Personal Data Theft Detection via Pattern Hashing and Pre-Indexing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting personal data theft across networks like the surface web, deep web, and Darknet are resource-intensive and lack effective privacy management, making them inefficient and insecure.
Innovation Solution
A computer-implemented method and system that uses pattern recognition to automatically detect personal data theft by scraping and indexing data from the Internet, employing cryptographic hashing for privacy and fuzzy algorithms to generate variations of search terms, allowing for efficient and secure detection and reporting of potential data theft.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data theft monitoring methods are used to detect personal data across the Internet, then detection capability is provided, but resource consumption becomes excessive and efficiency is reduced
Solution Approach 1:
The system segments the detection process into distinct phases: data crawling/indexing, pattern extraction, cryptographic hashing, and pattern matching. By dividing the monolithic detection task into modular components, each handling a specific aspect of data theft detection, the system reduces overall resource consumption while maintaining comprehensive detection capability across the Internet
Solution Approach 2:
The system performs preliminary actions by pre-crawling and indexing Internet data, and pre-extracting patterns from the indexed data before actual theft detection is needed. This advance preparation creates a ready-to-search database structure, eliminating the need for resource-intensive real-time crawling during detection operations and significantly improving efficiency
2Measurement precision
If personal data is transmitted in plain-text format to the detection company, then detection accuracy is improved, but privacy security is compromised
Solution Approach 1:
The system creates and uses cryptographic hashes (cryptographic copies) of personal data patterns instead of handling the original plain-text data. These hash representations preserve the ability to detect stolen data while eliminating privacy risks, as the hashes cannot be reverse-engineered to reveal the original sensitive information
Solution Approach 2:
The system transforms personal data from its original plain-text form into cryptographic hash representations through a parameter change in data format. This transformation maintains the data's identity for matching purposes while fundamentally altering its structure to eliminate privacy vulnerabilities, allowing accurate detection without exposing sensitive information
3Object-affected harmful factors
If cryptographically hashed personal data is transmitted to the detection company, then privacy security is improved, but resource requirement for culling scales exponentially
Solution Approach 1:
The system segments the computational workload by pre-extracting patterns from crawled data and creating their cryptographic hashes in advance. This segmentation separates the heavy computational tasks (crawling, pattern extraction, hashing) from the lightweight matching operations, reducing real-time resource requirements while maintaining security
Solution Approach 2:
The system performs preliminary cryptographic hashing of patterns during the data indexing phase, before detection queries are executed. By preparing hashed patterns in advance, the system avoids the need to hash large volumes of data during real-time detection, preventing exponential resource scaling while preserving privacy security
4Reliability
If vast amounts of data are crawled and scraped from the Internet, then detection coverage is improved, but resource intensity increases significantly
Solution Approach 1:
The system performs preliminary data crawling, scraping, and indexing operations to build a comprehensive database of Internet data before detection is needed. This advance data gathering ensures broad detection coverage is already in place, eliminating the need for resource-intensive real-time crawling during actual theft detection operations
Solution Approach 2:
The system segments the data handling process into distinct stages: crawling, scraping, indexing, pattern extraction, and hashing. By organizing these operations in a structured sequence with intermediate storage at each stage, the system manages vast data volumes more efficiently, reducing peak resource intensity while maintaining comprehensive detection coverage
Data Source
AI summary
The present disclosure relates to systems and methods for information security, specifically for automatically detecting theft of personal data. In one implementation, a computer-implemented method for automatically detecting theft of personal data on the Internet may include at least one processor configured to execute instructions, the instructions including receiving from a user, an electronic communication containing a first search term, extracting via pattern recognition one or more patterns corresponding with the first search term, and comparing the one or more patterns with a subset of data scraped from the Internet, with the subset of data scraped from the Internet being indexed by pattern for the first search term. The instructions may also include flagging matches of the one or more patterns with the subset of data based on the comparison and transmitting information associated with the matches in a report that indicates a possible theft of personal data.