Digital Resource Access Estimation via Privacy-Preserving Tree Aggregation
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Solution Overview
Problem
Existing methods for estimating digital resource access frequency lack privacy preservation, leading to potential leakage of user information, and often prioritize additive error reduction without considering multiplicative error.
Innovation Solution
A system that generates a tree model from access data, assigns private access values to nodes, and uses an iterative classification technique to produce privacy-sensitive and accurate estimates by minimizing both additive and multiplicative errors, while reducing computational resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional access frequency estimation methods are used, then measurement precision may be improved, but user privacy is compromised leading to information leakage
Solution Approach 1:
The patent introduces a tree model as an intermediary data structure between raw access data and estimation results. The tree model aggregates access patterns in a hierarchical manner, allowing frequency estimation while preventing direct observation of individual user access behavior. This intermediary structure enables measurement precision while protecting user privacy by design.
Solution Approach 2:
The patent transforms the estimation problem by changing parameters from individual user access counts to aggregated tree node values. By computing estimates based on tree model parameters rather than raw user data, the system achieves accurate frequency estimation while the parameter transformation inherently protects user privacy through aggregation.
2Measurement precision
If iterative classification technique with multiple iterations is applied, then measurement precision improves, but computational resource consumption increases
Solution Approach 1:
The patent segments the estimation problem into multiple iterations of classification, where each iteration processes a portion of the tree model. This segmentation allows the system to achieve high measurement precision through progressive refinement while managing computational resources by dividing the workload into manageable iterations rather than processing all data at once.
Solution Approach 2:
The iterative classification technique applies partial action by performing classification on subsets of data in each iteration rather than processing the entire dataset in a single pass. This approach achieves sufficient estimation accuracy through multiple partial classifications, reducing overall computational resource consumption compared to exhaustive single-pass methods.
Data Source
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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.