Blockchain-empowered crowdsourced computing system
In various embodiments, the present invention is directed to a decentralized and secure method for developing machine learning models using homomorphic encryption and blockchain smart contracts technology to realize a secure, decentralized system and privacy-preserving computing system incentivizes the sharing of private data or at least the sharing of resultant machine learning models from the analysis of private data. In various embodiments, the method uses a homomorphic encryption (HE)-based encryption interface designed to ensure the security and the privacy-preservation of the shared learning models, while minimizing the computation overhead for performing calculation on the encrypted domain and, at the same time, ensuring the accuracy of the quantitative verifications obtained by the verification contributors in the cipherspace.
1. A method for developing machine learning models through a decentralized and secure computing system, the method comprising the steps of:
establishing a peer to peer (P2P) network running on a blockchain platform and including a plurality of nodes capable of acting as any one of an application initiator node, a computing contributor node, and a verification contributor node;
wherein the application initiator node:
defines a task for generating a machine learning model, the task being defined through a blockchain smart contract, and
broadcasts the task to the P2P network via the blockchain smart contract such that the plurality of nodes receive the task and assist in the task as either computing contributor nodes or verification contributor nodes;
wherein each computing contributor node:
employs contributor-available data to generate a private model for achieving the task,
encrypts the private model, and
provides the encrypted private model for access by verification contributor nodes;
wherein each verification contributor node:
accesses the encrypted private model,
tests the private model to determine if it achieves the task, and
informs the application initiator node if a private model satisfies the task.
2. The method of claim 1 , further comprising decentralized storage for access by application initiator nodes, computing contributor nodes, and verification contributor nodes.
3. The method of claim 2 , wherein the application initiator node provides data or an initial machine learning model or both data and an initial machine learning model to decentralized storage for access by computing contributor nodes or verification contributor nodes or both computing contributor nodes and verification contributor nodes.
4. The method of claim 1 , wherein the private model is encrypted using homomorphic encryption.
5. The method of claim 1 , wherein the private model is encrypted using homomorphic encryption.
6. The method of claim 1 , wherein a public key and a secret key are generated by the application initiator, wherein said public key permits encryption of testing documents in the same manner as the private model so that the private model may be tested in cipherspace.
7. The method of claim 6 wherein said secret key may be used to decrypt the private model.
8. A method for developing machine learning models through a decentralized and secure computing system, the method comprising the steps of:
establishing a peer to peer (P2P) network running on a blockchain platform and including a plurality of nodes capable of acting as any one of an application initiator node, a computing contributor node, and a verification contributor node; and a decentralized storage accessible by said plurality of nodes;
wherein one of said plurality of nodes elects to act as an application initiator node, prepares a blockchain smart contract on said blockchain platform, said blockchain smart contract defining a task for generating a machine learning model, a criteria for selecting one or more nodes of said a plurality of nodes to act as a verification contributor node, a criteria for verifying completion of the task, and a criteria for triggering a transfer event, said task being broadcast the to the P2P network via the blockchain smart contract such that the plurality of nodes receive the task;
wherein said application initiator node generates a public key available to all of the nodes on said P2P network and a secret key available only to said application initiator node;
wherein each node receiving the tasks elects whether to assist with the task by acting as a computing contributor node, with the proviso that at least one of said plurality of nodes elects to act as a computing contributor node;
wherein each of said plurality of nodes acting as a computing contributor node employs contributor-available data to generate a private model for achieving the task, and stores an encrypted private model on the decentralized storage;
wherein one or more of said plurality of nodes is selected to act a verification contributor node according to the criteria for selecting one or more nodes of said a plurality of nodes to act as a verification contributor node set forth in the blockchain smart contract;
wherein each verification contributor node accesses the encrypted private model, tests the private model according to the criteria for verifying completion of the task set forth in the blockchain smart contract to determine if it achieves the task, and informs the blockchain smart contract on said blockchain platform if the private model satisfies the task;
wherein said blockchain smart contract on said blockchain platform determines whether the criteria for triggering a transfer event have been met, and if they have been met then allows said application initiator node to access and decrypt said private model.
9. The method of claim 8 , wherein the private model is encrypted using homomorphic encryption.
10. The method of claim 8 , wherein said public key permits encryption of testing documents in the same manner as the private model so that the private model may be tested in cipherspace.
11. The method of claim 8 wherein said secret key may be used to decrypt the private model.