Membership Inference Attacks Against Machine Learning Models
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- Type
- article
- Published
- 2016-10-18
- Cited by
- 5,570
- References
- 40
- Access
- Open access
- OpenAlex
- https://openalex.org/W2535690855
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10488675
Keywords
Inference, Machine learning, Computer science, Artificial intelligence, Perspective (graphical)
References
- Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing
- Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
- To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout
- Distilling the Knowledge in a Neural Network
- Crypto-Nets: Neural Networks over Encrypted Data
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
- Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
- Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays
- Kernel Logistic Regression and the Import Vector Machine
- Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
- Privacy-preserving deep learning
- Private Predictive Analysis on Encrypted Medical Data
- Privacy preserving association rule mining in vertically partitioned data
- Dropout: a simple way to prevent neural networks from overfitting
- Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning
- Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
- On the Difficulties of Disclosure Prevention in Statistical Databases or The Case for Differential Privacy
- Privacy-preserving Naïve Bayes classification
- Privacy-preserving logistic regression
Cited by
- SoK: Security and Privacy in Machine Learning
- Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
- Personal Model Training under Privacy Constraints
- Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking
- LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks
- Opportunities and obstacles for deep learning in biology and medicine
- Detecting Adversarial Image Examples in Deep Neural Networks with Adaptive Noise Reduction
- Evading Classifier in the Dark: Guiding Unpredictable Morphing Using Binary-Output Blackboxes
- Privacy-Preserving Data Mining: Methods, Metrics, and Applications
- Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing
- A Survey on Resilient Machine Learning
- Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
- Knock Knock, Who's There? Membership Inference on Aggregate Location Data
- Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API
- Evading Classifiers by Morphing in the Dark
- On the Protection of Private Information in Machine Learning Systems: Two Recent Approches
- PassGAN: A Deep Learning Approach for Password Guessing
- Trojaning Attack on Neural Networks
- The Unintended Consequences of Overfitting: Training Data Inference Attacks
- Machine Learning Models that Remember Too Much