EncryIP: A Practical Encryption-Based Framework for Model Intellectual
  Property Protection
Xin Mu Yu Wang Zhengan Huang Junzuo Lai Yehong Zhang Hui Wang Yue Yu
In the rapidly growing digital economy, protecting intellectual property (IP)
associated with digital products has become increasingly important. Within this
context, machine learning (ML) models, being highly valuable digital assets,
have gained significant attention for IP protection. This paper introduces a
practical encryption-based framework called \textit{EncryIP}, which seamlessly
integrates a public-key encryption scheme into the model learning process. This
approach enables the protected model to generate randomized and confused
labels, ensuring that only individuals with accurate secret keys, signifying
authorized users, can decrypt and reveal authentic labels. Importantly, the
proposed framework not only facilitates the protected model to multiple
authorized users without requiring repetitive training of the original ML model
with IP protection methods but also maintains the model's performance without
compromising its accuracy. Compared to existing methods like watermark-based,
trigger-based, and passport-based approaches, \textit{EncryIP} demonstrates
superior effectiveness in both training protected models and efficiently
detecting the unauthorized spread of ML models.