Web: http://arxiv.org/abs/2206.11480

June 24, 2022, 1:10 a.m. | Xun Xian, Mingyi Hong, Jie Ding

cs.LG updates on arXiv.org arxiv.org

The privacy of machine learning models has become a significant concern in
many emerging Machine-Learning-as-a-Service applications, where prediction
services based on well-trained models are offered to users via pay-per-query.
The lack of a defense mechanism can impose a high risk on the privacy of the
server's model since an adversary could efficiently steal the model by querying
only a few `good' data points. The interplay between a server's defense and an
adversary's attack inevitably leads to an arms race dilemma, …

arxiv defense extraction framework lg model understanding

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