作者
Daryna Oliynyk, Rudolf Mayer, Andreas Rauber
发表日期
2023/4/29
期刊
ACM Computing Surveys
页码范围
38
出版商
ACM
简介
Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making even the most complex Machine Learning models available for clients via, e.g., a pay-per-query principle. This allows users to avoid time-consuming processes of data collection, hyperparameter tuning, and model training. However, by giving their customers access to the (predictions of their) models, MLaaS providers endanger their intellectual property such as sensitive training data, optimised hyperparameters, or learned model parameters. In some cases, adversaries can create a copy of the model with (almost) identical behaviour using the the prediction labels only. While many variants of this attack have been described, only scattered defence strategies that address isolated threats have been proposed. To arrive at a comprehensive understanding why these attacks are successful and how they could be holistically …
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D Oliynyk, R Mayer, A Rauber - ACM Computing Surveys, 2023