---
title: "Timing is Everything: Inferring Temporal Membership in Continually Updated Models"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05781v1"
permalink: "https://feed.astralor.com/items/itm_3de1770c139955937a1f53d8"
published: "2026-11-04T00:36:31.000Z"
score: 56
channel: "tech"
tags: ["推理效率","RAG","Timing","Everything","Inferring Temporal Membership","Continually Updated Models","Continual","However"]
---

# Timing is Everything: Inferring Temporal Membership in Continually Updated Models

## 速览

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…

## 关键点

- 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.

## 全文

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 inference attack, which seeks to infer whether and when individual samples were involved in model training, at both timestamp-level and interval-level granularities. To this end, we propose TMIA, a novel black-box attack that leverages bidirectional LSTMs to infer temporal membership from model outputs observed across successive updates. Through extensive experiments on three language models and three image classification models, we show that TMIA is highly effective and significantly outperforms existing baselines. Finally, we evaluate three potential defenses, revealing the trade-off between privacy protection and model utility.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_3de1770c139955937a1f53d8