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Given a set of unlabeled images or (image, text) pairs, contrastive learning aims to pre-train an image encoder that can be used as a feature extractor for many downstream tasks.
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Differentially private data generative models
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AttriGuard: A practical defense against attribute inference attacks via adversarial machine learning. In USENIX Security Symposium
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Machine learning with membership privacy using adversarial regularization. In CCS
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Representation learning with contrastive predictive coding
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Stealing hyperparameters in machine learning. In IEEE S & P
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Privacy risk in machine learning: Analyzing the connection to overfitting. In CSF
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MBeacon: Privacy-Preserving Beacons for DNA Methylation Data. In NDSS
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Information leakage in embedding models. In CCS
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DeepSets
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FTC settlement with Ever orders data and AIs deleted after facial recognition pivot
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MicroImageNet classification challenge
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Extracting Training Data from Large Language Models. In USENIX Security Symposium
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Towards practical differentially private convex optimization. In IEEE S & P
Roger Iyengar, Joseph P Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang. 2019 · 2019
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Evaluating differentially private machine learning in practice. In USENIX Security Symposium
Bargav Jayaraman and David Evans. 2019 · 2019
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Memguard: Defending against black-box membership inference attacks via adversarial examples. In CCS
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong. 2019 · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In IEEE S & P
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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White-box vs black-box: Bayes optimal strategies for membership inference. In ICML
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou. 2019 · 2019
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Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. In NDSS
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2019 · 2019
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Label-only membership inference attacks. In ICML
Christopher A Choquette Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. 2021 · 2021
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Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19
Daryl LX Fung, Qian Liu, Judah Zammit, Carson Kai-Sang Leung, and Pingzhao Hu. 2021 · 2021
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Quantifying and Mitigating Privacy Risks of Contrastive Learning
Xinlei He and Yang Zhang. 2021 · 2021
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TransMIA: Membership Inference Attacks Using Transfer Shadow Training. In IJCNN
Seira Hidano, Takao Murakami, and Yusuke Kawamoto. 2021 · 2021
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Practical Blind Membership Inference Attack via Differential Comparisons. In NDSS
Bo Hui, Yuchen Yang, Haolin Yuan, Philippe Burlina, Neil Zhenqiang Gong, and Yinzhi Cao. 2021 · 2021
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Membership Inference Attacks and Defenses in Classification Models. In CODASPY
Jiacheng Li, Ninghui Li, and Bruno Ribeiro. 2021 · 2021
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Membership Leakage in Label-Only Exposures. In CCS
Zheng Li and Yang Zhang. 2021 · 2021
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Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. In IEEE S & P
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini. 2021 · 2021
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Learning transferable visual models from natural language supervision
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Systematic evaluation of privacy risks of machine learning models. In USENIX Security Symposium
Liwei Song and Prateek Mittal. 2021 · 2021
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BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning. In IEEE S & P
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong. 2022 · 2022
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