Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Due diligence of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Due diligence of documents is faster and simpler. An electronic mortgage application, for example, often contains or references a collection of many separate electronic documents. Electronic data representing an original version of an electronic document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic document has not changed since its creation. However, if the digital signatures do not match, then the electronic document has changed since its creation. The auditor may thus flag the electronic document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Due diligence of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Owners/Holders of different cryptographic coinages may buy, sell, trade, or otherwise convert different cryptographic coinages via an intermediary in a decentralized manner. Multiple and different cryptographic tokens may be pegged to different assets. The different cryptographic tokens are value related based on cryptographic exchange rates. Whenever an individual user or owner requests a market transaction (such as a buy or sell order), at least one of a destruction operation and a creation operation are performed. The destruction operation destroys or removes at least one of the cryptographic tokens, while the creation operation creates or injects new ones of a different cryptographic token. Owners/Holders may thus exchange or convert between different cryptographic assets, depending on their restive values and exchange rates.

Abstract: Digital or “smart” contracts execute in a blockchain environment. Any entity (whether public or private) may specify a digital contract via a blockchain. Because there may be many digital contracts offered as virtual services, the contract identifier uniquely identifies a particular decision table and/or the digital contract offered by a virtual machine, vendor or supplier. The blockchain is thus not burdened with the programming code that is required to execute the decision table and/or the digital contract. The blockchain need only include or specify the contract identifier (and perhaps one or more contractual parameters), thus greatly simplifying the blockchain and reducing its size (in bytes) and processing requirements.

Abstract: A Factom protocol cost effectively separates any blockchain (such as the Bitcoin blockchain) from any cryptocurrency (such as the Bitcoin cryptocurrency). The Factom protocol provides client-defined Chains of Entries, client-side validation of Entries, a distributed consensus algorithm for recording the Entries, and a blockchain anchoring approach for security.

Abstract: A multi-coin mechanism for maintaining a stable value of cryptographic coinage traded in a decentralized market exchange without requiring a reserve. Multiple, pegged cryptographic tokens are traded in the reserveless decentralized market exchange. Each of the multiple, pegged cryptographic tokens may be pegged to a different asset (such as different currencies and/or commodities). The multiple, pegged cryptographic tokens are value related based on cryptographic exchange rates. Whenever a market transaction is processed (such as a buy or sell order), at least one of a destruction operation and a creation operation are performed. The destruction operation destroys at least one of the pegged cryptographic tokens, while the creation operation creates new ones of the pegged cryptographic tokens n.

Abstract: A two-coin mechanism for maintaining a stable value of cryptographic coinage traded in a decentralized market exchange without requiring a reserve. A pegged cryptographic token and a variable-priced cryptographic token are both traded in the reserveless decentralized market exchange. The pegged cryptographic token and the variable-priced cryptographic token are value related based on a cryptographic exchange rate. Whenever a market transaction is processed (such as a buy or sell order), at least one of a destruction operation and a creation operation are performed. The destruction operation destroys at least one of the pegged cryptographic token and/or the variable-priced cryptographic token, while the creation operation creates new ones of the pegged cryptographic token and/or the variable-priced cryptographic token.

Abstract: Accounts receivables, accounts payables, and other debt instruments are registered to blocks of data in a blockchain. The blockchain may then be dispersed to subscribers for inspection. Any subscriber may inspect the blockchain, evaluate the debt instruments registered to the blockchain, and conduct automated, electronic purchases of any debt instruments. Smart, digital contracts executed by the blockchain may automate purchase of the debt instruments.

Abstract: Confidential, secret data may be shared via one or more blockchains. Mortgage applications, medical records, financial records, and other electronic documents often contain social security numbers, names, addresses, account information, and other personal data. A secret sharing algorithm is applied to any secret data to generate shares. The shares may then be integrated or written to one or more blockchains for distribution.

Abstract: Authentication of electronic document is based on multiple digital signatures incorporated into a blockchain. Structured data, metadata, and instructions may be hashed to generate the multiple digital signatures for distribution via the blockchain. Any peer receiving the blockchain may then verify an authenticity of an electronic document based on any one or more of the multiple digital signatures incorporated into the blockchain.

Abstract: Authentication of electronic document is based on multiple digital signatures incorporated into a blockchain. Structured data, metadata, and instructions may be hashed to generate the multiple digital signatures for distribution via the blockchain. Any peer receiving the blockchain may then verify an authenticity of an electronic document based on any one or more of the multiple digital signatures incorporated into the blockchain.

Abstract: Confidential, secret data may be shared via one or more blockchains. Mortgage applications, medical records, financial records, and other electronic documents often contain social security numbers, names, addresses, account information, and other personal data. A secret sharing algorithm is applied to any secret data to generate shares. The shares may then be integrated or written to one or more blockchains for distribution.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.