Paypal, Inc.

Vladimir Bacvanski

Michael Charles Todasco

Michael Jim Tien Chan

Alejandro Picos

Srinivasabharathi Selvaraj

Deepa Madhavan

Oluwatomisin Olayemi Jenrola

Gwendolyn Phelan Roberts

Sudheer Kilari

Meena Nagarajan

Arunkumar Kannimar Ponnaiah

Suryatej Gundavelli

Binh Nguyen

Liam Julian DiGregorio

Clare Patrice Tandy

John Lucas Timoney

Aklen Altanel

Suhail Sadiq

Devdatta Rivonkar

Rahul Mahendrakumar

Maria Mileder

Prajakta Dhavali

Daniel Santiago Rincon Silva

Methods and systems are presented for authenticating web content. A request to authenticate web content is received from a user device. A first document object model (DOM) tree representing elements of the web content within a hierarchical structure is accessed. A hash value for each node of the first DOM tree is calculated based on the corresponding element of the web content represented by that node. A second DOM tree associated with a host of the web content is retrieved from a database. The second DOM tree represents predefined elements of the web content. The hash value calculated for each node of the first DOM tree is compared with a hash value associated with a corresponding node of the second DOM tree to determine whether the first DOM tree matches the second DOM tree. The web content is authenticated when the first DOM tree matches the second DOM tree.

Page integrity assurance

Novel technical ways of analyzing a blockchain system using machine learning are presented. In various embodiments, A system can deploy, by a first entity, a policy smart contract on a blockchain to analyze a first smart contract deployed by a second entity, wherein the policy smart contract is governed by a set of rules, wherein the policy smart contract performs a first assessment that includes analyzing a set of functionalities of the first smart contract and detects a set of vulnerabilities associated with the first smart contract based on the set of rules. The system can determine at a first time a risk score corresponding to the first smart contract based on the analyzing and the detecting. In response to determining that the risk score is above a threshold score, the system can restrict users of a first platform corresponding to the first entity from accessing the first smart contract.

Automatic verification of decentrailized protocols

Techniques for data lifecycle discovery and management. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Rights management regarding user data associated with data lifecycle discovery platform

There are provided systems and methods for automated configuration of augmented and virtual reality avatars for user specific behaviors. A user may view a virtual environment and/or capture visual data utilizing a device at a location, where the visual data includes one or more real-world objects, or may interact with a virtual reality environment. An augmented or virtual reality experience may be displayed with virtual objects, graphics, avatars, and/or other virtual visualizations and interactions. A service provider may detect real-time data of the user's behaviors and current events or parameters for the corresponding augmented or virtual reality. Using available avatar customizations and configurations, the service provider may automatically configure avatars for presentation in such digital environments. These customizations may be used to automate avatar configurations and presentations without requiring user inputs and based on real-time data, which may be specifically coded and configured dependent on a digital environment.

Automated configuration of augmented and virtual reality avatars for user specific behaviors

Methods and systems are presented for dynamically modifying electronic content presented on a user device by third-party content providers based on goals associated with one or more entities. A content modification system may receive inputs related to goals for a user. The content modification system may synthesize the goals associated with the user. When the user uses a user device to request content from a third-party server, the content modification system may obtain the content and extract items included within the content. The content modification system may modify the content based on the synthesized goals, such as re-arranging the items, highlighting some of the items, or adding and/or removing items. The content modification system may cause the user device to present the modified content.

Goal-based dynamic modifications to user interface content

Techniques for managing erasure of data are presented. In response to receiving a request for erasure of data from a set of data stores, an erasure component can analyze a set of rules and information relating to the user account, including an account status and erasure hold status associated with the user account. The set of rules can be based on legal or contractual obligations applicable to the set of data stores, and can indicate various conditions under which data associated with a user account of a user can be eligible to be erased from the set of data stores or an associated data vault repository. The erasure component can determine eligibility for erasure of all or a portion of the set of data from the set of data stores based on the analysis results. Erasure component can determine erasure eligibility scores to pre-qualify user accounts for erasure eligibility.

Management of erasure or retention of user data stored in data stores

Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Data lifecycle discovery and management

Different communication and software protocols may be used by different blockchain networks. Cross-chain communication is provided via a software bridge configured to enable specific operations. An event request may be sent from a first blockchain network to a second blockchain network. The event request may be processed using a first protocol of the first network to provide a first output. An offer of equivalent processing provided by the second network may be verified by: processing the event request using a second protocol of the second network to provide a second output, and confirming that the second output matches the offer of equivalent processing. A processing equivalence of the event request may be established between the first network and the second network based on the first output of the event request processed using the first protocol and the second output of the event request processed using the second protocol.

Universally trusted bridges for heterogenous blockchain networks

An application runs in a first security zone of a computer system. Trace information generated from running the application is stored in a first security zone. Filtered trace information is generated by removing specified information from the trace information. The filtered trace information is stored in a particular storage location within the first security zone. An adapter application that is running within the first security zone is executable to access the particular storage location and call a second security zone. The adapter application transmits, from the first security zone to a datastore within the second security zone, the filtered trace information stored in the particular storage location. Communication between the first security zone and the second security zone is one-way from the first security zone to the second security zone.

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Mountain View, CA, United States of America

Vladimir Bacvanski