English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the rapidly growing digital economy, protecting intellectual property (IP)associated with digital products has become increasingly important. Within thiscontext, machine learning (ML) models, being highly valuable digital assets,have gained significant attention for IP protection. This paper introduces apractical encryption-based framework called \textit{EncryIP}, which seamlesslyintegrates a public-key encryption scheme into the model learning process. Thisapproach enables the protected model to generate randomized and confusedlabels, ensuring that only individuals with accurate secret keys, signifyingauthorized users, can decrypt and reveal authentic labels. Importantly, theproposed framework not only facilitates the protected model to multipleauthorized users without requiring repetitive training of the original ML modelwith IP protection methods but also maintains the model's performance withoutcompromising its accuracy. Compared to existing methods like watermark-based,trigger-based, and passport-based approaches, \textit{EncryIP} demonstratessuperior effectiveness in both training protected models and efficientlydetecting the unauthorized spread of ML models.

EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection