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Collaborative machine learning and related techniques such as federated learning allow multiple participants, each with his own training dataset, to build a joint model by training locally and periodically exchanging model updates.
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Generative adversarial nets
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Convolutional neural networks for sentence classification
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A data-driven approach to cleaning large face datasets
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Dropout: A simple way to prevent neural networks from overfitting
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CLiPS Stylometry Investigation (CSI) Corpus: A Dutch corpus for the detection of age, gender, personality, sentiment and deception in text
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
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Lasagne: First release
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Robust traceability from trace amounts
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Model inversion attacks that exploit confidence information and basic countermeasures
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Deep learning
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SparkNet: Training deep networks in Spark
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Privacy-preserving deep learning
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Petuum: A new platform for distributed machine learning on big data
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NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs
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A hybrid deep learning architecture for privacy-preserving mobile analytics
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DeepCity: A feature learning framework for mining location check-ins
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Semi-supervised knowledge transfer for deep learning from private training data
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Participatory cultural mapping based on collective behavior in location based social networks
D. Yang, D. Zhang, and B. Qu · 2015
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Beyond frontal faces: Improving person recognition using multiple cues
N. Zhang, M. Paluri, Y. Taigman, R. Fergus, and L. Bourdev · 2015
Cited alongside, same era.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Cited alongside, same era.
Membership Privacy in MicroRNA-based Studies
M. Backes, P. Berrang, M. Humbert, and P. Manoharan · 2016
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Censoring representations with an adversary
H. Edwards and A. Storkey · 2016
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Deep Learning
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio · 2016
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R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
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The Secret Sharer: Measuring unintended neural network memorization & extracting secrets
N. Carlini, C. Liu, J. Kos, Ú. Erlingsson, and D. Song · 2018
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Property inference attacks on fully connected neural networks using permutation invariant representations
K. Ganju, Q. Wang, W. Yang, C. A. Gunter, and N. Borisov · 2018
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https://en.wikipedia.org/wiki/General˙Data˙Protection˙Regulation , 2018
General Data Protection Regulation · 2018
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally · 2018
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Understanding membership inferences on well-generalized learning models
Y. Long, V. Bindschaedler, L. Wang, D. Bu, X. Wang, H. Tang, C. A. Gunter, and K. Chen · 2018
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Learning differentially private language models without losing accuracy
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2018
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Machine learning with membership privacy using adversarial regularization
M. Nasr, R. Shokri, and A. Houmansadr · 2018
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Towards reverse-engineering black-box neural networks
S. J. Oh, M. Augustin, M. Fritz, and B. Schiele · 2018
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Scalable private learning with PATE
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson · 2018
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Knock knock, who’s there? membership inference on aggregate location data
A. Pyrgelis, C. Troncoso, and E. De Cristofaro · 2018
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Towards demystifying membership inference attacks
S. Truex, L. Liu, M. E. Gursoy, L. Yu, and W. Wei · 2018
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Stealing hyperparameters in machine learning
B. Wang and N. Z. Gong · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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LOGAN: Membership inference attacks against generative models
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2019
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Deep private-feature extraction
S. A. Osia, A. Taheri, A. S. Shamsabadi, K. Katevas, H. Haddadi, and H. R. Rabiee · 2019
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