Abstract: Systems and methods for blockchain ledger growth management using separation of a blockchain ledger into multiple blockchain ledgers (each ledger having a state that can be tracked and used). The systems and methods also include linking the separated ledgers by utilizing a linking application and smart contracts added to the separated ledgers.

Abstract: The present teaching relates to method, system, medium, and implementations for authenticating a user. A first request is received to set up authentication information with respect to a user, wherein the first request specifies a type of information to be used for future authentication of the user. It is determined whether the type of information related to the user poses risks based on a reverse information search result. The type of information for being used for future authentication of the user is rejected when the type of information is determined to pose risks.

Abstract: Systems and methods are disclosed for cryptographic signing of content requests. One method includes receiving, at a content network, a content request from a publisher website, the publisher website purporting to be associated with a publisher domain. At the content network, a public key may be received associated with the publisher domain. At the content network, at least one policy may be received associated with the publisher domain. It may be determined whether the content request comprises a cryptographic signature. If it is determined that the content request does not comprise a cryptographic signature, content may or may not be provided to the publisher website according to the policy from the publisher domain. If it is determined that the content request comprises a cryptographic signature, the cryptographic signature of the request may be validated using the public key.

Abstract: Systems and methods are disclosed for identifying malicious traffic associated with a website.

Abstract: Systems and methods are disclosed for utilizing sender-recipient pair data to establish sender-level trust in future communication. One method comprises receiving raw communication data over a network and testing the received raw communication data against trained machine learning data to predict whether the raw communication data is associated with expected communication data. The raw communication data is sorted for expected communication data, which is further analyzed for sender-recipient pair data and assigned an expected communication pair data score. Senders associated with an expected communication pair data score that meets or exceeds a threshold are labeled and stored in a database as trusted. As a result of the sender-recipient pair analysis, recipients at-risk for being scammed can be identified, senders misidentified as spammers can be properly classified, and machine learning techniques utilized for analyzing raw communication data can be fine-tuned.

Abstract: Systems and methods are disclosed for determining if an account identifier is computer-generated. One method includes receiving the account identifier, dividing the account identifier into a plurality of fragments, and determining one or more features of at least one of the fragments. The method further includes determining the commonness of at least one of the fragments, and determining if the account identifier is computer-generated based on the features of at least one of the fragments, and the commonness of at least one of the fragments.

Abstract: Systems and methods are disclosed for determining if an account identifier is computer-generated. One method includes receiving the account identifier, dividing the account identifier into a plurality of fragments, and determining one or more features of at least one of the fragments. The method further includes determining the commonness of at least one of the fragments, and determining if the account identifier is computer-generated based on the features of at least one of the fragments, and the commonness of at least one of the fragments.

Abstract: Systems and methods for blockchain ledger growth management using separation of a blockchain ledger into multiple blockchain ledgers (each ledger having a state that can be tracked and used). The systems and methods also include linking the separated ledgers by utilizing a linking application and smart contracts added to the separated ledgers.

Abstract: A method for compliance with Know Your Customer (KYC) and other regulations includes a pseudonymous globally unique identifier stored on a blockchain that associates a pseudonymous first party address with a globally unique identifier representing the vetted identity of the owner of the address. The method also includes a trusted third party issuing a verifiable credential for a first pseudonymous party to a proposed transaction to a second pseudonymous party to the transaction.

Abstract: Systems and methods for blockchain ledger growth management using separation of a blockchain ledger into multiple blockchain ledgers (each ledger having a state that can be tracked and used). The systems and methods also include linking the separated ledgers by utilizing a linking application and smart contracts added to the separated ledgers.

Abstract: Technologies for compressing a blockchain. In some examples, the technologies include removing selected blocks within a blockchain, and replacing the selected blocks with a summary block and a padding block. Each block of the selected blocks includes data in a certain state (such as data in an obsolete state). The technologies can include generating the summary block and padding blocks according to the data in the selected blocks and an original root hash included in the selected blocks and other blocks of the blockchain. The generating of the summary and padding blocks can include generating a new root hash in the summary and padding blocks that only replaces the original root hash in the summary and padding blocks. The generation of the new root hash can be based on a part of a header of a non-selected block of the blockchain directly linked to an end block of selected blocks.

