Abstract: Example aspects of the present disclosure relate to an example method. The example method includes obtaining, by a computing system comprising one or more processors, a plurality of data bags. In the example method, each respective data bag of the plurality of data bags comprises a respective plurality of instances and is respectively associated with one or more proportion labels. The example method also includes generating, by the computing system, a plurality of training bags from the plurality of data bags according to a plurality of weights. In the example method, the training bags are generated such that a bag-level predicted proportion label error by a machine-learned prediction model over the plurality of training bags correlates to an instance-level predicted proportion label error by the machine-learned prediction model.

Abstract: One embodiment provides a method, including: receiving, at a central system, a query requesting access to a dataset, wherein the central system communicates with a plurality of data storage locations, each having a governance policy for data stored at the data storage location, wherein different portions of the dataset are stored within different of the plurality of data storage locations; sending a sub-query formulated based upon the query; receiving a governance enforcement actions listing corresponding to the portion of the dataset stored within the corresponding data storage location; generating a meta-policy of enforcement actions for all of the plurality of data storage locations storing portions of the dataset, wherein the meta-policy identifies enforcement actions and an order of the enforcement actions to be applied to the dataset; and providing the meta-policy to each of the plurality of data storage locations.

Abstract: An example operation may include one or more of identifying a data record to be included in a blockchain, creating a group of smart contracts to enable access to the data record to data consumers with access to the blockchain, determining the group of smart contracts have been registered by one or more blockchain peers, and storing the group of smart contracts on the blockchain.

Abstract: An example operation may include one or more of generating, by an executing client, a blockchain transaction comprising an anonymous rating, a proof, a nullifier, and a root node value, receiving, by a smart contract, the blockchain transaction, the anonymous rating related to an authorizing client, verifying the proof with the root node value and the nullifier, verifying that the root node value is a current or a previous merkle tree root node value, adding the anonymous rating to a shared ledger, marking the nullifier as used, and storing the marked nullifier to the shared ledger.

Abstract: A processor may identify one or more transaction verification requests from one or more entities. The processor may convert each of the one or more transaction verification requests into respective hashed transaction verification requests. The processor may send, on one or more private, anonymous channels, the hashed transaction verifications to an orchestrator. The processor my decrypt the hashed transaction verifications with the orchestrator. The processor may determine whether information in each of the one or more transaction verification requests matches.

Abstract: One embodiment provides a method, including: receiving, at a central system, a query requesting access to a dataset, wherein the central system communicates with a plurality of data storage locations, each having a governance policy for data stored at the data storage location, wherein different portions of the dataset are stored within different of the plurality of data storage locations; sending a sub-query formulated based upon the query; receiving a governance enforcement actions listing corresponding to the portion of the dataset stored within the corresponding data storage location; generating a meta-policy of enforcement actions for all of the plurality of data storage locations storing portions of the dataset, wherein the meta-policy identifies enforcement actions and an order of the enforcement actions to be applied to the dataset; and providing the meta-policy to each of the plurality of data storage locations.

Abstract: An example operation may include one or more of receiving, from an executing client, a blockchain transaction comprising an anonymous rating related to an authorizing client, a merkle tree root node value, a proof, and a nullifier, and in response, executing, by a smart contract, a valid historical value assert call on a lookback key storing the merkle tree root node value, verifying, through a valid historical value assert call, that the merkle tree root node value is a current or previous value of the merkle tree root node value, verifying the proof with the merkle tree root node value and the nullifier, adding the anonymous rating to a shared ledger, marking the nullifier as used, and storing the marked nullifier to the shared ledger.

Abstract: An example operation may include one or more of receiving a request to execute a blockchain transaction from a user device that is associated with a user account, determining whether to perform the blockchain transaction based on a dynamic participation policy of the user account, the dynamic participation policy being dynamically determined based on previous blockchain transactions of one or more of the user account and of other user accounts, and, in response to determining to perform the blockchain transaction, executing the blockchain transaction on behalf of the user account via a smart contract and storing information about the executed blockchain transaction.

Abstract: An example operation may include one or more of storing a tree structure via a blockchain storage, the tree structure comprising anonymous behavior data of a plurality of blockchain participants stored in a plurality of nodes in a hierarchical structure, receiving a request to add new anonymous behavior data to the tree structure, the request comprising a zero-knowledge proof generated by a blockchain participant, identifying an active leaf on the tree structure which stores previously recorded anonymous behavior data of the blockchain participant associated with the request based on the zero-knowledge proof, generating a new active leaf for the blockchain participant based on the new anonymous behavior data and the previously recorded anonymous behavior, and storing the new active leaf as a leaf node on the tree structure in the blockchain storage.

