Privacy-preserving machine learning for collaborative data sharing via auto-encoder latent space embeddings
Privacy-preserving machine learning in data-sharing processes is an ever-critical task that enables collaborative training of Machine Learning (ML) models without the need to share the original data sources. It is especially relevant when an organization must assure that sensitive data remains priva...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2022 |
| País: | Colombia |
| Institución: | Universidad de los Andes |
| Repositorio: | Séneca: repositorio Uniandes |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uniandes.edu.co:1992/64121 |
| Acceso en línea: | http://hdl.handle.net/1992/64121 |
| Access Level: | acceso abierto |
| Palabra clave: | Privacy preserving Machine learning Representation learning Ingeniería |
| Sumario: | Privacy-preserving machine learning in data-sharing processes is an ever-critical task that enables collaborative training of Machine Learning (ML) models without the need to share the original data sources. It is especially relevant when an organization must assure that sensitive data remains private throughout the whole ML pipeline, i.e., training and inference phases. This paper presents an innovative framework that uses Representation Learning via autoencoders to generate privacypreserving embedded data. Thus, organizations can share the data representation to increase machine learning models' performance in scenarios with more than one data source for a shared predictive downstream task. |
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