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 701–800 of 998

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  23. In-Depth Exploration of Differentially Private Quantization for Privacy-Preserving and Communication-Efficient Federated Learning
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  26. Using Rényi-divergence and Arimoto-Rényi Information to Quantify Membership Information Leakage
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  30. Membership Inference Countermeasure With A Partially Synthetic Data Approach
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  31. BAN - MPR: Defending against Membership Inference Attacks with Born Again Networks and Membership Privacy Regularization
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  32. Can the use of privacy enhancing technologies enable federated learning for health data applications in a Swedish regulatory context?
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  33. Keeping it Low-Key: Modern-Day Approaches to Privacy-Preserving Machine Learning
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  34. Privacy vs Utility analysis when applying Differential Privacy on Machine Learning Classifiers
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  62. Two-Stage High Precision Membership Inference Attack
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  66. Ultimate Power of Inference Attacks: Privacy Risks of High-Dimensional Models.
    Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos · 2019 · arXiv (Cornell University)
  67. Membership Inference with Privately Augmented Data Endorses the Benign while Suppresses the Adversary.
    Dahua Yu, Huishuai Zhang, Wei Ren Chen, Jian Yin, Tie‐Yan Liu · 2020 · arXiv (Cornell University)
  68. Privacy Analysis in Language Models via Training Data Leakage Report.
    Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel N. Jones, Victor Rühle, James Withers, Robert B. Sim · 2021 · arXiv (Cornell University)
  69. Accuracy-Privacy Trade-off in Deep Ensembles.
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  70. Causally Constrained Data Synthesis for Private Data Release
    Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Kumar Sharma, Zhang Cheng, Shruti Tople · 2021 · arXiv (Cornell University)
  71. Novel Neural Network Repair Methods for Data Privacy and Individual Fairness
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  72. Quantifying and Localizing Private Information Leakage from Neural Network Gradients
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  73. Defining the Characteristics of Type I Interferon Stimulated Genes: Insight from Expression Data and Machine Learning
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  74. Proceedings of the Fourth Workshop on Privacy in Natural Language Processing
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  75. Variations and Extensions of Information Leakage Metrics with Applications to Privacy Problems with Imperfect Statistical Information
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  76. Inferring Communities of Interest in Collaborative Learning-based Recommender Systems
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  77. Privacy-Preserving Federated Learning Compatible with Robust Aggregators
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  79. Robust and Privacy-Preserving Federated Learning
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  81. MRMMIA: Membership Inference Attacks on Memory in Chat Agents
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  82. Membership Encoding for Deep Learning
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  83. Reducing Risk of Model Inversion Using Privacy-Guided Training
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  84. Not one but many Tradeoffs: Privacy Vs. Utility in Differentially Private Machine Learning
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  85. Quantifying identifiability to choose and audit $ε$ in differentially private deep learning
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  86. Formalizing Distribution Inference Risks
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  87. Leave-one-out Unfairness
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  88. Integrity and privacy in adversarial machine learning
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  91. Just Keep Your Concerns Private: Guaranteeing Heterogeneous Privacy and Achieving High Availability for ERM Algorithms
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  92. Defense Against Adversarial Attacks
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  94. Label-only membership inference attack based on the transferability of adversarial sample
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  95. Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
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  96. Squeeze-Loss: A Utility-Free Defense Against Membership Inference Attacks
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  98. Adversarial Machine Learning Problems: Threats and Attacks
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