Automated Data Broker for Compliant Multi-Source Data Processing
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Solution Overview
Problem
The manual processing of user data for compliance with data privacy regulations and the inefficiencies arising from multiple downstream systems individually handling the same data leads to bottlenecks and inconsistent data processing, especially in scenarios involving numerous data sources and consumers.
Innovation Solution
An automated data broker system acts as an intermediary that processes input data from various services, ensuring compliance with privacy and regulatory requirements through pseudonymization, filtering, and transformation, providing standardized output data for multiple downstream systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing of user data is performed for compliance with data privacy regulations, then data quality and compliance are ensured, but processing efficiency and productivity deteriorate due to significant bottlenecks
Solution Approach 1:
The patent introduces an automated data broker system as an intermediary component between data sources and downstream systems. This broker automatically performs pseudonymization, filtering, and transformation of user data according to privacy regulations (GDPR, CCPA), eliminating the need for each downstream system to manually process data for compliance. The broker acts as a centralized mediator that handles compliance operations once, enabling multiple consumers to access compliant data efficiently.
Solution Approach 2:
The data broker performs compliance operations (pseudonymization, filtering, transformation) in advance before data is distributed to downstream systems. By preprocessing data to meet regulatory requirements upfront, the system eliminates the need for repeated manual compliance checks at each consumption point, significantly improving processing efficiency while maintaining data quality.
2Reliability
If multiple downstream systems individually process the same data for compliance, then each system ensures its own compliance requirements, but processing overhead and complexity increase significantly
Solution Approach 1:
The data broker is designed as a universal system that serves multiple downstream consumers simultaneously. It implements a single compliance processing pipeline that can handle requests from numerous downstream systems, replacing the need for each system to maintain its own compliance processing infrastructure. This multi-functional approach reduces overall system complexity while ensuring consistent compliance across all consumers.
Solution Approach 2:
The patent consolidates compliance processing operations from multiple independent downstream systems into a single centralized data broker. By merging duplicate compliance functions (pseudonymization, filtering, transformation) into one shared service, the system reduces architectural complexity and eliminates redundant processing while maintaining compliance for all downstream consumers.
3Reliability
If data is processed manually for pseudonymization and filtering, then data privacy compliance is achieved, but processing time and speed deteriorate
Solution Approach 1:
The patent replaces manual mechanical processing operations with automated computational systems. The data broker uses automated algorithms and software-based pseudonymization techniques to transform and filter data at high speed, eliminating the slow manual processes previously required for privacy compliance. This substitution dramatically increases processing speed while maintaining compliance standards.
Data Source
AI summary
An automated data broker system is provided. The automated data broker system serves as an intermediary system between input user data including user interactions with various types of services (for example, a video streaming platform, online retail shopping platform, video game streaming platform, etc.). The automated data broker system receives the various input data and modifies, filters, or otherwise transforms the input data using one or more APIs to produce output data that may then be provided to downstream systems for use and storage. The output data may be standardized in a form that is suitable for use in accordance with various rules relating to user data. For example, the output data may be pseudonymized and input data associated with users who have “opted-out” of data collection may be filtered out.