Abstract: An example operation includes one or more of solving, by a scheduler node, integer programming problem of maximizing a sum of organizations' endorsing peers that run chaincodes from a plurality of chaincodes within a consortium, making, by the scheduler node, endorsement policies (EPs) for the chaincodes from the plurality of the chaincodes to be satisfiable at any time, applying administrator's constraint of available endorsing peers to the maximized sum of organizations' endorsing peers, and adding resulting endorsing peers to a maintenance list.

Abstract: An example operation may include one or more of identifying a new entry, receiving one or more new entry signatures to approve the new entry to, such that the one or more new entry signatures each include a signature of the new entry and hashes of previously signed entries for a common block, verifying the one or more new entry signatures for commit by performing a signature verification of the new entry, determining the common block has more than one entry with a hash signed by a same member, and responsive to the signature check being performed on the new entry and determining the common block has more than one entry with a hash signed by a same member, designating one or more previously received entries as verified for commit without performing a signature verification of the one or more previously received entries.

Abstract: A node in a blockchain network may create a mapping scheme operation header for a subsequent operation, identify a first preceding operation in a first preceding block, the first preceding operation linked to the subsequent operation, and include a location of the first preceding block in the mapping scheme operation header for the operation proposal.

Abstract: An example operation may include one or more of encrypting content via an encryption key to generate encrypted content, storing the encrypted content via a distributed ledger, splitting the encrypted encryption key into a set of key shares via a threshold secret sharing scheme, and distributing the set of key shares among a plurality of nodes of a distributed vault, where each key share is distributed with an expiry value that identifies when the respective key share is to be deleted by a node.

Abstract: A processor may assign an indicator to each participant in a blockchain network. The indicator may respectively indicate a role of each participant. The processor may transform the indicator of each participant into respective scores. The respective scores may be directly associated with the role of each participant. The processor may aggregate the respective scores into an endorsement score. The processor may determine if the endorsement score exceeds an endorsement threshold.

Abstract: Systems, methods, and computer programming products leveraging the use of machine learning, cryptographic keys and blockchain technology for validating blockchain transactions. The disclosed systems, methods and products improve detection of malicious cyberattacks and fraud, while reducing occurrences of falsely invalidated transactions and improving overall blockchain security in both permissioned and permissionless blockchain networks. Classifiers are trained using machine learning and other classification techniques by building a transaction history to learn how to identify suspicious transactions on the blockchain. In permissionless and order-execute models of permissioned blockchains, cryptographic keys are publicly registered to guardians residing out of band, who may co-sign requests and override or resubmit transactions marked as suspicious by the classifiers.

Abstract: A processor may store a write set of a transaction in a cache. The cache may include state information, and the cache may be uncommitted to a blockchain. The processor may identify a subsequent transaction. The subsequent transaction may read from the cache when a requested key is present in the cache. The processor may order the transaction and the subsequent transaction based on what is read from the cache. Ordering the transaction and the subsequent transaction may avoid an abort of the subsequent transaction.

Abstract: In an approach for computing trade-offs between privacy and accuracy of data analysis on building a learning model, a processor receives a dataset for training a model. The dataset includes one or more pre-identified sensitive data fields. The processor determines a weight of each sensitive data field for the model. The processor evaluates resource cost of applying a privacy preservation technique to the one or more pre-identified sensitive data fields. The processor identifies correlation among the sensitive data fields. The processor presents a comparison of options for training the model, in terms of tradeoffs of accuracy for training the model and the resource cost of the privacy preservation technique.

Abstract: An example operation may include one or more of receiving a digital token that is created by software application of a blockchain network and which comprises a predefined data structure, determining, via a network node, an assessed value of the digital token based on an assessment of a plurality of attributes of the digital token, embedding, via the network node, the assessed value and an identifier of the blockchain network of the network node within a data container that is included in the predefined data structure of the digital token, and storing the digital token with the embedded assessed value at a blockchain memory address.

Abstract: Aspects of the present invention disclose a method for calculating a risk resulting from a network of networks that includes unknown relationships in a privacy preserving manner. The method includes one or more processors determining a set of conditions corresponding to a user of a network. The method further includes transmitting a compliance request corresponding to the set of conditions to one or more members of the network utilizing a privacy preserving algorithm. The method further includes determining a respective risk factor of one or more members of the network, wherein the respective risk factor corresponds to a response of each of the one or more members to the compliance request. The method further includes determining an overall risk of the network based at least in part on the risk factors of the one or more members.

