Adversarial VAE Sanitization for Privacy-Aware Data Sharing in Semiconductor Manufacturing

Abstract

Semiconductor manufacturing data sharing is constrained by intellectual property (IP) concerns, which makes it difficult to apply collaborative learning even when multiple installations of the same equipment exist. This paper studies the applicability of adversarial variational autoencoders (VAE) as a practical sanitization mechanism for this setting. Sensitive process variables are specified by a domain expert, and the model is trained to preserve utility while making those variables harder to infer from the released representation. We evaluate the approach on native semiconductor manufacturing time-series runs, first in the original single-party setting and then in a small multi-peer extension with local and joint adversaries. The results suggest a privacy-utility trade-off: sanitized representations retain the main public operating regimes while degrading reconstruction of the protected variables. The distributed study further shows that leakage must be evaluated after aggregation, not only per peer. These results position adversarial VAE sanitization as a useful applied tool for semiconductor data sharing, while also highlighting the limitations of the current centralized simulation and small number of available runs.