Abstract: A data security and fraud prevention system is configured to transmit a web form to a first device, receive through the web form first data including first user private data associated with a first user, receive a request to share the web form with a second user, create a cache storage and an unique identifier associated with the web form, and mask the first user private data to prevent access to the first user private data by the second user. The data security and fraud prevention system is further configured to encrypt the unique identifier, generate a share session between the first device and a second device associated with the second user, receive through the web form second data including second user private data associated with the second user, mask the second user private data to prevent access to the second user private data by the first user, and aggregate the first data and the second data to complete the web form.

Abstract: Methods and systems for using machine learning models to organize and select modular components for user interface templates. For example, the methods and systems may use machine learning and/or artificial intelligence to generate user interfaces with a preferred number of user input fields and/or pages, in which the preferred number both satisfies any requirements for the user interface (e.g., information, categories of information, etc.), and ensures that all privacy concerns are met.

Abstract: In some implementations, a device may obtain the biometric token associated with an account via a registration procedure associated with a first platform that is associated with a host. The server device may store an indication of the biometric token in a database. The server device may receive a login request associated with the account for a second platform that is associated with the host. The server device may identify that biometric authentication is enabled for the account based on the indication of the biometric token being stored in the database. The server device may provide an authentication challenge associated with the biometric token. The server device may receive a response to the authentication challenge indicating whether the authentication device has authenticated biometric information associated with a user based on the biometric token. The server device may authenticate the login request for the second platform based on the response.

Abstract: In some implementations, a system may receive, from a user device, a request to access information associated with the application. The request may include user device data indicating characteristic(s) associated with a particular use of the user device. The system may provide the user device data as input to a machine learning model, which may be trained based on historical data associated with historical usage of the application by the user device and/or other user devices. The system may receive, as an output from the machine learning model, a target environment associated with the user device. The system may identify a target user interface (UI) associated with the information associated with the application. The target UI may correspond to the target environment. The system may transmit, to the user device, UI data corresponding to the target UI.

Abstract: A data security and fraud prevention system is configured to transmit a web form to a first device, receive through the web form first data including first user private data associated with a first user, receive a request to share the web form with a second user, create a cache storage and an unique identifier associated with the web form, and mask the first user private data to prevent access to the first user private data by the second user. The data security and fraud prevention system is further configured to encrypt the unique identifier, generate a share session between the first device and a second device associated with the second user, receive through the web form second data including second user private data associated with the second user, mask the second user private data to prevent access to the second user private data by the first user, and aggregate the first data and the second data to complete the web form.

Abstract: Methods and systems for using machine learning models to organize and select modular components for user interface templates. For example, the methods and systems may use machine learning and/or artificial intelligence to generate user interfaces with a preferred number of user input fields and/or pages, in which the preferred number both satisfies any requirements for the user interface (e.g., information, categories of information, etc.), and ensures that all privacy concerns are met.

Abstract: Methods and systems for using machine learning models to organize and select modular components for user interface templates. For example, the methods and systems may use machine learning and/or artificial intelligence to generate user interfaces with a preferred number of user input fields and/or pages, in which the preferred number both satisfies any requirements for the user interface (e.g., information, categories of information, etc.), and ensures that all privacy concerns are met.

Abstract: Methods and systems for a service worker framework are provided herein. When a web application is accessed through a web browsing application, the web browsing application may invoke a service worker specifically created for the web application. The service worker may serve as an intermediary between the web application and the web browsing application and may intercept application data transmitted between the web browsing application and the web application. For example, the service worker may extract and encrypt any identification data included in the application data when it is received by a web browsing application. The service worker may store the encrypted identification data, for example, within the cache of the web browsing application and send modified application data to the web application (e.g., excluding the identification data).

Abstract: Methods and systems for using machine learning models to organize and select modular components for user interface templates. For example, the methods and systems may use machine learning and/or artificial intelligence to generate user interfaces with a preferred number of user input fields and/or pages, in which the preferred number both satisfies any requirements for the user interface (e.g., information, categories of information, etc.), and ensures that all privacy concerns are met.

Abstract: A data security and fraud prevention system is configured to transmit a web form to a first device, receive through the web form first data including first user private data associated with a first user, receive a request to share the web form with a second user, create a cache storage and an unique identifier associated with the web form, and mask the first user private data to prevent access to the first user private data by the second user. The data security and fraud prevention system is further configured to encrypt the unique identifier, generate a share session between the first device and a second device associated with the second user, receive through the web form second data including second user private data associated with the second user, mask the second user private data to prevent access to the second user private data by the first user, and aggregate the first data and the second data to complete the web form.

Abstract: Methods and systems for using machine learning models to organize and select modular components for user interface templates. For example, the methods and systems may use machine learning and/or artificial intelligence to generate user interfaces with a preferred number of user input fields and/or pages, in which the preferred number both satisfies any requirements for the user interface (e.g., information, categories of information, etc.), and ensures that all privacy concerns are met.