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Differentially private models seek to protect the privacy of data the model is trained on, making it an important component of model security and privacy.
Dropout: a simple way to prevent neural networks from overfitting
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Membership Inference Attack against Differentially Private Deep Learning Model
Md Atiqur Rahman, Tanzila Rahman, Robert Laganière, Noman Mohammed, and Yang Wang. 2018 · 2018
Differential privacy has disparate impact on model accuracy. In Advances in Neural Information Processing Systems . 15479–15488
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The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 19) . 267–284
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Network and Distributed Systems Security Symposium 2019 . Internet Society
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Extracting Training Data from Large Language Models
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Large Image Datasets: A Pyrrhic Win for Computer Vision?. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 1537–1547
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Our data, ourselves: Privacy via distributed noise generation. In Annual International Conference on the Theory and Applications of Cryptographic Techniques . Springer, 486–503
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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Hal Daumé III and Aarti Singh (Eds.), Vol. 119. PMLR, 5436–5446
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