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Auflistung nach Autor:in "Lutsch, Adrian"

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  • Konferenzbeitrag
    Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads
    (BTW 2023, 2023) Lutsch, Adrian; Singh, Gagandeep; Mundt, Martin; Mogk, Ragnar; Binnig, Carsten
    For domains with high data privacy and protection demands, such as health care and finance, outsourcing machine learning tasks often requires additional security measures. Trusted Execution Environments like Intel SGX are a powerful tool to achieve this additional security. Until recently, Intel SGX incurred high performance costs, mainly because it was severely limited in terms of available memory and CPUs. With the second generation of SGX, Intel alleviates these problems. Therefore, we revisit previous use cases for ML secured by SGX and show initial results of a performance study for ML workloads on SGXv2.
  • Zeitschriftenartikel
    Kurz erklärt: Measuring Data Changes in Data Engineering and their Impact on Explainability and Algorithm Fairness
    (Datenbank-Spektrum: Vol. 21, No. 3, 2021) Klettke, Meike; Lutsch, Adrian; Störl, Uta
    Data engineering is an integral part of any data science and ML process. It consists of several subtasks that are performed to improve data quality and to transform data into a target format suitable for analysis. The quality and correctness of the data engineering steps is therefore important to ensure the quality of the overall process. In machine learning processes requirements such as fairness and explainability are essential. The answers to these must also be provided by the data engineering subtasks. In this article, we will show how these can be achieved by logging, monitoring and controlling the data changes in order to evaluate their correctness. However, since data preprocessing algorithms are part of any machine learning pipeline, they must obviously also guarantee that they do not produce data biases. In this article we will briefly introduce three classes of methods for measuring data changes in data engineering and present which research questions still remain unanswered in this area.