Event Processing System with Differential Privacy Controls
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Solution Overview
Problem
Existing systems face challenges in processing user events to extract aggregate information while ensuring user privacy, particularly in social media platforms, as they struggle to prevent identification of individual users and inference of personal data from released information.
Innovation Solution
The method involves processing user events to generate exact counts with introduced artificial errors, applying quantization, and setting thresholds to ensure that information released is anonymized and cannot be attributed to individual users, using techniques like exact counting procedures, error introduction, and variable gating thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact counting procedures are used to compute precise aggregate information, then measurement precision is improved, but user privacy protection deteriorates as individual users may be identified from the released information
Solution Approach 1:
The patent introduces an intermediary mechanism (privacy budget and differential privacy framework) between the exact counting procedure and the released aggregate information. This intermediary adds controlled noise to the counting results, preventing direct identification of individual users while maintaining useful aggregate statistics. The privacy budget parameterizes the trade-off between precision and privacy protection.
Solution Approach 2:
The patent changes the parameter of the counting result by adding differential privacy noise with a specific variance based on the privacy budget. This parameter transformation converts exact counts into differentially private counts, systematically trading precision for privacy protection while maintaining statistical utility for aggregate analysis.
2Loss of information
If aggregate information is released with high precision, then information value is improved, but the risk of inferring individual user data increases
Solution Approach 1:
The patent converts the harmful effect of precise information (which enables inference attacks) into a benefit by deliberately adding noise. The added noise, while reducing precision, actually protects against inference attacks by making it computationally infeasible to determine individual user participation. The harm of lost precision is offset by the benefit of enhanced privacy protection.
Solution Approach 2:
The patent applies beforehand cushioning by pre-establishing a privacy budget and adding differential privacy noise before releasing aggregate information. This proactive measure cushions against potential inference attacks by ensuring that even with access to multiple aggregate queries, adversaries cannot reconstruct individual user data.
3Quantity of substance
If all user events are individually counted and released, then data completeness is improved, but privacy protection deteriorates as it enables identification of individual users
Solution Approach 1:
The patent extracts only the necessary aggregate information from the complete user event data, applying differential privacy to the extraction process. Instead of releasing all individual user events, it extracts aggregated counts with added noise, removing the ability to identify individual users while preserving the essential statistical properties for analysis.
4Ease of operation
If no privacy controls are applied to aggregate information, then ease of operation is improved, but security against privacy attacks deteriorates
Solution Approach 1:
The patent implements self-service privacy protection where the system automatically applies differential privacy mechanisms to aggregate information without requiring manual intervention for each query. The privacy budget is automatically managed and enforced, providing security against privacy attacks while maintaining ease of operation through automated privacy-preserving processing.
Data Source
AI summary
User events of a platform are processed to extract aggregate information about users of the platform at an event processing system. A query relating to the user events is received at the system and at least one query parameter is determined from the query. Various privacy controls are disclosed for ensuring that any information released in response to the query cannot be used to identify users individually or to infer information about individual users.