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Found 12 papers, 3 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.
PlasmoFAB: A Benchmark to Foster Machine Learning for Plasmodium falciparum Protein Antigen Candidate Prediction
Results: We developed PlasmoFAB, a curated benchmark that can be used to train machine learning methods for the exploration of Plasmodium falciparum protein antigen candidates.
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.
COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data
By combining convolutional kernel networks with pathway-induced kernels, our method enables robust and interpretable end-to-end learning on omics datasets ranging in size from a few hundred to several hundreds of thousands of samples.
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.
Convolutional Motif Kernel Networks
Our proposed method can be utilized on DNA and protein sequences.
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.
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes
Due to the complexity of cancer, clustering algorithms have been used to disentangle the observed heterogeneity and identify cancer subtypes that can be treated specifically.
Towards multiple kernel principal component analysis for integrative analysis of tumor samples
Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies.
Partially blind domain adaptation for age prediction from DNA methylation data
We apply the model to the problem of age prediction based on DNA methylation data from a variety of tissues, and compare it to a standard model, which does not take heterogeneity into account.
