Session Replay Privacy Masking Through Dynamic DOM Node Detection
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Solution Overview
Problem
Existing systems fail to effectively identify and mask private information in user interactions with websites, leaving it vulnerable to unauthorized access due to static tagging methods that become outdated with website changes, and do not efficiently handle false positives.
Innovation Solution
A system that dynamically identifies private information using session data and a browser module, updates a capture agent to mask corresponding nodes in the DOM, and includes a scanning module to detect and prevent false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static tagging methods are used to identify private information, then the system is simple to implement, but the identification accuracy deteriorates when website changes occur
Solution Approach 1:
The system transitions from static tagging to dynamic identification by using a browser module that executes JavaScript code during user sessions to automatically detect and identify private information fields based on current website state, ensuring accuracy despite website changes
Solution Approach 2:
The system enables self-updating of private information field identification by automatically learning from user interactions and session data, allowing the browser module to adapt to website changes without manual reconfiguration
2Productivity
If all user interactions are monitored and stored for analysis, then user experience issues can be identified, but private information security is compromised
Solution Approach 1:
The system extracts and masks private information from captured user interactions before transmission and storage. The masking module removes or obfuscates sensitive data while preserving the structural information needed for user experience analysis
Solution Approach 2:
The system introduces a masking module as an intermediary between data capture and data storage/transmission. This intermediary layer processes the captured interactions to remove private information while maintaining the analytical value of the session data
3Measurement precision
If dynamic identification of private information is implemented, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The browser module serves multiple functions: it captures user interactions, identifies private information fields dynamically, and works with the masking module to protect data. This multi-functionality reduces the need for separate dedicated components
4Ease of manufacture
If traditional masking methods are used, then implementation is straightforward, but false positives cannot be effectively reduced
Solution Approach 1:
The system uses feedback from user interactions and session analysis to refine private information field identification. By analyzing patterns in user behavior and data entry, the system learns to distinguish actual private information from false positives
Data Source
AI summary
Techniques are for identifying and masking instances of private information in tracked user interactions with a network site during a network session. A scanning module can receive set of captured user interactions (e.g., movements between portions of the network site and data provided in one or more fields on the network site) with the network site during the network session. Instances of private information can be identified and a number of nodes in a document object model (DOM) for the network site that correspond to each identified instance of private information. Information about each corresponding node can be provided to update a capture agent to mask each corresponding node in future user interactions.