Ownership preserving AI Market Places using Blockchain We present a blockchain based system that allows data owners, cloud vendors, and AI developers to collaboratively train machine learning models in a trustless AI marketplace. Data is a highly valued digital asset and central to deriving business insights. Our system enables data owners to retain ownership and privacy of their data, while still allowing AI developers to leverage the data for training. Similarly, AI developers can utilize compute resources from cloud vendors without loosing ownership or privacy of their trained models. Our system protocols are set up to incentivize all three entities  data owners, cloud vendors, and AI developers to truthfully record their actions on the distributed ledger, so that the blockchain system provides verifiable evidence of wrongdoing and dispute resolution. Our system is implemented on the Hyperledger Fabric and can provide a viable alternative to centralized AI systems that do not guarantee data or model privacy. We present experimental performance results that demonstrate the latency and throughput of its transactions under different network configurations where peers on the blockchain may be spread across different datacenters and geographies. Our results indicate that the proposed solution scales well to large number of data and model owners and can train up to 70 models per second on a 12peer non optimized blockchain network and roughly 30 models per second in a 24 peer network.

Crosschain Deals and Adversarial Commerce. Modern distributed data management systems face a new challenge: how can autonomous, mutuallydistrusting parties cooperate safely and effectively? Addressing this challenge brings up questions familiar from classical distributed systems: how to combine multiple steps into a single atomic action, how to recover from failures, and how to synchronize concurrent access to data. Nevertheless, each of these issues requires rethinking when participants are autonomous and potentially adversarial.  :66,propose the notion of a emphcrosschain deal, a new way to structure complex distributed computations in an adversarial setting. Deals are inspired by classical atomic transactions, but are necessarily different, in important ways, to accommodate the decentralized and untrusting nature of the exchange. We describe novel safety and liveness properties, along with two alternative protocols for implementing crosschain deals in a system of independent blockchain ledgers. One protocol, based on synchronous communication, is fully decentralized, while the other, based on eventuallysynchronous communication, necessarily requires stronger trust assumptions.