Dark Web PII Detection via Tor Intermediary and Pattern Matching
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Solution Overview
Problem
Existing systems are inefficient in identifying and verifying compromised personally identifiable information (PII) on the dark web, making it difficult to determine the extent of risk associated with PII data breaches, as traditional methods are burdensome and impractical due to the vast number of unindexed websites on the dark web.
Innovation Solution
A system configured to identify PII data patterns on various websites, using a crawler to extract and process data, and an AI engine to verify PII, which prioritizes websites based on the presence of PII data and links to other potential sources, assigning a risk score to matched data items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional web search methods are used to find compromised PII on the dark web, then the approach is simple and requires no special infrastructure, but it is completely ineffective because dark web sites are not indexed by standard search engines
Solution Approach 1:
The patent employs Tor as an intermediary network layer to access the dark web. Tor provides anonymous routing through a relay network, enabling the system to reach dark web sites that would otherwise be inaccessible. This intermediary infrastructure resolves the contradiction by providing reliable dark web access without requiring direct complex peer-to-peer connections or deep packet inspection capabilities.
Solution Approach 2:
The patent replaces traditional mechanical web crawling and indexing mechanisms with pattern-based detection. Instead of relying on search engine indexing (which doesn't work on dark web), the system uses regex patterns and data scraping to identify PII. This substitution of detection mechanisms enables reliable PII finding on dark web without requiring the complex infrastructure of traditional search engines.
2Measurement precision
If the system crawls all dark web sites to ensure comprehensive PII detection, then detection completeness is maximized, but the process becomes prohibitively expensive and time-consuming
Solution Approach 1:
The patent performs preliminary actions by pre-defining regex patterns for common PII formats (Social Security Numbers, credit card numbers, passport numbers, etc.) before crawling begins. This allows the system to immediately identify and extract PII when encountered, without needing to crawl entire sites exhaustively. The pre-prepared pattern library enables rapid, targeted detection that maintains completeness while reducing time requirements.
Solution Approach 2:
The patent applies partial action by focusing crawling and detection efforts on specific high-value targets rather than uniformly scanning all dark web sites. The system prioritizes sites known to host PII, uses pattern matching to quickly identify relevant content, and stops crawling when sufficient PII is found. This selective approach maintains detection precision while dramatically reducing the time and resources required compared to exhaustive crawling.
3Reliability
If the system visits numerous dark web sites to find compromised PII, then the likelihood of finding all compromised data increases, but the cost and complexity of operating such a system becomes impractical
Solution Approach 1:
The patent implements self-service by having the system automatically generate and refine its own search criteria through pattern analysis. The regex patterns are dynamically updated based on observed PII formats on dark web sites, allowing the system to adapt to changing tactics without external intervention. This self-adjusting mechanism maintains high reliability in PII detection while reducing operational complexity, as the system manages its own optimization rather than requiring manual configuration or complex administrative overhead.
Data Source
AI summary
A compromised data exchange system extracts data from websites using a crawler, detects portions within the extracted data that resemble personally identifying information (PII) data based on PII data patterns using a risk assessment module, and compares a detected portion to data within a database of disassociated compromised PII data to determine a match using the risk assessment module. A risk score may be assigned to a data item within the database in response to determining the match. In some embodiments, URL data may also be detected in the extracted data. The detected URL data represents further websites that can be automatically crawled by the system to detect further PII data.