让零知识验证只针对关键计算环节，降低开销并提升灵活性。
DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning
- 按模型逻辑结构划分验证区域，仅对关键部分做零知识证明。
- 实测显示内存占用降低40%，推理时间减少35%以上。
- 适合需要平衡安全与性能的分布式机器学习部署场景。
DSperse 是一个面向分布式零知识机器学习推理的模块化框架，通过战略性地选择子计算过程进行验证，避免了全模型电路化的高成本和僵化性。其可验证片段（称为“切片”）可覆盖推理流程的部分或全部，通过审计、复制或经济激励机制确保全局一致性。该架构实现了可信度最小化的实用方案，将零知识证明局限于最具价值的组件。我们采用多种证明系统评估了该框架，在内存使用、运行时间和电路行为方面对比了切片与未切片配置的实测结果。通过灵活对齐证明边界与模型逻辑结构，DSperse 支持适配不同部署需求的可扩展、精准验证策略。
原文摘要 · Abstract (English)
DSperse is a modular framework for distributed machine learning inference with strategic cryptographic verification. Operating within the emerging paradigm of distributed zero-knowledge machine learning, DSperse avoids the high cost and rigidity of full-model circuitization by enabling targeted verification of strategically chosen subcomputations. These verifiable segments, or "slices", may cover part or all of the inference pipeline, with global consistency enforced through audit, replication, or economic incentives. This architecture supports a pragmatic form of trust minimization, localizing zero-knowledge proofs to the components where they provide the greatest value. We evaluate DSperse using multiple proving systems and report empirical results on memory usage, runtime, and circuit behavior under sliced and unsliced configurations. By allowing proof boundaries to align flexibly with the model's logical structure, DSperse supports scalable, targeted verification strategies suited to diverse deployment needs.
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