New Secure Computing Tech Handles AI, Video, and Data Without Leaking Secrets | HackerNoon
The study evaluates a framework for secure collaboration focusing on deep learning applications and privacy preservation during training and inference.
The Open-Source Privacy Project Making Untrusted Devices Work Together Securely | HackerNoon
Veracruz introduces a framework for designing privacy-preserving delegated computations among mutually mistrusting parties, leveraging isolates to safeguard computations from interference.
Detecting and Masking Personal Data in Text | HackerNoon
Text sanitization is critical in concealing personal identifiers, ensuring privacy while analyzing text data. The study evaluates a two-step sanitization approach's efficacy on various datasets.
The Trials and Triumphs of DPFL Research | HackerNoon
This paper reviews and evaluates the capabilities and limitations of Distributed Privacy-preserving Federated Learning (DPFL) methods, bridging theory with practical applications.