Hashed Candidate Interception for Sensitive Data Leakage Prevention
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Solution Overview
Problem
Existing methods fail to effectively detect and prevent the accidental or malicious release of sensitive data, such as personal information and credit card credentials, due to difficulties in identifying protected information at a byte level and distinguishing sensitive data from non-sensitive information, especially in phishing and email scams.
Innovation Solution
A system that uses a processor to receive hashed sensitive data, a hashing function, and text classifiers to monitor user interfaces, detect input or output text, hash candidates, match them with hashed sensitive data, and intercept output text based on extracted attributes, thereby preventing data leakage and phishing attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text scanning and hashing is performed to detect sensitive data, then detection accuracy is improved, but processing time and system performance deteriorate
Solution Approach 1:
The system pre-hashes sensitive data attributes and stores them in a database before actual detection occurs. This preliminary preparation allows the system to perform fast lookup operations during runtime without recalculating hashes, significantly improving detection speed while maintaining accuracy.
Solution Approach 2:
The system extracts only the necessary attributes from the full text data (such as names, dates, amounts) and processes only these extracted attributes through hashing and comparison. This extraction approach reduces the processing volume while maintaining detection effectiveness on the most critical sensitive information.
2Adaptability or versatility
If comprehensive text scanning is performed to detect all sensitive data types, then detection coverage is improved, but system complexity and resource consumption deteriorate
Solution Approach 1:
The system segments the detection process into distinct modules: text extraction, attribute identification, hashing, database comparison, and alert generation. Each module handles a specific aspect of sensitive data detection, making the overall system more manageable and easier to extend for new data types without increasing overall complexity.
Solution Approach 2:
The system uses a universal hashing function and attribute-based detection mechanism that can identify multiple types of sensitive data (names, dates, amounts, etc.) through a single consistent process. This multi-functional approach expands detection coverage without proportionally increasing system complexity.
3Reliability
If real-time monitoring of user input and output is implemented, then data protection effectiveness is improved, but user experience and performance impact deteriorate
Solution Approach 1:
The system operates transparently in the background, automatically monitoring and detecting sensitive data without requiring user intervention or awareness. The monitoring process is self-contained and does not interfere with normal user operations, maintaining both protection effectiveness and user experience.
Data Source
AI summary
An example system includes a processor to receive hashed sensitive data including attributes, a hashing function, and text classifiers. The processor is to also monitor a user interface and detect an input text or an output text. The processor is to further scan the detected input text or the detected output text to detect a candidate based on the text classifiers. The processor is also to hash the detected candidate using the hashing function to generate a hashed candidate. The processor is to match the hashed candidate with hashed sensitive data. The processor is to also further extract an attribute from the matched hashed sensitive data. The processor is to intercept the output text based at least on the extracted attribute.