With the rapid development of the digital economy, data has become a key production factor in the new era. However, transforming data into commercial value is not simply about storing it in the cloud; it requires addressing a series of complex challenges. These challenges mainly include issues related to privacy protection, Compliance, data verifiability, and Liquidity.

In this context, the Polygon network, with its unique advantages, provides a practical path for building a decentralized data market and facilitating the compliant circulation of AI training data. The advantages of Polygon are mainly reflected in three aspects: first, its low-cost on-chain settlement mechanism makes frequent data verification and small payments possible; second, the compatibility of Polygon with the Ethereum ecosystem allows developers to easily leverage existing identity verification, payment systems, and smart contract modules; finally, Polygon's deep investment in zero-knowledge (ZK) technology provides technical support for proving data attributes while protecting the privacy of the original data.

However, building an efficient decentralized data market still faces many challenges. These challenges include the decentralization of data ownership, data providers' concerns about privacy breaches, data buyers' demand for verifiability of data quality, and regulatory agencies' requirements for Compliance audits. Traditional centralized markets often solve these issues by sacrificing privacy or relying on centralized trust, while decentralized markets need to shift the basis of trust from interpersonal relationships to mathematical proofs to meet both trust and Compliance requirements.

The solutions provided by Polygon bring new ideas to these issues. For example, in the scenario of trading medical imaging data, data providers can keep the original files off-chain while submitting data fingerprints and zero-knowledge proofs on-chain to demonstrate that their data meets specific quality and diversity standards. This approach allows data purchasers to confirm data quality without directly accessing the original images, and to pay authorization fees accordingly.

In terms of business models, decentralized data markets provide more flexible options for data trading. Data can be sold in shards, billed based on usage, or charged according to the number of model inferences. Micropayment and pay-per-use mechanisms are particularly suitable for the testing and validation phases of data quality.

With the rapid development of artificial intelligence technology, the demand for high-quality, Compliance training data is increasing. Polygon provides the technical foundation for building a decentralized data market that balances privacy, Compliance, verifiability, and Liquidity, and is expected to promote the healthy development of the data economy in the AI era, creating a win-win situation for data providers, users, and regulators.