Tight Auditing of Differentially Private Machine Learning
暂无分享,去创建一个
Florian Tramèr | Nicholas Carlini | A. Terzis | T. Steinke | Milad Nasr | Jamie Hayes | Matthew Jagielski | B. Balle
[1] Edward Raff,et al. A General Framework for Auditing Differentially Private Machine Learning , 2022, NeurIPS.
[2] Alexandre Sablayrolles,et al. CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning , 2022, ArXiv.
[3] Florian Tramèr,et al. Measuring Forgetting of Memorized Training Examples , 2022, ICLR.
[4] Shruti Tople,et al. Bayesian Estimation of Differential Privacy , 2022, ArXiv.
[5] Jason M. Altschuler,et al. Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss , 2022, NeurIPS.
[6] Samuel L. Smith,et al. Unlocking High-Accuracy Differentially Private Image Classification through Scale , 2022, ArXiv.
[7] R. Shokri,et al. Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) , 2022, Neural Information Processing Systems.
[8] Florian Tramèr,et al. Debugging Differential Privacy: A Case Study for Privacy Auditing , 2022, ArXiv.
[9] Florian Tramèr,et al. Quantifying Memorization Across Neural Language Models , 2022, ICLR.
[10] Joseph P. Near,et al. Backpropagation Clipping for Deep Learning with Differential Privacy , 2022, ArXiv.
[11] Borja Balle,et al. Reconstructing Training Data with Informed Adversaries , 2022, 2022 IEEE Symposium on Security and Privacy (SP).
[12] Florian Tramèr,et al. Membership Inference Attacks From First Principles , 2021, 2022 IEEE Symposium on Security and Privacy (SP).
[13] Milad Nasr,et al. Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning , 2021, 2021 IEEE Symposium on Security and Privacy (SP).
[14] Jonathan Ullman,et al. Auditing Differentially Private Machine Learning: How Private is Private SGD? , 2020, NeurIPS.
[15] A. Honkela,et al. Computing Tight Differential Privacy Guarantees Using FFT , 2019, AISTATS.
[16] David Evans,et al. Evaluating Differentially Private Machine Learning in Practice , 2019, USENIX Security Symposium.
[17] Amir Houmansadr,et al. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[18] Úlfar Erlingsson,et al. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks , 2018, USENIX Security Symposium.
[19] Somesh Jha,et al. Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting , 2017, 2018 IEEE 31st Computer Security Foundations Symposium (CSF).
[20] Ilya Mironov,et al. Rényi Differential Privacy , 2017, 2017 IEEE 30th Computer Security Foundations Symposium (CSF).
[21] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[22] Pramod Viswanath,et al. The Composition Theorem for Differential Privacy , 2013, IEEE Transactions on Information Theory.
[23] Ian Goodfellow,et al. Deep Learning with Differential Privacy , 2016, CCS.
[24] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[25] L. Wasserman,et al. A Statistical Framework for Differential Privacy , 2008, 0811.2501.
[26] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[27] S P Azen,et al. OBTAINING CONFIDENCE INTERVALS FOR THE RISK RATIO IN COHORT STUDIES , 1978 .