  The need for data security and model integrity has been accentuated by the
rapid adoption of AI and ML in data-driven domains including healthcare,
finance, and security. Large models are crucial for tasks like diagnosing
diseases and forecasting finances but tend to be delicate and not very
scalable. Decentralized systems solve this issue by distributing the workload
and reducing central points of failure. Yet, data and processes spread across
different nodes can be at risk of unauthorized access, especially when they
involve sensitive information. Nesa solves these challenges with a
comprehensive framework using multiple techniques to protect data and model
outputs. This includes zero-knowledge proofs for secure model verification. The
framework also introduces consensus-based verification checks for consistent
outputs across nodes and confirms model integrity. Split Learning divides
models into segments processed by different nodes for data privacy by
preventing full data access at any single point. For hardware-based security,
trusted execution environments are used to protect data and computations within
secure zones. Nesa's state-of-the-art proofs and principles demonstrate the
framework's effectiveness, making it a promising approach for securely
democratizing artificial intelligence.
