Information Leak Prevention Ambiguity Resolution
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Solution Overview
Problem
Current digital traffic filtering systems face significant challenges in effectively mitigating false positive indications of unauthorized information dissemination, leading to inefficient resource allocation and potential legal and business risks due to high rates of false alarms and miss-detections.
Innovation Solution
A method and system that define positive and negative criteria sets, establish an ambiguity set, and apply ambiguity resolution criteria to accurately identify and mitigate false positives, using techniques such as Luhn validation, checksum validation, and statistical analysis to validate and categorize electronic traffic items, thereby reducing false positive errors and ensuring compliance with information protection policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital traffic filtering systems use information filters and regular expressions to identify patterns of private information, then information leak prevention capability is improved, but false positive error rate increases
Solution Approach 1:
The patent introduces an intermediary validation process between the initial pattern matching and the final false positive determination. This intermediary layer uses multiple validation techniques (Luhn validation, checksum validation, statistical analysis) to verify whether detected patterns represent actual private information or false positives, thereby maintaining detection capability while reducing erroneous alarms
Solution Approach 2:
The system dynamically changes validation parameters and criteria based on the specific context of detected patterns. Different validation methods are applied depending on the type of information detected, and the system adjusts its sensitivity and validation thresholds to optimize the balance between detection accuracy and false positive reduction
2Reliability
If digital traffic filtering systems apply strict information leak prevention policies, then security compliance is improved, but resource allocation efficiency deteriorates due to high false alarm rates
Solution Approach 1:
The system applies partial validation actions based on the confidence level and context of detected patterns. Instead of applying full validation procedures to all detected items, the system selectively applies validation based on risk assessment, thereby maintaining security compliance while reducing unnecessary resource consumption on low-risk false positives
Solution Approach 2:
The system implements feedback mechanisms where the results of validation processes are used to refine future detection and validation decisions. The system learns from validated cases to improve its ability to distinguish true positives from false positives, thereby enhancing both security compliance and resource efficiency over time
3Measurement precision
If digital traffic filtering systems use multiple validation techniques, then false positive mitigation is improved, but system complexity increases
Solution Approach 1:
The validation system is segmented into multiple independent validation modules (Luhn validation module, checksum validation module, statistical analysis module). Each module handles a specific validation technique, allowing the system to apply multiple validation methods without creating a monolithic complex structure. This modular segmentation makes the system more manageable and maintainable despite using multiple validation techniques
Data Source
AI summary
A method for mitigating false positive type errors while applying an information leak prevention policy to identify important information and to prevent outward leakage. A positive criterion is defined for a positive set, and a negative criterion for a negative set of benign traffic. An ambiguity set contains items showing indications for both positive and negative sets. An ambiguity resolution criterion allows ambiguous items to be placed in/removed from the positive set or negative set. Each information item is searched for matches with the positive set. Each item in the positive set is checked for membership in the ambiguity set. The ambiguity resolution criteria are used for each member of the ambiguity set and to remove items from the positive set accordingly. The leak prevention policy is applied for all items remaining in the positive set thus protecting the important information.