PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement
Multimodal Large Language Models (MLLMs) have shown strong capabilities, but they may memorize private information from web data, raising privacy concerns. Machine unlearning offers a way to remove such private knowledge without retraining from scratch. However, existing MLLM unlearning benchmarks have two major limitations. First, they rely on simplified images that contain only the single target individual, failing to reflect the visual complexity of real-world photos. Second, they typically assume that the forget set and retain set are fully separated, ignoring the fact that private information is often visually entangled with benign public information. For example, a private individua...
| Authors | Xianren Zhang, Delvin Ce Zhang, Dongwon Lee et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 July 2026 |
| Last updated at source | 27 August 2026 |
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