PII Tracking System for Social Network Post Analysis
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Solution Overview
Problem
Social networks often lead to accidental or intentional disclosure of Personally Identifiable Information (PII), which can compromise user security and computer security, as this information can be exploited for unauthorized access to accounts and systems.
Innovation Solution
Implementing a system that uses network analysis, content analysis, and trend analysis to detect PII disclosure patterns in social media posts, providing real-time feedback and recommendations to users on reducing or eliminating PII exposure through draft post evaluation and asynchronous detection methods, utilizing natural language processing to categorize and assess risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users freely publish content on social networks, then user expression and social interaction are enhanced, but PII disclosure risk increases
Solution Approach 1:
The system implements real-time feedback by analyzing draft posts before publication and notifying users of detected PII. The feedback mechanism provides specific information about what PII was detected and offers recommendations for modification, allowing users to publish safely while maintaining expression freedom.
Solution Approach 2:
The system performs preliminary analysis of content before it is published to the social network. By evaluating draft posts in advance and identifying PII beforehand, the system prevents harmful disclosures while allowing users to freely express themselves after receiving feedback.
2Reliability
If PII detection and analysis systems are implemented, then user security is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary PII detection service that sits between the user and the social network. This intermediary handles the complex analysis of content for PII using natural language processing and pattern recognition, while presenting a simple interface to users and requiring minimal changes to the social network infrastructure.
Solution Approach 2:
The system replaces manual PII detection with automated computational methods including natural language processing, machine learning models, and pattern recognition algorithms. This substitution handles the complexity of security analysis programmatically rather than requiring manual review processes.
3Object-affected harmful factors
If real-time PII detection is performed on draft posts, then PII exposure is reduced, but processing time and computational resources increase
Solution Approach 1:
The system performs PII detection on draft posts before final publication, allowing users to review and modify content while the analysis is occurring. This preliminary action prevents PII exposure without requiring delays at the moment of publication.
Solution Approach 2:
The system uses periodic scanning and analysis of draft post content rather than continuous monitoring. By analyzing content at specific intervals and triggering events (such as when a user pauses or submits), the system reduces computational overhead while maintaining effective PII detection.
Data Source
AI summary
Systems and methods are provided for receiving, at a server, activity data from one or more social networks that include one or more posts from a user. A network graph based on the one or more posts from the received activity data. The server may tokenize the contents of the one or more posts. The server may label and categorize the tokenized posts. Personally identifiable information (PII) may be determined from the labeled and categorized posts that are tokenized. A risk report may be generated based on determined PII in at least one of the labeled and categorized posts that are tokenized, and the risk report may be transmitted. In some implementations, the server may provide an application for composition of a social media post, where the application provides real-time feedback and content risk assessment of the post, and provides recommendations for reducing or eliminating PII in the post.