Abstract: A storage architecture and associated usage techniques are described for providing efficient modification and use of access rights for stored data. The access rights may be associated with data stored on blockchain storage, and a separate ledger storage system may be used to provide improvements for modifying access rights for such stored data. For example, groups of data may be created and stored on blockchain storage before access to the stored data groups is made available to end users, and additional information related to those stored data groups (e.g., about their access rights) may be stored in a separate ledger storage system. When a particular user later requests access rights for one of those previously stored data groups, corresponding modifications may be quickly made to the separate ledger storage system to provide the user with substantially immediate access to that stored data group.

Abstract: A computer-implemented method may include (1) providing, on a display screen of a computing device, a display that obscures an item of information in a secure field without obscuring an entirety of the display; (2) detecting, using at least a camera of the computing device, a change in orientation of a user of the computing device relative to the computing device; (3) in response to the change in orientation, causing the display to show the item of information in the secure field; and (4) after causing the display to show the item in the secure field, (i) detecting, using the camera, a subsequent change in the orientation of the user relative to the computing device, and (ii) in response to detecting the subsequent change in the orientation of the user, causing the display to again obscure the item of information in the secure field without obscuring the entire display.

Abstract: A blockchain data recording method includes determining a recording type as one of an on-chain type and an off-chain type according to a target data to be recorded in a blockchain, transmitting a first transaction proposal for recording the target data in a blockchain node configured to store an on-chain data on a blockchain to the blockchain node when the recording type is an on-chain type, and recording the target data in a storage node provided to store an off-chain data, and transmitting a second transaction proposal for storing recording information of the target data in the blockchain to the blockchain node when the recording type is an off-chain type.

Abstract: A system for authorizing secured access using cryptographic hash value validations is provided. In particular, the system may receive requests from users and/or computing systems to obtain secured access a particular resource or to execute a certain process. In response, the system may require that the user and/or computing system complete additional required steps (e.g., a computation) before being granted access to the resources or processes. In this way, the system may prevent unauthorized or unintended access to the system's resources or processes.

Abstract: A system is provided for the storage of data, the system having: an encrypted host platform upon which regulatory controlled data is stored; a controller configured to allow a primary user to set permission settings and identify authorized end users and degrees of access granted to each the authorized end user, the authorized end user being pre-cleared for compliance with regulatory controls pertaining to the regulatory controlled data; the controller configured to permit access to the encrypted host platform only if the hosting platform is in compliance with predefined data security protocols the controller configured to allow the authorized end user access to the regulatory controlled data, and the controller configured to exclude access to both a provider of the system for storage and a system host platform provider; at least one individual computing device accessible by at least one the authorized end user, the individual computing device configured to provide authorized end user identification data to th

Abstract: Techniques are disclosed relating to the management of data. A data provider computer system may store particular data of a user. The data provider computer system may commence sharing of a portion of the particular data with a data consumer computer system. The data provider computer system may continue sharing additional portions of the particular data with the data consumer computer system in response to receiving a report from a verification environment indicating that the particular data is being utilized by the data consumer computer system in accordance with a specified usage policy.

Abstract: Embodiments relate to de-obfuscated data to process at one or more artificial-intelligence (AI) decision nodes throughout a communication decision tree. Through processing directed by the communication decision tree, additional de-obfuscated data is collected and securely transmitted in de-obfuscated or re-obfuscated form to enrich underlying data structures.

Abstract: With the systems and methods described herein, one or more security counter measures can be applied to received security data, e.g., by an initial detector, for identifying signatures or patterns in the received security data and determining whether to promote identifiers (e.g., URLs, IP addresses, domains, etc.) to an attacker learning system. If the identified signatures or patterns and/or the identifiers related thereto are determined to meet a threshold criterion, the identifiers are promoted to the attacker learning system. At the attacker learning system, a machine learning model is applied to promoted identifiers and security data associated therewith for determining whether the identifiers are malicious and should be added or otherwise included in an attacker database. Other aspects also are described.

Abstract: A high-performance distributed ledger and transaction computing network fabric over which large numbers of transactions (involving the transformation, conversion or transfer of information or value) are processed concurrently in a scalable, reliable, secure and efficient manner. In one embodiment, the computing network fabric or “core” is configured to support a distributed blockchain network that organizes data in a manner that allows communication, processing and storage of blocks of the chain to be performed concurrently, with little synchronization, at very high performance and low latency, even when the transactions themselves originate from distant sources. This data organization relies on segmenting a transaction space within autonomous but cooperating computing nodes that are configured as a processing mesh. Each computing node typically is functionally-equivalent to all other nodes in the core.

Abstract: There may be provided a computer-implemented method. It may be implemented using a blockchain network such as, for example, the Bitcoin network.

Abstract: Smart contract code is verified by storing smart contract code, identifying a type system for verifying a determinism of the smart contract code based on a first set of security-level values and a second set of security level values, and verifying the determinism of the smart contract code by taking the first set of security-level values as deterministic values and the second set of security level values as non-deterministic values.

