Digital Resource Access Frequency Estimation with Private Tree Values
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Solution Overview
Problem
Existing methods for determining digital resource access frequency lack privacy preservation and efficiency, leading to potential leakage of user information.
Innovation Solution
A system that generates a tree model from access data, assigns private access values to nodes, and uses iterative classification to produce accurate and privacy-sensitive estimates of user access frequency, minimizing computational resources and preserving user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to determine digital resource access frequency, then computational efficiency can be maintained, but user privacy is compromised and information leakage occurs
Solution Approach 1:
The patent segments the access frequency data into multiple bins or ranges, where each bin represents a frequency interval. Instead of storing exact access counts, the system categorizes users into bins based on their access frequency, thereby preserving privacy while maintaining useful statistical information for resource management decisions.
Solution Approach 2:
The patent introduces an intermediary mechanism (such as a trusted execution environment or privacy-preserving computation layer) that allows the system to process access frequency data without exposing individual user information. This intermediary enables the computation of aggregate statistics while preventing direct observation of individual access patterns.
2Measurement precision
If exact access frequency data is collected and stored, then measurement precision is improved, but computational resources and storage requirements increase
Solution Approach 1:
The patent changes the parameter representation from exact integer counts to binned or rounded frequency ranges. This parameter transformation reduces the precision requirement while maintaining sufficient accuracy for resource management purposes, thereby decreasing computational and storage complexity.
Solution Approach 2:
The patent employs lightweight, approximate data structures that can be efficiently created and discarded, rather than maintaining complex, precise records of individual access patterns. This approach uses simpler, less resource-intensive representations that suffice for the intended analytical purposes.
3Measurement precision
If detailed access data is processed, then estimation accuracy is improved, but privacy preservation is compromised
Solution Approach 1:
By segmenting access frequency into bins, the patent achieves a balance where aggregate estimation accuracy is maintained for resource management while individual user privacy is protected through the aggregation process that masks individual contribution details.
Solution Approach 2:
The patent applies different levels of data detail to different purposes: aggregated binned data is used for privacy-preserving analysis and estimation, while more detailed data is only retained where absolutely necessary and protected through appropriate security measures.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an estimate for a number of users that accessed a digital resource within a time window. In one aspect, a method comprises: obtaining access data for a digital resource; generating a tree model based on the access data; selecting, for each node in the tree model, a respective private access value for the node that: (i) is an approximation of an access value for the node, and (ii) is selected from a finite set of possible private access values; and generating an estimate for the number of users that accessed the digital resource at least the predefined number of times within the time window based on private access values associated with one or more nodes in the tree model.