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pro vyhledávání: '"Sasy, Sajin"'
Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection. While a rich set of tools exist for this purpose, there are currently no practical hyp
Externí odkaz:
http://arxiv.org/abs/2111.04906
Autor:
Sasy, Sajin, Ohrimenko, Olga
We study secure and privacy-preserving data analysis based on queries executed on samples from a dataset. Trusted execution environments (TEEs) can be used to protect the content of the data during query computation, while supporting differential-pri
Externí odkaz:
http://arxiv.org/abs/2009.13689