Dirk Englund

The necessity of multi-party computing has become increasingly evident due to the exploding demand for distributed machine learning. Offloading computationally intensive DNN inference to cloud servers introduces vulnerabilities that compromise user data security. To address this challenge, we introduce a coherent linear algebra engine for private multi-party computation of distributed machine learning tasks, leveraging conventional telecom components. We evaluate the fidelity of inner product computations, MNIST classification accuracy, and potential information leakage. Our analyses reveal a trade-off between classification accuracy and data privacy. This trade-off diminishes in significance for large-scale tasks, indicating the potential to achieve both classification accuracy and privacy simultaneously.