Privacy Management for Infinite Data Streams
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current privacy-preserving streaming data analysis techniques are limited by their inability to provide accurate and stable results over long periods, often relying on simplified data streams and time-constrained methods, which fail to meet the needs of real-life data and privacy-conscious users.
Innovation Solution
A method and system that continuously anonymize streaming data from user devices or sensors, generating distorted summaries with provable privacy guarantees, allowing for arbitrary data structures and regular summary updates without time constraints, ensuring high-quality data sharing while protecting user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current privacy-preserving streaming data analysis techniques are used, then user privacy is protected, but the accuracy and stability of results deteriorate over long periods
Solution Approach 1:
The system pre-calculates and stores summary statistics (such as histograms, aggregates) at multiple granularities before queries are issued. These pre-computed summaries are maintained in a summary store with associated metadata including freshness indicators and coverage periods, enabling the system to quickly retrieve accurate results without real-time computation overhead.
Solution Approach 2:
The system dynamically adapts its query processing strategy based on the freshness of pre-computed summaries. When summaries are fresh and cover the required time period, the system serves queries directly from the summary store. When summaries become stale or insufficient, the system automatically triggers re-computation or falls back to processing raw streaming data, ensuring continuous accuracy.
2Productivity
If simplified data streams and time-constrained methods are used, then processing speed is improved, but the ability to meet real-life data needs deteriorates
Solution Approach 1:
The system segments the data stream processing into multiple independent components: raw data ingestion, summary computation, summary storage, and query serving. Each component operates independently with well-defined interfaces, allowing parallel processing and enabling the system to handle complex real-life data scenarios while maintaining high processing throughput through optimized pipeline operations.
3Reliability
If continuous anonymization of streaming data is implemented, then user privacy is protected, but data quality for analysis may deteriorate
Solution Approach 1:
The system replaces traditional mechanical anonymization techniques (such as simple aggregation or suppression) with a sophisticated summary-based approach that uses statistical representations (histograms, aggregate functions) to preserve privacy while maintaining analytical utility. The summary structures capture essential data patterns without exposing individual records, and the system tracks summary freshness to ensure analysis quality remains acceptable.
Data Source
Figure 1~2
Figure 3
Figure 4~5
AI summary
An apparatus, method, and computer readable medium for management of infinite data streams. The apparatus includes a memory that stores streaming data with a data set and a processor operably connected to the memory. The processor transforms the data set to a second data set. To transform the data set, the processor determines whether a difference level exceeds a threshold, andtransforms the data set by adding a noise when the difference level exceeds the threshold. When the difference level does not exceed the threshold, the processor determines whether a retroactive count is greater than a threshold, transforms the data set by adding a second noise when the retroactive count is greater than the threshold, and transforms the data set by adding a third noise when the retroactive count is not greater than the threshold. The processor transmits the second data set to a data processing system for further processing.