Summary
Scientific progress increasingly depends on sharing powerful machine learning models across stakeholders, especially in sensitive fields like medicine, genomics, and disaster response. Machine Learning as a Service (MLaaS) allows researchers to collaborate without directly exchanging proprietary machine learning models or private data. However, these systems face serious and growing security vulnerabilities. Adversaries can steal models, reconstruct sensitive inputs, or intercept private data in transit, putting years of investment and sensitive societal applications at risk. Current computing
