskhodnikova,et al. What Can We Learn Privately? , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.
[39] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[40] Irit Dinur,et al. Revealing information while preserving privacy , 2003, PODS.
[41] S. Kasiviswanathan,et al. Balancing utility and scalability in metric differential privacy , 2022, UAI.
[42] Yin Tat Lee,et al. Network size and size of the weights in memorization with two-layers neural networks , 2020, NeurIPS.
[43] W. Hager,et al. and s , 2019, Shallow Water Hydraulics.
[44] Salil P. Vadhan,et al. The Complexity of Differential Privacy , 2017, Tutorials on the Foundations of Cryptography.
[45] W. Marsden. I and J , 2012 .
[46] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[47] W. Rudin. Principles of mathematical analysis , 1964 .