Timing is Everything: Inferring Temporal Membership in Continually Updated Models
First systematic study of temporal membership inference shows attackers can determine not just if but when data was used to train continually updated models — raising GDPR right-to-erasure concerns.
Summary written by editorial AI · Source link below
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. We formalize the notion of temporal membership in continually updated models. We then define the temporal membership
Editorial Analysis
Continually updated ML models in enterprise production may inadvertently leak temporal training-data membership, complicating GDPR erasure compliance and data-retention audits.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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