Inference Detection System for Document Privacy Protection
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Solution Overview
Problem
The challenge lies in detecting and preventing undesirable inferences from documents, especially when private information becomes partially public, allowing the public to infer sensitive details that were intended to remain private, due to the difficulty in concealing both direct and indirect clues in a vast and interconnected information environment like the Web.
Innovation Solution
A system that extracts relevant keywords from documents, formulates queries against a reference corpus, and identifies potential inferences by analyzing the intersection of knowledge from the documents and the reference corpus, using techniques like TF.IDF analysis and search query ratios to flag potentially dangerous inferences for manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If information is made publicly accessible on the Web, then information availability and accessibility are improved, but information privacy protection deteriorates due to easy inference of sensitive details from disparate sources
Solution Approach 1:
The system performs preliminary inference detection before publishing documents. It extracts keywords from the document, formulates search queries, and checks if combinations of keywords can infer sensitive information from reference corpora, allowing preventive redaction before public release
Solution Approach 2:
The system introduces an intermediary inference detection mechanism between the document and the public. This intermediary analyzes potential inferences by searching reference corpora with keyword combinations, acting as a buffer to prevent harmful information leakage while maintaining public access
2Object-affected harmful factors
If all information including indirect clues is concealed to protect privacy, then information privacy protection is improved, but information accessibility and usefulness deteriorate
Solution Approach 1:
The system applies selective redaction rather than blanket concealment. It analyzes each keyword combination locally to determine if it creates harmful inferences, redacting only specific sensitive information while leaving non-sensitive information accessible
Solution Approach 2:
The system changes the parameter of information disclosure from binary (all or nothing) to graded. It uses inference strength metrics to determine the level of redaction needed, allowing partial disclosure of information that does not create significant inference risks
3Measurement precision
If comprehensive inference detection is performed by analyzing all possible keyword combinations against reference corpora, then inference detection accuracy is improved, but system complexity and computational cost increase
Solution Approach 1:
The system performs partial inference detection by focusing on the most likely inference paths. It uses techniques like TF.IDF to prioritize important keywords and limits analysis to top-k search results, achieving sufficient detection accuracy without exhaustive analysis of all possible combinations
4Object-affected harmful factors
If extensive redaction is applied to prevent all possible inferences, then privacy protection is improved, but document quality and readability deteriorate
Solution Approach 1:
The system applies redaction locally only where inference risks are detected. It preserves the original document quality in non-sensitive areas while selectively redacting only the minimal necessary information that could lead to harmful inferences
Data Source
AI summary
One embodiment of the present invention provides a system that detects inferences from documents. During operation, the system receives one or more documents and extracts a first set of knowledge relevant to the documents. The system further formulates one or more queries to one or more reference corpora based on the first set of knowledge. The system then extracts a second set of knowledge from results received in response to the queries. Additionally, the system produces a mapping relationship between at least one document and a piece of the second set of knowledge which is not within the first set of knowledge, the mapping relationship indicating an inference from the documents.