Anonymized Data Analysis for Real-Time Communication Security
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Solution Overview
Problem
Existing methods fail to effectively analyze communication sessions in real-time using anonymized sensitive information data, hindering the ability to improve security and prevent phishing attempts or other security breaches without compromising compliance requirements.
Innovation Solution
A system and method that utilize anonymized sensitive information data, combined with contextual data and a trained Machine Learning model, to generate real-time instructions for improving communication session outcomes, including security alerts and user prompts, while ensuring compliance by anonymizing and classifying sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensitive information data is anonymized to ensure compliance, then compliance requirements are met, but the ability to analyze communication sessions for security threats is reduced
Solution Approach 1:
The patent introduces an intermediary analysis layer that operates on anonymized data representations. The system creates simplified models of communication patterns from anonymized sensitive information, allowing security analysis without direct access to raw personal data. This intermediary layer preserves compliance while enabling threat detection through pattern recognition in the anonymized data structure.
Solution Approach 2:
The system creates copies of communication patterns and sensitive information structures for analysis purposes. By working with replicated anonymized data models rather than original data, the system can perform extensive security analysis and training without compromising the original anonymized datasets, thus maintaining both compliance and detection capabilities.
2Difficulty of detecting and measuring
If sensitive information data is used to detect suspicious activities, then security analysis accuracy is improved, but compliance requirements are violated
Solution Approach 1:
The system uses anonymized data as an intermediary that enables suspicious activity detection without violating compliance. The analysis module processes anonymized information representations to identify patterns indicative of phishing or security breaches, maintaining the ability to detect threats while ensuring personal data protection through the anonymization layer.
3Speed
If real-time analysis of communication sessions is performed, then security response time is improved, but processing complexity increases
Solution Approach 1:
The system segments the communication analysis process into distinct modules: data collection, anonymization, pattern analysis, and alert generation. Each module handles specific tasks independently, allowing real-time processing through pipelined operations. The segmentation enables parallel processing of different analysis functions, reducing overall complexity while maintaining fast response times.
Solution Approach 2:
The system performs preliminary actions by pre-defining security patterns, thresholds, and analysis rules before actual communication sessions occur. This preparation allows the real-time analysis to focus only on matching current data against pre-established criteria, significantly reducing processing complexity during live operations while maintaining rapid response capability.
Data Source
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AI summary
Since the inception of the General Data Protection Regulation (GDPR), it has become necessary for companies to take certain actions towards personal information, specifically personal information processing. In order to avoid liability, companies limit the type of personal data they process, or employ techniques such as anonymization, to help them remain compliant. The present invention discloses a way to utilize anonymized sensitive information data (such as personal information) and positively influence the outcome of a communication session. Provided is an instruction generation device for generating, in real-time, an instruction concerning a communication session. The instruction generation device generates an analysis result by analysing anonymized sensitive information data and communication data. A trained Machine Learning model generates a probability value representing an outcome of the communication session using the analysis result and contextual data. The probability value is used to generate an instruction which affects the outcome of the communication session.