Tree-Based Digital Access Frequency Estimation with Privacy Protection
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Solution Overview
Problem
Existing methods for estimating digital resource access frequency lack efficiency and privacy preservation, often requiring significant computational resources and compromising accuracy for privacy protection.
Innovation Solution
A system utilizing tree models, such as 1D and 2D tree models, to process access data and generate privacy-preserving access frequency estimates by combining noise with node values, limiting the number of nodes used for estimation to enhance accuracy and reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to estimate digital resource access frequency, then computational resources can be fully utilized, but privacy preservation is compromised and computational efficiency decreases
Solution Approach 1:
The patent introduces a tree model as an intermediary data structure between raw access data and frequency estimates. This tree model aggregates access patterns in a privacy-preserving manner, allowing accurate frequency estimation without exposing individual user information. The tree structure serves as a mediator that transforms detailed access data into aggregated statistics while maintaining estimation accuracy.
Solution Approach 2:
The patent transforms the estimation problem by changing parameters from individual user access counts to aggregated tree node values representing frequency ranges. By organizing data into time intervals and frequency buckets within the tree structure, the system achieves accurate estimation while preventing information leakage about individual users.
2Measurement precision
If comprehensive access data is processed to ensure accuracy, then estimation precision improves, but computational resources increase significantly
Solution Approach 1:
The patent segments the access data processing into hierarchical tree structures with nodes representing different time intervals and frequency ranges. This segmentation allows the system to process data in manageable chunks rather than handling all raw access records directly, significantly improving computational efficiency while maintaining accuracy through the structured aggregation at each node.
Solution Approach 2:
The patent introduces dimensional organization by structuring data in a tree model with dimensions for time intervals and access frequency ranges. This dimensional transformation allows efficient querying and estimation by navigating the tree structure rather than processing all raw data, improving computational productivity while preserving estimation accuracy.
3Loss of information
If privacy preservation techniques are applied, then user information protection improves, but estimation accuracy may deteriorate
Solution Approach 1:
The patent applies local quality by maintaining different levels of detail at different nodes in the tree structure. Leaf nodes preserve fine-grained local access patterns for accurate estimation, while parent nodes provide aggregated privacy-protected summaries. This local differentiation allows the system to prevent information leakage at higher levels while maintaining estimation accuracy through detailed local data at leaf nodes.
Data Source
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AI summary
In one aspect, there is provided a method performed by one or more computers that includes: obtaining access data for a digital resource, access data including data identifying a set of users that accessed the digital resource at a time point, processing the access data to generate data defining a tree model, where each node in the tree model is associated with: (i) a key that specifies time intervals in the time span, and (ii) a value that is based on a respective number of users that satisfy a node-specific selection, receiving a request to determine a number of users that accessed the digital resource at least a predefined number of times within a time window, and in processing the tree model to generate an estimate for the number of users that accessed the digital resource at least the predefined number of times within the time window.