Abstract: An example operation may include one or more of retrieving a block hash of a data block stored on a blockchain, randomly determining a subset of peer organizations from a blockchain network of the blockchain to be endorsers based on a value of the block hash, transmitting a blockchain storage request from a client to the randomly determined subset of endorser peer organizations, and collecting simulated responses from the randomly determined subset of endorser peer organizations into a storage request proposal.

Abstract: An example operation may include one or more of storing a list of unavailable blockchain peers that cannot be a lead peer of a blockchain consensus protocol, generating a pre-prepare message comprising a new block of a blockchain, appending commit messages received during a commit stage of a previous block to the blockchain, to the pre-prepare message, where the commit messages identify an unavailable blockchain peer from the list that is now available, and broadcasting the pre-prepare message with the new block and the appended commit messages to a plurality of blockchain peers.

Abstract: An example operation includes one or more of receiving view change messages which request a view change from a previous primary peer of a blockchain to the primary peer, identifying that a change to a state of the blockchain is in process with the previous primary peer based on metadata of the received view change messages, verifying that the change to the state of the blockchain corresponds to a latest change to the blockchain based on a received view data message, and transmitting a new view message to following peers which includes the in-process change to the state of the blockchain.

Abstract: A processor may endorse the group of transactions received from one or more clients. The processor may provide a read-set and a write-set for each transaction of the group of transactions. The processor may order the group of transactions at an ordering service. The ordering service may have a group of orderers and may order the group of transactions. The order of the transactions may be based on the read-set and the write-set of each transaction. The processor may commit the group of transactions to the blockchain.

Abstract: In some embodiments, a processor may store a value and a hash of the value in a ledger associated with a blockchain network. The processor may construct a block hash from the hash of the value. The processor may validate a transaction by identifying that the hash of the value matches the hash of the value from a previous transaction. The processor may maintain integrity of the block hash. In some embodiments, a processor may provide a redact transaction for a blockchain network. The processor may initialize the redact transaction. The redact transaction may be associated with a validated transaction. The processor may identify a hash value indicated within the redact transaction and associated with the validated transaction. The processor may redact a value associated with the hash value.

Abstract: An example operation includes one or more of receiving view change messages which request a view change from a previous primary peer of a blockchain to the primary peer, identifying that a change to a state of the blockchain is in process with the previous primary peer based on metadata of the received view change messages, verifying that the change to the state of the blockchain corresponds to a latest change to the blockchain based on a received view data message, and transmitting a new view message to following peers which includes the in-process change to the state of the blockchain.

Abstract: A computer-implemented system and method process a private transaction for dividing a blockchain transaction into a plurality of requests. Each of the requests have transaction data and a same transaction identifier. Each request is sent to a respective participating shard of a set of participating shards in the blockchain transaction of the blockchain. A response to the request is received from each participating shard, and each of the responses is sent to at least the participating shards except the shard the response was received from. In an atomic commit operation, a prepare transaction is created comprising information from the responses and the transaction identifier. The prepare transaction is submitted to the blockchain, and validity answers are received from the shards.

Abstract: A pseudo-relevance feedback (PRF) system is disclosed that determines an optimized relevance model for a search query by utilizing a posterior relevance model to estimate the likelihood that an initial set of top-K retrieved documents would be retrieved given the posterior relevance model, re-ranking the top-K documents based on their respective estimates of likelihood of retrieval, determining a rank similarity between the initial ranking of the top-K documents and the re-ranking of the top-K documents, updating one or more model parameters of the posterior relevance model based on the rank similarity, and iteratively performing the above process until the rank similarity is maximized, at which point, the optimized relevance model is obtained.

Abstract: An example operation includes one or more of solving, by a scheduler node, integer programming problem of maximizing a sum of organizations' endorsing peers that run chaincodes from a plurality of chaincodes within a consortium, making, by the scheduler node, endorsement policies (EPs) for the chaincodes from the plurality of the chaincodes to be satisfiable at any time, applying administrator's constraint of available endorsing peers to the maximized sum of organizations' endorsing peers, and adding resulting endorsing peers to a maintenance list.

Abstract: An example operation includes one or more of receiving view change messages which request a view change from a previous primary peer of a blockchain to the primary peer, identifying that a change to a state of the blockchain is in process with the previous primary peer based on metadata of the received view change messages, verifying that the change to the state of the blockchain corresponds to a latest change to the blockchain based on a received view data message, and transmitting a new view message to following peers which includes the in-process change to the state of the blockchain.