Machine-Driven Resource Disambiguation for Privacy-Safe Web Hints
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Solution Overview
Problem
Existing web page transmission techniques risk unintentionally sharing sensitive user information through crowd-sourced hints, compromising privacy.
Innovation Solution
Implement machine-driven crowd-disambiguation of network resources using fully ambiguated resource instances (FARI) and partially disambiguated resource instances (PDRI) to protect privacy, allowing resources to be resolved only when sufficiently anonymized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If crowd-sourced hints are used to improve web page loading time, then web page loading efficiency is improved, but user privacy is compromised due to potential exposure of sensitive information
Solution Approach 1:
The patent segments resource identifiers into two distinct forms: fully ambiguated resource instances (FARI) for privacy protection and partially disambiguated resource instances (PDRI) for resource resolution. This segmentation allows the system to simultaneously protect user privacy while enabling efficient resource loading by distributing different levels of identification information across multiple communication stages.
Solution Approach 2:
The patent introduces FARI as an intermediary representation that masks the actual resource identity while still allowing the system to track and resolve resources. The FARI acts as a privacy-protecting mediator between the user's resource requests and the crowd-disambiguation machine, preventing direct exposure of sensitive resource identifiers until disambiguation is complete.
2Measurement precision
If resource fingerprints are communicated to crowd-disambiguation machine, then resource resolution accuracy is improved, but sensitivity of user information increases
Solution Approach 1:
The patent implements a dynamic identification system where resource identifiers transition from fully ambiguated (FARI) to partially disambiguated (PDRI) states based on the disambiguation progress. This dynamic approach allows the system to maintain high resolution accuracy for resources that have been sufficiently disambiguated while protecting the privacy of resources that remain in the ambiguated state.
Solution Approach 2:
The patent changes the parameter of resource identifier ambiguity from a static state to a dynamic spectrum. Resources are represented with varying degrees of disambiguation (from fully ambiguated to partially disambiguated), allowing the system to adjust the level of information disclosure based on disambiguation confidence and privacy requirements.
3Measurement precision
If fully disambiguated resource identifiers are used, then resource identification precision is improved, but privacy protection is weakened
Solution Approach 1:
The patent segments the resource identification process into multiple stages with different levels of precision. FARI provides coarse-grained identification for privacy protection, while PDRI provides fine-grained identification for resource resolution. This multi-stage segmentation allows the system to use high-precision identifiers only when necessary and when privacy risks are mitigated through disambiguation.
Solution Approach 2:
The patent performs preliminary disambiguation actions before full resource identification is revealed. By pre-processing resource identifiers through the FARI→PDRI transformation pipeline and establishing disambiguation thresholds, the system prepares privacy-protected representations in advance, allowing high-precision identification to occur only after sufficient disambiguation has been achieved.
Data Source
AI summary
Embodiments seek to protect privacy of potentially sensitive client resources in web transactions using crowd-disambiguation. Crowd-disambiguation machines can aggregate information about resources from multiple clients as resource fingerprints, and can use the fingerprints to provide crowd-sourced services in a privacy-protected manner. For example, embodiments can communicate a resource fingerprint as a fully ambiguated resource instance (FARI) and a partially disambiguated resource instance (PDRI). When one (or few) clients communicates the resource fingerprint, the identity of the resource remains obfuscated from the crowd-disambiguation machine. As more clients communicate fingerprints for the same resource (e.g., identified by the matching FARIs), respective, differently generated PDRIs of those fingerprints enable the crowd-disambiguation machine to resolve further portions of the resource, ultimately permitting the resource to be revealed and considered non-private (e.g., for use in hint generation or other crowd-sourced services).