Zero knowledge training refers to methods where a machine learning model is trained using private data without the data itself being revealed to the model trainer or other parties. This involves applying cryptographic techniques, such as secure multi-party computation or homomorphic encryption, during the training process. The goal is to preserve the confidentiality of sensitive datasets while still enabling the creation of accurate predictive models. It merges privacy with artificial intelligence.
Context
Zero knowledge training is an emerging field at the intersection of cryptography and artificial intelligence, holding significant promise for privacy-preserving applications in digital assets and beyond. It addresses the critical challenge of using sensitive financial or personal data for model development without compromising privacy. News often reports on research breakthroughs and new platforms that facilitate such training, indicating a future where AI can be leveraged more securely and confidentially.
ZKPoT consensus leverages zk-SNARKs to cryptographically verify decentralized model training performance, ensuring data privacy and robust, efficient machine learning on-chain.
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