Geolocation Probe Trust Scoring for Malicious Data Filtering
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Solution Overview
Problem
Existing internet geolocation methods face challenges in achieving precision, timeliness, reliability, and resilience, particularly in the presence of malicious probes that can compromise the accuracy of geolocation results.
Innovation Solution
A computer-implemented method that computes trust scores for each probe based on trust score penalties, which are calculated by comparing probe distances to the target and distances between probes. This method selects the most trustworthy probes for subsequent geolocation instances, thereby enhancing the reliability and accuracy of geolocation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all probes are used in geolocation instances, then the quantity of probes increases, but the reliability and accuracy decrease due to malicious probes
Solution Approach 1:
The patent extracts malicious probes from the probe set by computing trust scores and removing probes with scores below a threshold. This separates the harmful elements (malicious probes) from the useful elements (trustworthy probes), thereby improving geolocation reliability while maintaining an optimized number of probes.
Solution Approach 2:
The patent implements a feedback mechanism where trust scores are computed based on historical probe performance and distance consistency, and these scores feed back into the probe selection process for future geolocation instances. This continuous feedback loop enables the system to adaptively improve reliability by learning from past probe behavior.
2Reliability
If trust score computation is performed for all probes, then the reliability improves, but the computational complexity and time increase
Solution Approach 1:
The patent applies local quality by computing trust scores differently for different probes based on their individual characteristics and historical performance. Rather than applying a uniform complex computation to all probes, the system tailors the trust score computation to each probe's specific data availability and reliability profile, optimizing the balance between reliability improvement and computational complexity.
3Measurement precision
If distance comparisons are performed for all probe pairs, then the measurement precision improves, but the productivity decreases due to increased computation time
Solution Approach 1:
The patent applies partial action by performing distance comparisons only for probe pairs that are necessary for computing trust scores, rather than exhaustively comparing all possible probe pairs. The system computes distances selectively based on probe proximity and relevance, achieving sufficient measurement precision without the computational overhead of complete pairwise comparison.
Data Source
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AI summary
A computer-implemented method comprising: computing, for each probe of a plurality of probes used in a first instance of geolocation for locating a target, a trust score based on a trust score penalty corresponding to the probe; and selecting, based on the trust scores, at least one probe from the plurality of probes for use in a second instance of geolocation, wherein the computer-implemented method comprises, before computing the trust scores based on the trust score penalties, computing the trust score penalties by performing a penalty computation process.