Conflicting Interactions Among Protection Mechanisms for Machine Learning Models. (arXiv:2207.01991v2 [cs.LG] UPDATED)

Nowadays, systems based on machine learning (ML) are widely used in different
domains. Given their popularity, ML models have become targets for various
attacks. As a result, research at the intersection of security/privacy and ML
has flourished. Typically such work has focused on individual types of
security/privacy concerns and mitigations thereof. However, in real-life
deployments, an ML model will need to be protected against several concerns
simultaneously. A protection mechanism optimal for one security or privacy
concern may interact negatively …

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