Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom, Samuel, Giacomelli, Irene, Fredrikson, Matt, Jha, Somesh·2018·2018 IEEE 31st Computer Security Foundations Symposium (CSF)

Cited by (998)

Items 301–400 of 998

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  9. Evaluating the Vulnerability of End-to-End Automatic Speech Recognition Models to Membership Inference Attacks
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  11. Fast and Accurate Deep Leakage from Gradients Based on Wasserstein Distance
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  12. Differentially private federated learning with Laplacian smoothing
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  13. That which we call private
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  14. Alleviating Privacy Attacks via Causal Learning
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  15. Assessing differentially private deep learning with Membership Inference
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  16. Membership Leakage in Label-Only Exposures
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  17. Antipodes of Label Differential Privacy: PATE and ALIBI
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  18. Making Machine Learning Forget
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  26. Unexpected Information Leakage of Differential Privacy Due to the Linear Property of Queries
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  31. Privacy-Preserving Taxi-Demand Prediction Using Federated Learning
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  32. Data and Model Dependencies of Membership Inference Attack
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  33. Differentially Private Learning Does Not Bound Membership Inference.
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  34. On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times
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