Distributed Synthetic Data Collaboration for Privacy-Preserving Analytics
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Solution Overview
Problem
Data collaboration systems face challenges in sharing high-quality data without compromising data security and privacy, leading to inaccurate and insufficient insights due to the lack of personally identifiable information, and exposure to data breaches in centralized setups.
Innovation Solution
A distributed secure data collaboration framework generates synthetic datasets using local generators and a central model, creating statistically representative data without exposing sensitive information, utilizing transformers and a private set intersection model to maintain privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collaboration systems share real data between organizations, then data insights and analytics quality improve, but data security and privacy are compromised
Solution Approach 1:
The system generates synthetic datasets that replicate the statistical properties and patterns of real datasets without containing actual sensitive information. The synthetic data is created by learning from real data through local generators and a central generator, producing statistically similar but fundamentally different data that preserves analytical value while eliminating privacy risks
Solution Approach 2:
The system introduces an intermediary processing layer where real data is first transformed into feature maps through local generators, then combined through a central generator to create synthetic data. This intermediary transformation process acts as a buffer that prevents direct exposure of sensitive information while maintaining data utility for collaboration
2Ease of operation
If centralized data processing is used, then data collaboration is simplified, but exposure to data breaches increases
Solution Approach 1:
The system divides the data processing task into segmented components: local generators at each organization's node that process data independently, and a central generator that coordinates synthesis. This segmentation ensures no single entity has access to all raw data, reducing breach impact while maintaining collaborative functionality
Solution Approach 2:
Each local generator is trained and operates with data specific to its organization, maintaining local data sovereignty and processing quality. The central generator then synthesizes results from these localized processing units, combining benefits of both centralized coordination and decentralized data handling
3Object-affected harmful factors
If synthetic data is generated from real data, then data privacy is protected, but data representativeness and quality may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the central generator evaluates and adjusts synthetic data generation based on statistical properties of the input data. Local generators receive feedback about data distributions and patterns, allowing them to refine their feature map generation to better preserve the statistical characteristics of the original datasets
Solution Approach 2:
The system transforms data through parameter changes by converting real data into feature maps with different representations, then generating synthetic data with adjusted parameters that capture essential statistical properties. This parameter transformation preserves representativeness while achieving privacy protection
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements a secure distributed data collaboration architecture for generating synthetic datasets. For example, the disclosed system sends a request to perform a data collaboration with a first dataset of a first local node and a second dataset of a second local node. The disclosed system receives intermediate feature maps from the local nodes that correspond with the datasets and generates a combined feature map. Further, the disclosed system generates a synthetic dataset from the combined feature map by utilizing a central generative model. Moreover, the synthetic dataset generated by the disclosed system is statistically representative of the first dataset and the second dataset.