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Found 7 papers, 0 papers with code
Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach
Several domains increasingly rely on machine learning in their applications.
A Privacy-Preserving Federated Learning Approach for Kernel methods
An example for such a use case is machine learning on clinical data.
Bringing the Algorithms to the Data -- Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)
The need for data privacy and security -- enforced through increasingly strict data protection regulations -- renders the use of healthcare data for machine learning difficult.
CECILIA: Comprehensive Secure Machine Learning Framework
To address this, we propose a secure 3-party computation framework, CECILIA, offering PP building blocks to enable complex operations privately.
ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves
In this setting, it can also be a problem to compute the global AUC, since the labels might also contain privacy-sensitive information.
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare
We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework, enabling the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise.
Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework
Eye tracking is handled as one of the key technologies for applications that assess and evaluate human attention, behavior, and biometrics, especially using gaze, pupillary, and blink behaviors.
