In today's increasingly important prediction markets and data analytics landscape, a long-neglected issue remains: how can users verify the authenticity of data while protecting their privacy? Traditional solutions either protect algorithmic privacy but fail to verify results, or open-source algorithms expose core business secrets, creating a trust dilemma. @brevis_zk's application at @Polymarket provides an answer: Zero-knowledge proof (ZK) verification: Ensures the computation process is correct without revealing algorithmic details. Privacy and trust go hand in hand: @KaitoAI's proprietary algorithms remain confidential, but users can verify that the data has not been manipulated. On-chain auditability: All verification activities are transparently recorded and traceable, establishing market trust. The implementation of Brevis @brevis_zk signifies that, for the first time, the prediction markets, sentiment analysis, and even the entire Web3 data industry have achieved infrastructure that ensures privacy without sacrificing trust, and trust without compromising privacy. This not only solves the industry's trust problem but also provides a verifiable paradigm for future large-scale privacy data applications, presenting a significant opportunity.