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- W4376270037 abstract "Standard machine learning approaches rely on having access to all of the data relevant to a problem, but this does not map well to real-world situations comprising different entities that each have access to a subset of the information, but who do not wish to make all of their data available to each other due to commercial, legal or regulatory issues. Such cases can occur in fabs. Using data from the MADEin4 project, we show how a privacy-preserving augmented machine learning (PAML) approach can be applied to building virtual metrology models by combining data from two collaborating entities that have access to different aspects of the relevant data, and who wish to build the virtual metrology model whilst retaining control of their own data. The results show that the privacy-preserving PAML model nearly matches the performance of a model built from the pooled data, and significantly outperforms the models built using only the data available to individual entities." @default.
- W4376270037 created "2023-05-13" @default.
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- W4376270037 date "2023-05-01" @default.
- W4376270037 modified "2023-10-01" @default.
- W4376270037 title "Privacy-preserving Amalgamated Machine Learning for Virtual Metrology" @default.
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- W4376270037 doi "https://doi.org/10.1109/asmc57536.2023.10121077" @default.
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