Abstract: Securely sharing proof of knowledge of information without revealing the information, for example to allow an institution to prove it has knowledge regarding an alert. Code or software may be generated (at a first computer system) which takes as input one or more details regarding an entity and returns a value based on a match to one or more actual details regarding the entity. A name may be generated for the code based on information describing the entity, and the code may be published to a blockchain, along with at least one key corresponding to the code. For each name an entry may be creating in an index, converting the name to a blockchain address. A proof based on the code may be generated at a second computer system, which, if verified, results in the first institution and the second institution sharing information regarding the entity.

Abstract: Securely sharing proof of knowledge of information without revealing the information, for example to allow an institution to prove it has knowledge regarding an alert. Code or software may be generated (at a first computer system which takes as input one or more details regarding an entity and returns a value based on a match to one or more actual details regarding the entity. A name may be generated for the code based on information describing the entity, and the code may be published to a blockchain, along with at least one key corresponding to the code. For each name an entry may be creating in an index, converting the name to a blockchain address. A proof based on the code may be generated at a second computer system, which, if verified, results in the first institution and the second institution sharing information regarding the entity.

Abstract: Securely sharing proof of knowledge of information without revealing the information, for example to allow an institution to prove it has knowledge regarding an alert. Code or software may be generated (at a first computer system) which takes as input one or more details regarding an entity and returns a value based on a match to one or more actual details regarding the entity. A name may be generated for the code based on information describing the entity, and the code may be published to a blockchain, along with at least one key corresponding to the code. For each name an entry may be creating in an index, converting the name to a blockchain address. A proof based on the code may be generated at a second computer system, which, if verified, results in the first institution and the second institution sharing information regarding the entity.

Abstract: Securely sharing proof of knowledge of information without revealing the information, for example to allow an institution to prove it has knowledge regarding an alert. Code or software may be generated (at a first computer system) which takes as input one or more details regarding an entity and returns a value based on a match to one or more actual details regarding the entity. A name may be generated for the code based on information describing the entity, and the code may be published to a blockchain, along with at least one key corresponding to the code. For each name an entry may be creating in an index, converting the name to a blockchain address. A proof based on the code may be generated at a second computer system, which, if verified, results in the first institution and the second institution sharing information regarding the entity.

Abstract: A computerized method for deanonymizing cryptocurrency users by analyzing bank transfers to a cryptocurrency exchange may include receiving data from a currency transfer made by a user at a bank to a cryptocurrency exchange. Candidate currency exchange transactions made by the user may be identified from the currency exchange transactions completed by the cryptocurrency exchange within a predefined time interval. A set of anonymous transactions in blockchain cryptocurrency ledger in the predefined time interval may be identified where the cryptocurrency value in transactions of anonymous users in a blockchain cryptocurrency ledger matches a currency transfer amount made by the user in the candidate currency exchange transactions. Anonymous transactions in the set may be identified as being made by the user based on the probability assigned to each anonymous transaction in the set.

Abstract: A computerized method for deanonymizing cryptocurrency users by analyzing bank transfers to a cryptocurrency exchange may include receiving data from a currency transfer made by a user at a bank to a cryptocurrency exchange. Candidate currency exchange transactions made by the user may be identified from the currency exchange transactions completed by the cryptocurrency exchange within a predefined time interval. A set of anonymous transactions in blockchain cryptocurrency ledger in the predefined time interval may be identified where the cryptocurrency value in transactions of anonymous users in a blockchain cryptocurrency ledger matches a currency transfer amount made by the user in the candidate currency exchange transactions. Anonymous transactions in the set may be identified as being made by the user based on the probability assigned to each anonymous transaction in the set.

Abstract: Method and system for configuring a model and deploying it on a target. The method may include using a processing unit, automatically generating a plugin based on a statistical model, the plugin including variables and issue definitions extracted from the model; and deploying the plugin on a target platform.

Abstract: A system or method may include receiving, by a processor, data describing a network, wherein the network includes a plurality of entities and links describing relationships between the plurality of entities. The method may further include identifying a set of seed entities from the plurality of entities based on predefined rules. The method may further include generating a set of sub-networks based on the set of identified seed entities, wherein each of the sub-networks may include one or more other entities of the plurality of entities having at least one link to the at least one seed entity. The method may further include calculating a risk score for each of the generated sub-networks.

Abstract: Method and system for configuring a model and deploying it on a target. The method may include using a processing unit, automatically generating a plugin based on a statistical model, the plugin including variables and issue definitions extracted from the model; and deploying the plugin on a target platform.

Abstract: A modified igNAR peptide sequence derived from a wild-type igNAR peptide sequence is diversified by mutating the amino acid sequence at 50% or more of the amino acids in the CDR3 loop region and optionally at 50% or more of the amino acids in the CDR3 loop region. The modified igNAR peptide may have the sequence of SEQ ID NO: 8, 10 or 50 to 85. The modified igNAR peptides have binding activity against albumin protein sequences, such as human serum albumin. These modified igNAR peptides may have utility in extending the in vivo half-life of biological molecules e.g. therapeutic agents, and so may be used in medicine.