Over the past several months, Algemetric has been exploring how privacy-preserving machine learning performs under more realistic network conditions.
Secure computation is often evaluated in environments where participating systems are located close together. In practice, organizations may need to collaborate across regions and networks, where latency, communication, and implementation choices can have a much greater impact on performance.
Our recent work has focused on comparing established secure-computation approaches, measuring communication and runtime across local and wide-area environments, and understanding how implementation decisions affect practical performance.
The objective is broader than demonstrating that the underlying cryptography works. We want to understand where privacy-preserving machine learning can provide meaningful real-world value, what practical barriers remain, and what would be required to move these approaches closer to deployment.
We’ll share more as this work progresses.
