Differential Privacy Noise Allocation for User Metric Aggregation
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Solution Overview
Problem
Existing systems fail to effectively protect user metrics privacy while maintaining accurate aggregate metrics, as managers can infer individual user metrics by filtering data based on user profiles, leading to potential reverse-computation of metric values.
Innovation Solution
A method that generates and applies random noise to user metrics data, ensuring privacy by adding noise at the individual metric level, using a distribution like Laplace distribution to maintain accuracy and consistency during aggregation and drill-down operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If aggregate metrics data is collected and made accessible for analysis, then productivity and insights are improved, but user privacy is compromised as managers can filter and infer individual user metrics
Solution Approach 1:
The patent introduces noise as an intermediary element between the original metric data and the reported data. This noise acts as a mediator that prevents direct inference of individual user metrics while preserving aggregate statistical properties. The noise is carefully calibrated to provide differential privacy guarantees while maintaining data utility for aggregate analysis.
Solution Approach 2:
The patent transforms the original metric data by adding randomized noise values with specific distribution characteristics (e.g., Laplace distribution). This parameter change modifies the data representation to hide individual values while preserving aggregate properties, enabling productivity analysis without exposing individual user information.
2Object-affected harmful factors
If noise is added to metric data to protect privacy, then user privacy is improved, but measurement precision deteriorates due to data distortion
Solution Approach 1:
The patent carefully controls the parameters of the added noise, such as using Laplace distribution with specifically calibrated scale parameters. This allows the noise to provide sufficient privacy protection while minimizing distortion to aggregate metrics. The noise parameters are tuned to achieve the desired balance between privacy and accuracy.
Solution Approach 2:
The patent applies noise at the individual metric level rather than aggregating first, which is a form of partial action. This granular approach allows for more precise control over the privacy-accuracy tradeoff, as the noise added to each individual metric can be carefully managed to preserve aggregate properties while protecting individual values.
3Object-affected harmful factors
If noise is applied at the individual metric level, then privacy protection is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent segments the noise generation and application process into distinct, modular steps: generating noise values, distributing them to specific metrics, and applying them individually. This segmentation makes the complex process more manageable and implementable, breaking down the overall complexity into smaller, well-defined operations.
Solution Approach 2:
The patent uses well-established probability distributions (such as Laplace distribution) with standardized parameters for noise generation. By relying on known mathematical models with defined parameters, the system avoids the need to develop complex custom noise generation algorithms, thereby reducing overall system complexity while maintaining effective privacy protection.
Data Source
AI summary
A system and method for applying noise to data is described. The system accesses a metric value of a metric of each user from a group of users of an application. The metric indicates a measure of an operation of the application by a corresponding user. The system generates noise values and defines a distribution of the noise values to the group of users. The system modifies the metric value of the metric of each user with a corresponding noise value from the noise values based on the distribution.