Real-Time Training Data Auditing for Sensitive Data Exposure
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Solution Overview
Problem
Existing data platforms inadvertently expose sensitive information during data ingestion and distribution, compromising information security in machine learning and artificial intelligence model training.
Innovation Solution
Implement a data management system that performs real-time data ingestion and auditing to identify and remove sensitive information, ensuring secure data handling by excluding sensitive data from exposed datasets and applying data anonymization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is ingested and distributed in real-time for machine learning training, then productivity and speed are improved, but sensitive information may be inadvertently exposed compromising information security
Solution Approach 1:
The system performs preliminary actions by implementing real-time data auditing and classification before data is distributed for machine learning training. The data processing system proactively identifies and flags sensitive information in incoming data streams, applying security measures beforehand to prevent inadvertent exposure while maintaining fast data processing speeds.
Solution Approach 2:
The patent introduces an intermediary layer between data ingestion and distribution that acts as a security filter. This intermediary component classifies data elements, identifies sensitive information, and applies appropriate security measures without blocking the overall data flow, thus maintaining productivity while enhancing information security.
2Reliability
If real-time data auditing is implemented to identify sensitive information, then information security is improved, but device complexity increases
Solution Approach 1:
The data processing system is segmented into distinct functional modules: data reception components, auditing components, classification components, and distribution components. Each module has a specific responsibility, making the overall complex system manageable through modular design while maintaining real-time security auditing capabilities.
Solution Approach 2:
The system dynamically adjusts processing parameters based on data classification results. When sensitive information is detected, the system changes parameters such as access controls, encryption levels, or routing decisions without restructuring the entire system, thus maintaining security while managing complexity through parameter adjustment rather than structural complexity.
Data Source
AI summary
In some implementations, a system may receive, at a first type of data structure, a set of data elements of a data stream. The system may forward the set of data elements to a second type of data structure and a third type of data structure. The system may receive, based on forwarding the set of data elements to the second type of data structure and the third type of data structure, a query for machine learning training data. The system may transmit, to a computational element associated with a machine learning processing platform, information relating to the set of data elements to train a machine learning model, wherein the information includes timing information relating to a set of instances of each data element of the set of data elements.