Privacy-Preserving AI Interoperability Across Heterogeneous Devices
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Solution Overview
Problem
Existing synchronization techniques for devices with varying operating systems and platforms face connectivity issues, compatibility problems, and privacy concerns, leading to incomplete data synchronization and inefficient device interoperability.
Innovation Solution
A device interoperability system that establishes connections with user devices, boots from storage, and operates using the devices' processing power, enabling secure data synchronization and compatibility across different OSes while preserving privacy through encryption and secure authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is synchronized across devices with different operating systems and platforms, then device interoperability is improved, but privacy concerns and security risks increase
Solution Approach 1:
The patent segments the data synchronization process into multiple components: local data processing on individual devices, aggregation of anonymized data subsets, and distributed model training. This segmentation allows interoperability benefits while minimizing privacy exposure by keeping sensitive data localized and only sharing processed insights.
Solution Approach 2:
The patent introduces an intermediary aggregation layer that receives data from multiple devices, processes it to remove identifying information, and distributes processed data subsets for model training. This intermediary acts as a buffer that enables cross-device learning while protecting individual device privacy through anonymization and controlled data sharing.
2Adaptability or versatility
If existing synchronization techniques are used across devices with varying operating systems, then connectivity is established, but compatibility problems and data corruption occur
Solution Approach 1:
The patent changes the parameters of data exchange by transforming raw device data into standardized, anonymized data subsets with controlled characteristics. This parameter transformation ensures compatibility across different operating systems while maintaining data integrity through standardized formats and validation protocols.
Solution Approach 2:
The patent creates a universal data aggregation and processing framework that can handle data from multiple operating systems and platforms simultaneously. This universal approach uses standardized protocols and formats that work across iOS, Android, and other platforms, eliminating compatibility issues while enabling broad device support.
3Measurement precision
If centralized data aggregation is performed for AI/ML model training, then model accuracy is improved, but privacy preservation and security are compromised
Solution Approach 1:
The patent segments the training data into multiple anonymized subsets distributed across different devices rather than aggregating all raw data centrally. Each device receives a subset of training data and contributes model parameters back to the central system, achieving accurate model training while keeping sensitive data localized and protected.
Solution Approach 2:
The patent inverts the traditional centralized aggregation approach by distributing training data subsets to devices and collecting model parameters instead of collecting raw data. This inversion achieves the same model training objectives while fundamentally protecting privacy by never centralizing sensitive user data.
Data Source
AI summary
A system for privacy preserving artificial intelligence (AI) comprising: a first and second device interoperability system (DIS) coupled to an artificial intelligence analysis subsystem (AIAS). The first and second DIS receive a first and second plurality of data sets from coupled first and second pluralities of user devices respectively. The first and second DIS create first and second aggregated data sets based on the received first and second pluralities of data sets. The first and second DIS determine first and second subsets of model parameters for an AI model based on the first and second aggregated data sets; then transmit first and second sets of information based on the model parameter subsets to the AIAS. The AIAS creates a set of model parameters for the AI model based on the transmitted sets of information, and transmits the set of model parameters to the first and second DIS for deployment.