Abstract: Techniques are disclosed relating to generating user-specific time-to-live (TTL) values using machine learning. In various embodiments, a server system maintains a cache data store that is operable to store data for a plurality of users of a web service. In response to a cache miss for a request from a first one of the plurality of users, the server system may generate a user-specific TTL value for the first user. In various embodiments, generating the user-specific TTL value may include using a machine learning model to generate a predicted future access pattern for the first user that indicates a distribution of time periods during which the first user is expected to access the web service and, based on the predicted future access pattern, determining the user-specific TTL value for the first user.

Abstract: The invention relates to a contribution incentive-based blockchain collaboration method and system, which is characterized in, comprising: 1) applying from an ordinary node to a qualification assessment node for registered qualification; 2) dividing a task into a number of subject tasks and creating corresponding subject task ledgers respectively, and creating a number of sub-tasks corresponding to the subject task in each of the subject task ledgers; 3) applying from the ordinary node to the qualification assessment node for receiving the sub-tasks; 4) by the qualification assessment node, auditing all sub-tasks which are applied for reception by the ordinary node; 5) submitting completed sub-tasks from the ordinary node to a task quality audit node; 6) by the task quality audit node, calculating contribution incentives and qualification credits for the sub-tasks completed by the ordinary node; 7) by the task quality audit node, traversing through each subject task ledger respectively, for finding out a bloc

Abstract: A method may include obtaining various votes for a blockchain transaction from various blockchain nodes. The method may further include determining various weighted votes using the votes and respective cybersecurity states of the blockchain nodes. The respective cybersecurity states may correspond to whether a predetermined security vulnerability is associated with the blockchain nodes. The respective cybersecurity states may be dynamic values that are updated based on changes among predetermined security vulnerabilities. The method may further include determining whether to validate the blockchain transaction based on the weighted votes.

Abstract: A device receives, from an application, a request to access an attestation key stored in a secure element of the device. The device obtains an attestation policy, by which to verify an identity of the application. The device examines an application file associated with the application, to determine whether the application file satisfies the attestation policy. The device selectively generates a temporary key based on a result of examining the application file. The temporary key may be used to access the attestation key. The temporary key may be generated based on the application file satisfying the attestation policy, and may not be generated based on the application file not satisfying the attestation policy.

Abstract: A machine learning server is provided for predicting innovations in one or more scenarios. The machine learning server includes a processor and a memory in communication with the processor. The processor is configured to receive a user input, define a scenario profile based on the user input, apply a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable outcome, prompt for a user selection from the target, apply a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the measureable desired outcome, obtain descriptive information related to the at least one candidate innovation, and present the descriptive information related to the at least one candidate innovation.

Abstract: In an example, a data structure comprises a first genesis record having an associated authentication value and a plurality of subsequent records arranged in an order, wherein at least one subsequent record includes the authentication value associated with the first genesis record and an authentication value associated with another record in the data structure. A method may include designating one of the subsequent records as a second genesis record, acquiring an authentication value for the second genesis record, deleting at least one record preceding the second genesis record in the data structure and adding a further record to the data structure wherein the further record includes the authentication value of the second genesis record and an authentication value associated with a directly preceding record in the data structure.

Abstract: A method performed by a system of a host organization for providing access controls for data a blockchain, the system providing a blockchain interface to a blockchain on behalf of a plurality of tenants of the host organization each serving as nodes in a blockchain network. The method includes receiving a request including an identifier of a requestor, the request to access transaction data having defined privileges, requesting access to the transaction data from nodes in the blockchain network, a request for access including the identifier of the requestor, receiving at least one shared secret from an owner node in the blockchain network indicating consensus to access the transaction data by the requestor, and denying access to the transaction data in response to receiving insufficient shared secrets from the nodes indicating the requestor does not meet requirements of the defined privileges of the transaction data.

Abstract: Aspects of the disclosure relate to real-time management of access controls. A computing platform may detect, via the communication interface, a trigger related to an activity of an enterprise user over a network. Then, the computing platform may retrieve, via the communication interface and from the network device, data associated with the activity. The computing platform may then compare, via the computing device, the retrieved data with a central tendency for activities with the network device. Subsequently, the computing platform may identify, based on the comparing, a deviation from the central tendency. Then, the computing platform may adjust, dynamically in real-time and in response to identifying the deviation, an access control or multiple access controls for the enterprise user to the network device.

Abstract: A detection modeling system performs a distribution analysis to alert to model degradation. The detection modeling system may have a distribution analysis module configured to perform an alerting process in conjunction with a processing device. The distribution analysis module may receive a risk tolerance rating for alerting to degradation of an analytical model, and determine a threshold value for a model metric based on the risk tolerance rating. The model metric may be a measure of a parameter associated with the analytical model. The distribution analysis module may also monitor the analytical model for degradation using the threshold value for the model metric and model metric values and alert to model degradation of the analytical model based on the monitoring of the analytical model.