Abstract: An example operation may include one or more of determining, by a file redaction device, redacted segments of a source file, receiving, by a signature update device, the redacted source file segments, a stored trapdoor key, and stored auxiliary data segments, determining modified auxiliary data from the redacted source file segments, the trapdoor key and the auxiliary data segments, executing chaincode to obtain a modified auxiliary data signature and identifiers of the redacted source file segments, and storing the modified auxiliary data signature and identifiers of the redacted source file segments to a shared ledger of a blockchain network. Each stored auxiliary data segment including a random string of data corresponding to a segment of the source file.

Abstract: A framework is provided in which a querying agency can request (via a query entity) encrypted data through a service provider from a data owning agency that stores encrypted data. The framework uses homomorphic encryption. The data may be gallery entities, and each of the elements in the framework operate on doubly-encrypted information. The service provider compares a representation of an encrypted query entity from the querying agency and representations of encrypted gallery entities from the data owning agency, resulting in doubly-encrypted values of a metric between corresponding compared representations. The querying agency gets result(s), based on the metric, which indicate whether it is probable the service provider has data similar to or the same as query data in the query entity. The elements have to perform communication in order for the querying agency or the data owning agency to get cleartext information corresponding to the query entity.

Abstract: A computer system receives a set of data encrypted by a homomorphic encryption transformation. The computer system performs machine learning operations using the encrypted set of data. The machine learning operations build, using homomorphic operations, a trained model of the data having a mapping between the encrypted data and output of the trained model. The model is stored for use for performing inferencing of other encrypted data to determine a corresponding output of the trained model. The computer system may perform inferencing of the other encrypted data at least by accessing the stored trained model and predicting by using the trained model a label in an encrypted format that corresponds to the other encrypted data. The computer system may send the label toward the client for the client to decrypt the label.

Abstract: An example operation may include one or more of creating a source file, segmenting the source file into source file segments, creating a number of auxiliary data segments corresponding to source file segments, performing a chameleon hash of the source file segments and the auxiliary data segments, obtaining a source file signature from the chameleon hash, performing a cryptographic hash of the auxiliary data segments, obtaining an auxiliary data signature from the cryptographic hash, and storing the source file and cryptographic signatures to a shared ledger of a blockchain network. Each auxiliary data segment includes a random string of data that is generated based on a corresponding source file segment.

Abstract: A computer system accesses and processes regulatory requirements for data item(s) in a private manner. Both the data item(s) and the regulatory requirements are accessed and processed privately. The computer system creates an orchestration strategy satisfying the regulatory requirements. The orchestration strategy includes recommendation(s) associating the data item(s) with process(es). The computer system outputs indications of the orchestration strategy to be used to implement regulatory compliance for processing of the data item(s) by associated ones of the process(es). The computer system may be implemented as a portion of a cloud environment, and compliance may be offered as a service for cases where data usage by an application (implementing the process(es)) does not address compliance with the regulatory requirements, but following the orchestration strategy ensures use of the application on the data item(s) will comply with the regulatory requirements.

Abstract: A data intersection is assessed of data to be used between at least two parties. The data is to be used in an artificial intelligence (AI) application. Evaluation is performed of set of instructions required for the AI application, where the evaluation creates a modified set of instructions where operands are symbolically associated with corresponding privacy levels. Using the assessed data intersection and the modified set of instructions, a mapping is created from the data to operands with associated privacy metrics. The mapping treats overlapping data from the assessed data intersection differently from data that is not overlapping to improve privacy relative to without the mapping. The AI application is executed using the data to produce at least one parameter of the AI application. The at least one parameter is output for use for a trained version of the AI application. Apparatus, methods, and computer program products are described.

Abstract: ML model(s) are created and trained using training data from user(s) to create corresponding trained ML model(s). The training data is in FHE domains, each FHE domain corresponding to an individual one of the user(s). The trained machine learning model(s) are run to perform inferencing using other data from at least one of the user(s). The running of the ML model(s) determines results. The other data is in a corresponding FHE domain of the at least one user. Using at least the results, it is determined which of the following issues is true: the results comprise objectionable material, or at least one of the trained ML model(s) performs prohibited release of information. One or more actions are taken to take to address the issue determined to be true. Methods, apparatus, and computer program product are disclosed.

Abstract: An example operation may include one or more of joining, by a host device, a blockchain managed by one or more devices on a decentralized network, the blockchain is configured to use one or more smart contracts that specify transactions among a plurality of end-users, creating on the blockchain the smart contract defining authentication parameters for an authentication of an end-user from the plurality of the end-users, executing the smart contract to perform the authentication of the end-user associated with a transaction based on the authentication parameters by generating an authentication challenge for the transaction, and recording an authentication log produced by the authentication challenge into a metadata of a transaction payload for analytics.

Abstract: An example operation may include one or more of receiving gridlock resolution messages from participants of a gridlocked payment network, each gridlock resolution message comprising a subset of local payments to be performed and a zero-knowledge proof that indicates that the subset of local payments creates a positive post-balance for the respective participant without revealing the positive-post balance, determining whether zero knowledge proofs included in the gridlock resolution messages are valid, aggregating the subsets of local payments to be performed from the gridlock resolution messages to create a global nettable set of payments, and publishing the global nettable set via a data block among a hash-linked chain of data blocks of a distributed ledger accessible to the participants.

Abstract: An example operation may include one or more of detecting a suspected biometric authentication incident, submitting a first blockchain transaction including a first report to a blockchain network, submitting a second blockchain transaction including a second report to the blockchain network, and taking an action, by one or more blockchain nodes, in response to determining one or more of the first and second reports are relevant to the one or more blockchain nodes. The first report includes public and private data corresponding to the suspected biometric authentication incident, and the second report includes one or more of a root cause and mitigation steps for the incident.

