arXiv:2611.05781v1 Announce Type: new Abstract: Continual model updating has become a common practice in the deployment of machine learning systems. However, the dynamic training behaviors give rise to new risks not sufficiently addressed by existing studies. In this paper, we present the first systematic study of temporal privacy risk in continually updated deep learning models.
Timing is Everything: Inferring Temporal Membership in Continually Updated Models
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