Abstract: Systems and methods are disclosed for utilizing sender-recipient pair data to establish sender-level trust in future communication. One method comprises receiving raw communication data over a network and testing the received raw communication data against trained machine learning data to predict whether the raw communication data is associated with expected communication data. The raw communication data is sorted for expected communication data, which is further analyzed for sender-recipient pair data and assigned an expected communication pair data score. Senders associated with an expected communication pair data score that meets or exceeds a threshold are labeled and stored in a database as trusted. As a result of the sender-recipient pair analysis, recipients at-risk for being scammed can be identified, senders misidentified as spammers can be properly classified, and machine learning techniques utilized for analyzing raw communication data can be fine-tuned.

Abstract: The present teaching relates to method, system, medium, and implementation for secure data management by a service provider. A request is first received for carrying out a transaction with a user and one or more data items associated with the user are then determined that need to be validated prior to the transaction. A request is then sent to the user seeking to validate the one or more data items. When a cloaked identifier is received from the user with information related to a trusted party, the cloaked identifier is then sent to the trusted party with a request for a validation response. When the validation response is received with an indication that the one or more data items are validated, the transaction with the user is carried out.

Abstract: Systems and methods are disclosed for identifying human users on a network.

Abstract: Systems and methods are disclosed for utilizing sender-recipient pair data to establish sender-level trust in future communication. One method comprises receiving raw communication data over a network and testing the received raw communication data against trained machine learning data to predict whether the raw communication data is associated with expected communication data. The raw communication data is sorted for expected communication data, which is further analyzed for sender-recipient pair data and assigned an expected communication pair data score. Senders associated with an expected communication pair data score that meets or exceeds a threshold are labeled and stored in a database as trusted. As a result of the sender-recipient pair analysis, recipients at-risk for being scammed can be identified, senders misidentified as spammers can be properly classified, and machine learning techniques utilized for analyzing raw communication data can be fine-tuned.

Abstract: Systems, methods, and computer-readable media are provided for pairing users for electronic communications over a network. In accordance with one implementation, a method is provided for pairing users that request a chat encounter or other form of electronic communication. The operations of the method include receiving a request from a first user and calculating a plurality of pairing scores, each pairing score based on a collaborative filtering score. The method further includes selecting a second user based on the plurality of pairing scores and pairing the first user with the second user to enable electronic communications.

Abstract: The disclosed systems and methods provide a novel framework that leverages collected logged-in daily active users (LIDAU) data to drive network traffic to network resources, as well as engage these users to continue or remain engaged with the resources through personalization and customization according to their behaviors and patterns. LIDAU data, which is based on a raw data feed of information from network resources and an identified dataset determined from the raw data feed, is used as a basis to increase LIDAU for a specific user or a set of other users. The determined understanding of LIDAU, and its impact on users as well as the network resources the users are interacting with, enables the framework to personalize the resources that are anticipated as being visited by the users in advance of the users visiting them.

Abstract: Systems and methods are disclosed for identifying human users on a network.

Abstract: Systems and methods are disclosed for identifying malicious traffic associated with a website.

Abstract: Systems and methods are disclosed for analyzing a plurality of failed login records that correspond to failed login attempts detected by a computing system, to identify suspicious patterns of activity that can facilitate the supplementation of password blacklists for improving account security. To accomplish the foregoing, failed login records that include information associated with failed login attempts are obtained for analysis. The failed login records are analyzed to identify a set of failed login records that show initial characteristics of a suspicious pattern of activity. The information included in the set of failed login records are further analyzed to determine whether a suspicious pattern of activity is actually present. When a suspicious pattern of activity is identified in the set of failed login records, the passwords used in the failed login attempts are stored in password blacklists associated with the account identifier(s) with which the passwords were used.