Abstract: An example operation may include one or more of receiving, at an endorser node, a request message from a client system which comprises data to be stored on a blockchain, determining whether to endorse the data via invocation of chaincode which receives the data as input and executes the data against a current state of the blockchain, in response to a determination to endorse the data, generating a response message including a result of the execution and signing the response message based on a traceable blinded ring signature associated with the endorser node, and transmitting the generated response message that has been signed with the traceable blinded key ring to the client system.

Abstract: Respective sets of homomorphically encrypted training data are received from multiple users, each encrypted by a key of a respective user. The respective sets are provided to a combined machine learning model to determine corresponding locally learned outputs, each in an FHE domain of one of the users. Conversion is coordinated of the locally learned outputs in the FHE domains into an MFHE domain, where each converted locally learned output is encrypted by all of the users. The converted locally learned outputs are aggregated into a converted composite output in the MFHE domain. A conversion is coordinated of the converted composite output in the MFHE domain into the FHE domains of the corresponding users, where each converted decrypted composite output is encrypted by only a respective one of the users. The combined machine learning model is updated based on the converted composite outputs. The model may be used for inferencing.

Abstract: An example operation may include one or more of creating a source file, segmenting the source file into source file segments, creating a number of auxiliary data segments corresponding to source file segments, performing a chameleon hash of the source file segments and the auxiliary data segments, obtaining a source file signature from the chameleon hash, performing a cryptographic hash of the auxiliary data segments, obtaining an auxiliary data signature from the cryptographic hash, and storing the source file and cryptographic signatures to a shared ledger of a blockchain network. Each auxiliary data segment includes a random string of data that is generated based on a corresponding source file segment.

Abstract: An example operation may include one or more of determining, by a file redaction device, redacted segments of a source file, receiving, by a signature update device, the redacted source file segments, a stored trapdoor key, and stored auxiliary data segments, determining modified auxiliary data from the redacted source file segments, the trapdoor key and the auxiliary data segments, executing chaincode to obtain a modified auxiliary data signature and identifiers of the redacted source file segments, and storing the modified auxiliary data signature and identifiers of the redacted source file segments to a shared ledger of a blockchain network. Each stored auxiliary data segment including a random string of data corresponding to a segment of the source file.

Abstract: An example operation may include one or more of initiating, by a file verification device, verification of a source file or a redacted source file, executing one of a smart contract or chaincode to verify the chameleon hash signature and the auxiliary data hash signature, and providing a notification whether the verification was successful or unsuccessful. In response to initiating verification of the source file, the method further includes the file verification device receiving stored source file segments and stored auxiliary data segments, generating a chameleon hash signature, and generating an auxiliary data hash signature. In response to initiating verification of the redacted source file, the method further includes receiving stored redacted file segments, stored auxiliary data segments, and stored modified auxiliary data, generating a chameleon hash signature, and generating an auxiliary data hash signature.

Abstract: An AI model is trained by determining insights for a sequence of computations used in the AI model. The sequence is applied to encrypted data and label pair(s), wherein computational details of each of the computations are defined. Information may also be committed for selected ones of the sequence of computations into a distributed database. The committed information may include computational details used in processing performed for the selected computations, and the distributed database may have a property that the committed information for each selected computation is linked with a verifiable signature of integrity with a previously committed computation in the sequence. Indication is received from an end-user computer system of selected computation(s). Computational details of the indicated selected computation(s) are sent toward the end-user computer system for use by the end-user computer system for verifying the indicated selected computation(s).

Abstract: A computer system receives a set of data encrypted by a homomorphic encryption transformation. The computer system performs machine learning operations using the encrypted set of data. The machine learning operations build, using homomorphic operations, a trained model of the data having a mapping between the encrypted data and output of the trained model. The model is stored for use for performing inferencing of other encrypted data to determine a corresponding output of the trained model. The computer system may perform inferencing of the other encrypted data at least by accessing the stored trained model and predicting by using the trained model a label in an encrypted format that corresponds to the other encrypted data. The computer system may send the label toward the client for the client to decrypt the label.

Abstract: An example operation may include one or more of joining, by a host device, a blockchain managed by one or more devices on a decentralized network, the blockchain is configured to use one or more smart contracts that specify transactions among a plurality of end-users, creating on the blockchain the smart contract defining authentication parameters for an authentication of an end-user from the plurality of the end-users, executing the smart contract to perform the authentication of the end-user associated with a transaction based on the authentication parameters by generating an authentication challenge for the transaction, and recording an authentication log produced by the authentication challenge into a metadata of a transaction payload for analytics.

Abstract: An example operation may include one or more of obtaining a first biometric sample of a user from a user device. extracting, by an issuing node of a permissioned blockchain network, a biometric template from the first biometric sample, encrypting the biometric template, distributing an issuetoken proposal comprising the encrypted biometric template to the blockchain network, and generating and distributing a biometric token to the user device. In response to the user indicating to the user device to redeem the biometric token, the method includes one or more of presenting, by the user device, the biometric token to a verifying node of the blockchain network, validating, by the verifying node, the biometric token, receiving, by the verifying node, a second biometric sample from the user device, distributing a redeemtoken proposal to the blockchain network, committing a transaction corresponding to the biometric token, to the blockchain network, and invalidating the biometric token.

Abstract: An example operation may include one or more of detecting a suspected biometric authentication incident, submitting a first blockchain transaction including a first report to a blockchain network, submitting a second blockchain transaction including a second report to the blockchain network, and taking an action, by one or more blockchain nodes, in response to determining one or more of the first and second reports are relevant to the one or more blockchain nodes. The first report includes public and private data corresponding to the suspected biometric authentication incident, and the second report includes one or more of a root cause and mitigation steps for the incident.