Abstract: A group of transactions to be rolled back in a blockchain ledger can be identified. Whether the transactions in the group are eligible to be rolled back together can be determined. Other transactions can be recommended, and the other transactions may be added to the group to be rolled back together. A rollback transaction can be added to the blockchain ledger that rolls back effect of the transactions, where the rollback transaction has zero or more inputs and zero or more output, the rollback transaction having at least one selected from at least one input and at least one output. An unspent output of a transaction in the group is provided as an input of the rollback transaction. Input of a transaction in the group, that is not an output of another transaction in the group, is provided as an output of the rollback transaction.

Abstract: A group of transactions to be rolled back in a blockchain ledger can be identified. Whether the transactions in the group are eligible to be rolled back together can be determined. Other transactions can be recommended, and the other transactions may be added to the group to be rolled back together. A rollback transaction can be added to the blockchain ledger that rolls back effect of the transactions, where the rollback transaction has zero or more inputs and zero or more output, the rollback transaction having at least one selected from at least one input and at least one output. An unspent output of a transaction in the group is provided as an input of the rollback transaction. Input of a transaction in the group, that is not an output of another transaction in the group, is provided as an output of the rollback transaction.

Abstract: Execution of one or more actions executing on at least one device is monitored to protect access to a selected digital asset. The monitoring includes determining whether a chosen action to be initiated by one entity to claim a chosen digital asset of another entity has completed. Based on determining that the chosen action has not completed, execution of a selected action, initiated by the other entity, to claim a selected digital asset of the one entity is prevented. Based on preventing execution of the selected action, a determination is made as to whether the chosen action has completed the claim of the chosen digital asset of the other entity. Based on determining that the chosen action has completed, completion of execution of the selected action to claim the selected digital asset of the one entity is facilitated.

Abstract: A computer-implemented method for automatically and digitally securing an asset is disclosed. The computer-implemented method includes hashing a secret preimage to produce a secret hash. The computer-implemented method further includes locking a digital collateral asset with the secret hash for a first predetermined period of time. The computer-implemented method further includes locking a digital loan asset with the secret hash for a second predetermined period of time, wherein the second predetermined period of time is less than the first predetermined period of time. The computer-implemented method further pledging a digital loan repayment up to the amount of time of the first predetermined period of time plus the second predetermined period of time. The computer-implemented method further includes responsive to receiving a pledge for the digital loan repayment, automatically unlocking the digital collateral asset for a borrower of the digital loan asset.

Abstract: One embodiment provides a method, including: receiving a query from a user; providing the query to data owners, wherein each of the data owners has a local machine learning model and wherein the plurality of data owners train a meta-model; secret sharing model output from the data owners between the other data owners, wherein the model output comprises an output responsive to the query computed using the local machine learning model; receiving, from each of the plurality of data owners, a set of meta-features corresponding to the query; and generating a response to the query, wherein the generating comprises determining, by evaluating the meta-model using the set of meta-features received from each of the plurality of data owners, weights for outputs from the local machine learning models and aggregating the outputs in view of the weights.

Abstract: One embodiment provides a computer implemented method, including: receiving, at a service provider from each of a plurality of data owners each having data accessible to the service provider, a privacy budget, wherein the privacy budget identifies a set of privacy requirements to be employed by the service provider on data of the data owner; receiving, at the service provider, a query from a client; receiving, at the service provider from each of at least a subset of the data owners, a response to the query; adding, by the service provider, noise to each of the responses, wherein an amount of noise added to each response is based upon the privacy budget of the data owner corresponding to a given response; and returning, by the service provider to the client, an aggregated response including the responses having added noise.

Abstract: One embodiment provides a method, including: receiving a query from a user; providing the query to data owners, wherein each of the data owners has a local machine learning model and wherein the plurality of data owners train a meta-model; secret sharing model output from the data owners between the other data owners, wherein the model output comprises an output responsive to the query computed using the local machine learning model; receiving, from each of the plurality of data owners, a set of meta-features corresponding to the query; and generating a response to the query, wherein the generating comprises determining, by evaluating the meta-model using the set of meta-features received from each of the plurality of data owners, weights for outputs from the local machine learning models and aggregating the outputs in view of the weights.

Abstract: One embodiment provides a computer implemented method, including: receiving, at a service provider from each of a plurality of data owners each having data accessible to the service provider, a privacy budget, wherein the privacy budget identifies a set of privacy requirements to be employed by the service provider on data of the data owner; receiving, at the service provider, a query from a client; receiving, at the service provider from each of at least a subset of the data owners, a response to the query; adding, by the service provider, noise to each of the responses, wherein an amount of noise added to each response is based upon the privacy budget of the data owner corresponding to a given response; and returning, by the service provider to the client, an aggregated response including the responses having added noise.