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.

翻译：随着人工智能和机器学习在医疗健康、金融和安全等数据驱动领域的快速普及，对数据安全与模型完整性的需求日益凸显。大型模型在疾病诊断和财务预测等任务中至关重要，但其往往结构脆弱且可扩展性不足。去中心化系统通过分布式工作负载和减少中心化故障点来解决这一问题。然而，分散在不同节点的数据和流程可能面临未经授权访问的风险，尤其是在涉及敏感信息时。Nesa通过采用多重技术构建的综合框架应对这些挑战，以保护数据和模型输出。该框架包含用于安全模型验证的零知识证明，同时引入基于共识的验证机制以确保节点间输出一致性并确认模型完整性。分割学习将模型划分为由不同节点处理的片段，通过防止任何单点获取完整数据来保障数据隐私。在硬件安全层面，采用可信执行环境在安全区域内保护数据与计算过程。Nesa通过前沿的证明机制与设计原理验证了该框架的有效性，为安全实现人工智能民主化提供了可行路径。