Systems, methods, and computer-readable media for facilitating an authentication processing service are provided.

A method comprises obtaining a video stream of a first party who is engaging with a second party in a telemedicine session; determining statuses of one or more privacy aspects of a first party environment, wherein the privacy aspects of the first party environment are aspects of the first party environment that have a potential to compromise privacy of sensitive information provided by the second party to the first party during the telemedicine session; and causing a second party computing device to present a user interface of a telemedicine facilitation application, wherein the user interface of the telemedicine facilitation application includes the video stream of the first party and also includes a set of one or more notifications, wherein each of the one or more notifications indicates the status of a different one of the privacy aspects of the first party environment.

The instant invention relates to a system that in one form is a referral system for use in various healthcare sectors, which utilizes patient profiles, created using patient trackers, to monitor patient follow-up care. The instant invention allows physicians and providers to communicate about patient’s care in its entirety, beginning with the originating provider creating a patient tracker, sharing that patient tracker with referred physicians, and discharging the patient once the patient tracker’s plans have concluded. This uninterrupted flow of communication amongst all providers and physicians allows for a secured method of tracking each patient’s healthcare plan, provides for efficient healthcare referral follow-ups, and updates the originating HCPs records to ensure there are no gaps in terms of patient treatment.

Group-level privacy preservation is implemented within federated machine learning. An aggregation server may distribute a machine learning model to multiple users each including respective private datasets. The private datasets may individually include multiple items associated with a single group. Individual users may train the model using their local, private dataset to generate one or more parameter updates and to determine a count of the largest number of items associated with any single group of a number of groups in the dataset. Parameter updates generated by the individual users may be modified by applying respective noise values to individual ones of the parameter updates according to the respective counts to ensure differential privacy for the groups of the dataset. The aggregation server may aggregate the updates into a single set of parameter updates to update the machine learning model.

User-level privacy preservation is implemented within federated machine learning. An aggregation server may distribute a machine learning model to multiple users each including respective private datasets. Individual users may train the model using the local, private dataset to generate one or more parameter updates. Prior to sending the generated parameter updates to the aggregation server for incorporation into the machine learning model, a user may modify the parameter updates by applying respective noise values to individual ones of the parameter updates to ensure differential privacy for the dataset private to the user. The aggregation server may then receive the respective modified parameter updates from the multiple users and aggregate the updates into a single set of parameter updates to update the machine learning model. The federated machine learning may further include iteratively performing said sending, training, modifying, receiving, aggregating and updating steps.

A system, method, and computer program product are provided for centralized consent management. In operation, the consent management system receives user selections from a user indicating which user data is capable of being utilized for analysis by a company. The consent management system stores the user selections of which user data is capable of being utilized for analysis by the company in a consent database. The consent management system generates a consent vector corresponding to the user selections of which user data is capable of being utilized for analysis by the company. Additionally, the consent management system associates the consent vector with a consent vector identification. Further, the consent management system tags incoming data with the consent vector identification to associate a user consent with the incoming data. The consent management system stores and encodes the incoming data. Moreover, the consent management system enforces consent restrictions by conditionally allowing access to the stored data by the company based on corresponding consent vector identifications.

A technological approach can be employed to protect data. Datasets from distinct computing environments of an organization can be scanned to identify data elements subject to protection, such as sensitive data. The identified elements can be automatically protected such as by masking, encryption, or tokenization. Data lineage including relationships amongst data and linkages between computing environments can be determined along with data access patterns to facilitate understanding of data. Further, personas and exceptions can be determined and employed as bases for access recommendations.

Embodiments of various systems and methods described herein provide an identity security database analytics system which is configured to provide security alerts to a user. The security alerts can include for personalized metrics related to potential identity theft incidents. The personalized metrics can include user specific information on security breaches of the user's personal information as well as depersonalized statistics generated based on information of other users having one or more similar characteristics of the user.

A system, apparatus and product comprising: a multi-tenant layer that comprises shared resources, wherein the shared resources are accessible to multiple tenants of the storage system, wherein the shared resources comprise shared logic resources and shared data resources; and multiple single-tenant layers, wherein each single-tenant layer is associated with a respective tenant of the multiple tenants, wherein each single-tenant layer comprises a database and business logic of the respective tenant, wherein a multi-tenant encryption scheme is configured to enable secure communications with the multiple tenants without divulging sensitive information to the multi-tenant layer.

An apparatus includes a memory and a hardware processor. The memory stores a plurality of logging rules. Each logging rule assigned to a tier of a multi-tier platform. The processor receives source code for an application configured to execute on a plurality of tiers of the multi-tier platform and detects, within the source code, an entry point and an exit point for a tier of the plurality of tiers. The processor determines, based on the plurality of logging rules, a first attribute that is to be logged during execution in the tier and a second attribute that is not to be logged during execution in the tier and inserts, between the entry point and the exit point in the source code, logging code that, when executed, logs the first attribute and hides the second attribute.