Friendly Man-in-the-Middle Data Stream Correlation for Privacy Protection
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Solution Overview
Problem
Current methods for controlling Internet access and protecting private information on Internet-connected devices lack finer-grain controls, leading to vulnerabilities in data privacy, as they either block wholesale access or require manual endpoint-based protections, failing to detect and manage sensitive information leakage through cookies and beacons.
Innovation Solution
Implementing a friendly man-in-the-middle (FMITM) system that performs lexical analysis on cookies and beacons within a network, intercepting and transforming data streams to block or anonymize sensitive information, using a centralized point of control like a broadband router or standalone device, and providing a user interface for content-aware inspection and policy-based management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wholesale blocking of Internet access is implemented, then data privacy protection is improved, but Internet accessibility and usability deteriorate
Solution Approach 1:
The patent segments Internet access control into two layers: (1) wholesale blocking at the router level for basic privacy protection, and (2) fine-grained content-level filtering at the endpoint device for selective access. This allows users to combine both approaches - maintaining wholesale blocking as a baseline while enabling specific applications or content types to pass through with enhanced privacy controls.
Solution Approach 2:
The patent introduces an intermediary component (privacy protection software or browser plugin) that sits between the user and the Internet, intercepting data streams and applying contextual analysis. This intermediary enables fine-grained control over specific types of content (cookies, beacons, tracking pixels) without blocking entire applications or websites, thus maintaining accessibility while improving privacy.
2Measurement precision
If manual endpoint-based protections are deployed, then data privacy control precision is improved, but device complexity and administrative burden increase
Solution Approach 1:
The patent implements self-service mechanisms where the privacy protection system automatically performs contextual analysis of data streams, identifies sensitive information patterns (cookies, beacons, tracking pixels), and applies filtering rules without requiring manual user intervention. The system self-configures and adapts to different content types automatically, eliminating the need for users to manually configure complex privacy settings for each application or website.
Solution Approach 2:
The patent creates a universal privacy protection mechanism that works across multiple applications, browsers, and devices through a single centralized configuration. The contextual analysis engine provides multi-functional capability to detect and filter various types of tracking content (cookies, beacons, web bugs, pixels) using a unified approach, eliminating the need for separate manual configurations for each application or device.
3Measurement precision
If contextual analysis of data streams is performed, then detection of sensitive information leakage is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial analysis by focusing computational resources only on specific portions of data streams that are most likely to contain sensitive information - namely cookies, beacons, tracking pixels, and other known tracking content types. Rather than analyzing every byte of data traffic, the system selectively applies contextual analysis patterns to suspected content, significantly reducing processing overhead while maintaining high detection accuracy.
Solution Approach 2:
The patent implements preliminary action by pre-configuring contextual analysis patterns and signatures for known tracking content types (cookies, beacons, web bugs, pixels) before data streams are processed. These pre-established patterns enable rapid matching and identification of sensitive content without requiring complex real-time analysis, thus reducing processing time while maintaining detection accuracy.
Data Source
AI summary
Example embodiments of the present invention relate to a method, an apparatus and a computer-program product for friendly man-in-the-middle data stream correlation. An example method includes receiving a data stream transmitted from a source intended for a destination. A contextual analysis of portions of the data stream then may be performed, with respective portions of the data stream being correlated according to the contextual analysis.