Abstract: A processor may identify one or more transaction verification requests from one or more entities. The processor may convert each of the one or more transaction verification requests into respective hashed transaction verification requests. The processor may send, on one or more private, anonymous channels, the hashed transaction verifications to an orchestrator. The processor my decrypt the hashed transaction verifications with the orchestrator. The processor may determine whether information in each of the one or more transaction verification requests matches.

Abstract: An example operation may include one or more of receiving a content request to add a content to a blockchain storage structure implemented on a blockchain database, temporarily storing the content of the content request in a queue implemented via the blockchain database based on when the request is received, receiving a request to flush the queue which is invoked by chaincode, and in response to the flush request, removing the content from the queue and adding the content to the blockchain storage structure.

Abstract: An example operation may include one or more of generating, by an executing client, a blockchain transaction comprising an anonymous rating, a proof, a nullifier, and a root node value, receiving, by a smart contract, the blockchain transaction, the anonymous rating related to an authorizing client, verifying the proof with the root node value and the nullifier, verifying that the root node value is a current or a previous merkle tree root node value, adding the anonymous rating to a shared ledger, marking the nullifier as used, and storing the marked nullifier to the shared ledger.

Abstract: An example operation may include one or more of receiving, from an executing client, a blockchain transaction comprising an anonymous rating related to an authorizing client, a merkle tree root node value, a proof, and a nullifier, and in response, executing, by a smart contract, a valid historical value assert call on a lookback key storing the merkle tree root node value, verifying, through a valid historical value assert call, that the merkle tree root node value is a current or previous value of the merkle tree root node value, verifying the proof with the merkle tree root node value and the nullifier, adding the anonymous rating to a shared ledger, marking the nullifier as used, and storing the marked nullifier to the shared ledger.

Abstract: An example operation may include one or more of receiving a content request to add a content to a blockchain storage structure implemented on a blockchain database, temporarily storing the content of the content request in a queue implemented via the blockchain database based on when the request is received, receiving a request to flush the queue which is invoked by chaincode, and in response to the flush request, removing the content from the queue and adding the content to the blockchain storage structure.

Abstract: An example operation may include one or more of storing a tree structure via a blockchain storage, the tree structure comprising anonymous behavior data of a plurality of blockchain participants stored in a plurality of nodes in a hierarchical structure, receiving a request to add new anonymous behavior data to the tree structure, the request comprising a zero-knowledge proof generated by a blockchain participant, identifying an active leaf on the tree structure which stores previously recorded anonymous behavior data of the blockchain participant associated with the request based on the zero-knowledge proof, generating a new active leaf for the blockchain participant based on the new anonymous behavior data and the previously recorded anonymous behavior, and storing the new active leaf as a leaf node on the tree structure in the blockchain storage.

Abstract: An example operation may include one or more of identifying a data record to be included in a blockchain, creating a group of smart contracts to enable access to the data record to data consumers with access to the blockchain, determining the group of smart contracts have been registered by one or more blockchain peers, and storing the group of smart contracts on the blockchain.

Abstract: An example operation may include one or more of receiving a request to execute a blockchain transaction from a user device that is associated with a user account, determining whether to perform the blockchain transaction based on a dynamic participation policy of the user account, the dynamic participation policy being dynamically determined based on previous blockchain transactions of one or more of the user account and of other user accounts, and, in response to determining to perform the blockchain transaction, executing the blockchain transaction on behalf of the user account via a smart contract and storing information about the executed blockchain transaction.

Abstract: Methods and arrangements for identifying discrepancies associated with estimates for asset groups. A contemplated method includes: utilizing at least one processor to execute computer code that performs the steps of: receiving a plan comprising at least one activity, wherein the plan comprises a time estimate and a resource estimate for the at least one activity; identifying at least one asset associated with the at least one activity; obtaining data relating to the at least one asset; identifying, using the data obtained, an asset group, wherein an asset group comprises at least two assets having at least one similar datum; detecting at least one discrepancy between at least one of: the time estimate and the resource estimate, for the asset group; and notifying a user of the at least one discrepancy. Other variants and embodiments are broadly contemplated herein.