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Towards a trustworthy, secure and reliable enclave for machine learning in a hospital setting: The Essen Medical Computing Platform (EMCP)

2022-01-13 · Hendrik F. R. Schmidt, Jörg Schlötterer, Marcel Bargull, Enrico Nasca, Ryan Aydelott, Christin Seifert, Folker Meyer

AI/Computing at scale is a difficult problem, especially in a health care setting. We outline the requirements, planning and implementation choices as well as the guiding principles that led to the implementation of our secure research computing enclave, the Essen Medical Computing Platform (EMCP), affiliated with a major German hospital. Compliance, data privacy and usability were the immutable requirements of the system. We will discuss the features of our computing enclave and we will provide our recipe for groups wishing to adopt a similar setup.

📄 PDF Abstract BibTeX arXiv:2201.04816

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Scalable Memory Protection in the PENGLAI Enclave

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Secure hardware enclaves have been widely used for protecting security-critical applications in the cloud. However, existing enclave designs fail to meet the requirements of scalability demanded by new scenarios like ser…

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With the increasing popularity of cloud based machine learning (ML) techniques there comes a need for privacy and integrity guarantees for ML data. In addition, the significant scalability challenges faced by DRAM couple…

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Privado: Practical and Secure DNN Inference with Enclaves

2018-10-01 · Karan Grover, Shruti Tople, Shweta Shinde, Ranjita Bhagwan 외

Cloud providers are extending support for trusted hardware primitives such as Intel SGX. Simultaneously, the field of deep learning is seeing enormous innovation as well as an increase in adoption. In this paper, we ask …