8 results about "Data steward" patented technology

Systems and methods for the selection, verification and recommendation of cohort sample sets is provided. In some embodiments, a dataset selection optimization includes first receiving at data stewards classes of data required by the data consumer. The data stewards process their data (or a subset of their data) into a vector set within a sequestered computing node. These vector sets are transferred to a core management system for minimizing a difference between a target vector and any combination of the data stewards' vector sets. A cost function may also be applied to the vector sets during this optimization. Once the data steward(s) that best match the target vector are identified, they may be placed in contact with the data consumer for access of their information.

Systems and methods related to the generation and application of dynamic policies in a zero-trust computing environment are provided. In some embodiments, the method of dynamic policy application comprises receiving a query. Data and an algorithm are then processed in response to the query on a runtime server within a trusted computing environment to generate a result. A dynamic outbound policy is generated responsive to a data steward. It is used to validate the result. The result may be shared as output when the result meets the criteria of the dynamic outbound policy, otherwise the result may be rejected when the result fails to meet the criteria of the dynamic outbound policy. In addition to the dynamic outbound policy, the query may also be subjected to an inbound policy. This process may all occur in an iterative way within a Jupyter Notebook environment.

Embodiments of this disclosure provide, but are not limited to, solutions for protecting, sharing, verifying, and / or generating data resources such as documents. In exemplary embodiments, a data resource is represented, expressed, or defined as a plurality of hashed constituent segments. Using the segment hashes as leaves, a Merkle tree is generated representing the complete original version of the data resource. An edited version of the data resource can be shared, omitting one or more segments that the data administrator wishes to keep secret or does not wish to share in its original form. To verify the authenticity and / or completeness of the edited data resource, a validator is provided with sufficient data (e.g., the hash of each of the non-shared segments) to verify the presence of non-shared segments in the tree representing the complete original version, and an authorized signature that signed the root. In some embodiments, the data resource is a code library, and the segments are parts of computer code that perform specific subtasks. Shared code segments, when executed on a processor, may be selected to combine to perform larger tasks.

Systems and methods related to the generation of a hypothesis is presented. First a user inputs at least one research paper or study. The at least one research paper or study is mined by an AI algorithm for a set of data requirements. The set of data requirements may be combined with a tailored prompt into a generative AI system to generate a data specification. Next, within a secure enclave, the data specification may be combined with natural language (NL) prompts to interrogate data sets from a plurality of data stewards. A data set from the interrogated data sets that meets the data specification is selected. The data set is then encrypted, used to generate and train an AI model.

Systems and methods related to the generation and application of dynamic policies in a zero-trust computing environment are provided. In some embodiments, the method of dynamic policy application comprises receiving a query. Data and an algorithm are then processed in response to the query on a runtime server within a trusted computing environment to generate a result. A dynamic outbound policy is generated responsive to a data steward. It is used to validate the result. The result may be shared as output when the result meets the criteria of the dynamic outbound policy, otherwise the result may be rejected when the result fails to meet the criteria of the dynamic outbound policy. In addition to the dynamic outbound policy, the query may also be subjected to an inbound policy. This process may all occur in an iterative way within a Jupyter Notebook environment.

An embedded next generation access control system imposes fine-grained access control on data in a resource database and includes a resource database, NGAC database, administrator, and mediator. The resource database includes controlled resource data and a mediator. The NGAC database includes an adjudicator and access control policy data. The administrator communicates access control policy input to the NGAC database upon which access control policy data is made in the NGAC database. The controlled resource data is access controlled by access control policy data via calls of an adjudicator API, which implements Next Generation Access Control standard by NIST, by the resource database to obtain access decisions. The adjudicator determines which controlled resource data are accessed by a user and provides an access control list for each user and user attribute. The adjudicator also provides a prohibited access control list for a user and user attributes in the access control policy data if there are any.

The invention discloses an automatic verification system for bioequivalence test data, and relates to the technical field of data services, the system comprises a data acquisition module, a knowledge graph construction module, a semantic reasoning verification module, an abnormity early warning module and a collaborative updating module; bE test data are respectively collected from the clinical data management unit, the detection instrument and the laboratory information management unit, a standardized data pool is formed through format standardization processing, and standard integration of the data is realized; core entities and corresponding attributes are defined, association relationships and mapping rules among the entities are determined, and by calculating association strength among different entities, reliable entity pairs are screened, incorporated into a knowledge graph and stored, and a traceable semantic association system among data is established; and extracting to-be-verified data, performing pop-up early warning by combining entity association strength, calculating an exception judgment index and generating a verification result early warning report, and meanwhile, pushing an exception early warning mail to a data administrator to ensure that data exception is identified and handled in time.