Adaptive Privacy Budgeting for Accurate Interaction Prediction

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Solution Overview

Problem

Existing methods for tracking user interactions lack differential privacy, leading to potential breaches of user privacy when estimating interaction counts and contexts.

Innovation Solution

A method involving multiple iterations of adding noise to ground truth values to generate noisy datasets, processing these datasets to determine a predicted value within a threshold accuracy, and providing the result only when the standard deviation meets the threshold, thereby maintaining differential privacy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If noise is added to ground truth values to generate noisy datasets, then differential privacy is maintained, but measurement precision deteriorates

Engineering Contradiction:

Improvedifferential privacyVSAvoidaccuracy of interaction data

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The system performs multiple iterations of noise addition and processing before providing the final result. By pre-computing multiple noisy datasets and processing them through multiple iterations, the system accumulates sufficient accuracy information while maintaining privacy in each individual iteration, resolving the contradiction between privacy protection and measurement precision.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously processes noisy datasets through multiple iterations, where each iteration refines the predicted value. This continuous processing allows the system to gradually improve accuracy while maintaining differential privacy throughout the entire process, rather than requiring a single high-precision measurement that would compromise privacy.

Inventive Principle:

Principle #20Continuity of useful action

2Measurement precision

If multiple iterations of noise addition are performed to achieve target accuracy, then measurement precision is improved, but computational resources increase

Engineering Contradiction:

Improveaccuracy of predicted valueVSAvoidcomputational resources

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system incorporates feedback mechanisms where the standard deviation of noise-added values is calculated and compared against a threshold. Based on this feedback, the system determines whether to continue additional iterations or stop processing. This feedback loop efficiently allocates computational resources by stopping iterations once the target accuracy is achieved, avoiding unnecessary computational expenditure while ensuring sufficient precision.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12499255B2Adaptive privacy budgeting and adaptive sampling value prediction

Publication Date: 2025.12.16 GOOGLE LLC

AI summary

Systems and methods for generating and maintaining differential privacy while providing accurate values can include obtaining a plurality of noise-added values, processing the plurality of noise-added values to determine a predicted value. The plurality of noise-added value may be utilized to determine one or more accuracy values that can be compared to a threshold to determine if more data is to be obtained and processed before providing a predicted value.