Advancing Adoption of Privacy Preserving Machine Learning in AI-Enabled Enterprises: Efficient Homomorphic Encryption Through Radix Caching
Guardado en:
| Publicado en: | ProQuest Dissertations and Theses (2025) |
|---|---|
| Autor principal: | |
| Publicado: |
ProQuest Dissertations & Theses
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Resumen: | Privacy-preserving machine learning, especially through homomorphic encryption (HE), is essential to facilitate secure data processing in AI-driven enterprise systems. Nevertheless, HE's computational burden significantly restricts its practical implementation, particularly in critical domains like healthcare and edge devices, where data security and processing efficiency are essential. This study investigates a caching solution to mitigate this bottleneck, focusing on caching radix trees as a means to enhance the computational efficiency of HE-based processes. The research introduces and evaluates a caching approach that uses currently available homomorphic encryption libraries. The aim is to reduce the processing time of homomorphic encryption operations with the goal of making privacy-preserving machine learning applications more scalable. This optimization method deliberately caches ciphertexts, facilitating a transition from quadratic to almost linear computational complexity. Key performance indicators are examined, including the inference time and correctness of the computation across various HE schemes and compute configurations. The research findings indicate that radix tree caching can substantially improve HE performance, making it suitable for wider implementation in enterprise settings. This contribution tackles the efficiency difficulties of HE while creating a framework for privacy-preserving approaches that facilitate real-time, data-intensive applications without jeopardizing security or accuracy. This practice offers a pragmatic solution to a significant obstacle in privacy-preserving machine learning, enhancing both theoretical and applied understanding in secure AI. |
|---|---|
| ISBN: | 9798290963150 |
| Fuente: | ProQuest Dissertations & Theses Global |