Enhanced data security and access control using machine learning
AssigneeAlcon Inc.
InventorUma Chandrashekhar
Life of this patent
Estimating the term…
Abstract
Techniques for controlling data access using machine learning are provided. In one aspect, first, second, and third training data sets are generated from a set of historical access records and a set of historical data records, where the access records correspond to requests for data and comprise information identifying whether the request satisfies one or more data access rules, and the data records correspond to data elements and comprise information identifying whether the data element satisfies the one or more data access rules. One or more machine learning models are trained based on the first, second, and third training data sets to generate an output identifying whether requests for data should be granted.
Claims20
A method for using one or more machine learning models to automatically control data access, comprising:
- receiving, from a first user, a first request for data relating to a second user;
- automatically determining whether the first request satisfies one or more data access rules by processing the first request using a first set of one or more trained machine learning models;
- upon determining that the first request satisfies the one or more data access rules, automatically retrieving a first plurality of data elements based on the first request;
- automatically determining whether each of the first plurality of data elements satisfies the one or more data access rules by processing each of the first plurality of data elements individually using a second set of one or more trained machine learning models;
- upon determining that each data element of a first set of data elements, from the first plurality of data elements, individually satisfies the one or more data access rules, determining whether the first set of data elements collectively satisfies the one or more data access rules by processing the first set of data elements using a third set of one or more trained machine learning models;
- and
- upon determining that the first set of data elements satisfy the one or more data access rules, generating a custom report including the first set of data elements.
One or more non-transitory computer-readable media containing instructions which, when executed by one or more processors, cause a system to perform one or more operations, the operations comprising:
- receiving, from a first user, a first request for data relating to a second user;
- automatically determining whether the first request satisfies one or more data access rules by processing the first request using a first set of one or more trained machine learning models;
- upon determining that the first request satisfies the one or more data access rules, automatically retrieving a first plurality of data elements based on the first request;
- automatically determining whether each of the first plurality of data elements satisfies the one or more data access rules by processing each of the first plurality of data elements individually using a second set of one or more trained machine learning models;
- upon determining that each data element of a first set of data elements, from the first plurality of data elements, individually satisfies the one or more data access rules, determining whether the first set of data elements collectively satisfies the one or more data access rules by processing the first set of data elements using a third set of one or more trained machine learning models;
- and
- upon determining that the first set of data elements satisfy the one or more data access rules, generating a custom report including the first set of data elements.
A system, comprising:
- one or more processors;
- and
- one or more non-transitory computer-readable media containing instructions which, when executed by the one or more processors, cause the system to perform one or more operations, the operations comprising:
- receiving, from a first user, a first request for data relating to a second user;
- automatically determining whether the first request satisfies one or more data access rules by processing the first request using a first set of one or more trained machine learning models;
- upon determining that the first request satisfies the one or more data access rules, automatically retrieving a first plurality of data elements based on the first request;
- automatically determining whether each of the first plurality of data elements satisfies the one or more data access rules by processing each of the first plurality of data elements individually using a second set of one or more trained machine learning models;
- upon determining that each data element of a first set of data elements, from the first plurality of data elements, individually satisfies the one or more data access rules, determining whether the first set of data elements collectively satisfies the one or more data access rules by processing the first set of data elements using a third set of one or more trained machine learning models;
- and
- upon determining that the first set of data elements satisfy the one or more data access rules, generating a custom report including the first set of data elements.
Dependencies are inferred from the claim text.