Compromised PII Detection via Pattern Crawling and Risk Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to effectively identify and mitigate compromised personally identifiable information (PII) on the internet, particularly in cases of data breaches and phishing, as PII data is often sold on unindexed websites and difficult to detect due to its varied presentation patterns.
Innovation Solution
A system configured to crawl websites for data patterns resembling PII, using a risk assessment module to extract and verify PII data, compare it to a database of compromised PII, and assign risk scores, while disassociating and encrypting data to prevent re-identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional search engines are used to find compromised PII websites, then the search process is simple, but the dark web and unindexed websites cannot be effectively searched
Solution Approach 1:
The system segments the web into indexable and non-indexable portions, applying different detection methods to each. Traditional search engines handle indexable websites while the system uses specialized crawling and pattern recognition for dark web and unindexed sites, resolving the contradiction between ease of search and detection capability.
Solution Approach 2:
The system introduces an intermediary layer between traditional search engines and the dark web. This intermediary uses alternative search mechanisms and pattern recognition algorithms to access and detect compromised PII on websites that traditional search engines cannot reach, maintaining operational simplicity while extending detection reach.
2Reliability
If PII data is searched and extracted from websites, then compromised data can be identified, but the system complexity increases due to pattern recognition and data verification processes
Solution Approach 1:
The system employs self-service mechanisms where the pattern recognition module automatically learns and adapts to different PII presentation formats without requiring manual reconfiguration. The verification process self-corrects by cross-referencing multiple data sources and patterns, maintaining high reliability while reducing the need for complex manual intervention.
Solution Approach 2:
The system dynamically changes search parameters and pattern recognition rules based on the website structure and data presentation methods. This allows the same system architecture to adapt to various website formats and dark web platforms, maintaining reliability without requiring proportional increases in system complexity.
3Measurement precision
If data is scraped and stored in databases, then PII can be compared against compromised data, but data security risks increase due to storage of sensitive information
Solution Approach 1:
The system extracts only the necessary pattern information from scraped data for comparison purposes, separating the identification function from the storage function. By storing pattern signatures rather than full PII records, the system maintains matching precision while significantly reducing the impact of potential data breaches.
Solution Approach 2:
The system segments data storage into multiple layers: raw scraped data is temporarily stored, then processed into pattern signatures that are stored separately. This segmentation allows precise matching while limiting the exposure of sensitive information in case of a breach, as only de-identified patterns remain in the database.
4Measurement precision
If pattern recognition algorithms are used to identify PII, then detection accuracy improves, but false positives increase due to similar patterns in legitimate data
Solution Approach 1:
The system implements feedback mechanisms where detected PII patterns are verified against multiple criteria and cross-referenced with known legitimate data patterns. Results are fed back into the system to refine pattern recognition algorithms, reducing false positives while maintaining high detection accuracy through continuous learning and adjustment.
Solution Approach 2:
The system applies different pattern recognition thresholds and validation rules to different data contexts and websites. By tailoring the quality standards locally to each website's characteristics and data presentation methods, the system maintains high accuracy while reducing false positives that would occur with uniform application of detection rules.
Data Source
AI summary
In some embodiments, a compromised data exchange system may include an interface configured to couple to a network, a processor, and a memory accessible to the processor. The memory may be configured to store instructions that, when executed, cause the processor to extract data from one or more websites using a crawler, detect portions within the data that resemble personally identifying information (PII) data based on PII data patterns using a risk assessment module, and to compare a detected portion to data within a database of disassociated compromised PII data to determine a match using the risk assessment module. The instructions may further cause the processor to selectively assign a risk score to a data item within the database in response to determining the match using a risk scoring module.