These mechanisms enable secure, verifiable data trading across multiple domains. For example, a healthcare provider could tokenize anonymized patient records, encrypt them with AES-256, upload to IPFS with [[Proof of Space (PoSp)]] enforcement, and attach [[ZKP/ZKP Base Layer/ZKP Blockchain/Storage Layer/On-Chain Metadata Storage|ZKP-verified metadata.]] This [[ZKP/Data Marketplace/Tokenized Datasets/Comprehensive Mechanisms of Tokenized Datasets|tokenized asset]] could then be listed on the marketplace, with privacy preserved and availability guaranteed. Another scenario involves tokenizing a machine learning model. A data scientist might encrypt a convolutional neural network trained on image data, upload it to [[ZKP/Data Marketplace/High-Level Overview/Off-Chain Storage with IPFS|IPFS,]] and register it with metadata detailing its architecture and performance metrics. Consumers could [[ZKP/Data Marketplace/User Interactions/Data Purchaser Capabilities|purchase access]], [[ZKP/Data Marketplace/Tokenized Datasets/Encryption and ZKP Ownership Verification|using ZKPs to verify attributes]] like accuracy without exposing the model's weights. [[ZKP/Data Marketplace/Tokenized Datasets/Comprehensive Mechanisms of Tokenized Datasets|The tokenized dataset framework]] creates a comprehensive system for managing the complete [[ZKP/Data Marketplace/Tokenized Datasets/Lifecycle of Datasets/Lifecycle Management|lifecycle of data assets]], from creation and registration through versioning, [[ZKP/Data Marketplace/Tokenized Datasets/Tiered Access Control|access control]], and [[ZKP/Data Marketplace/Tokenized Datasets/Lifecycle of Datasets/Archival and Versioning|eventual archival or revocation]], all within a privacy-preserving, decentralized environment enabled by the modular architecture. See also: [[Proof Pods in the Data Marketplace|Proof Pods in the Data Marketplace]]