Privacy-Preserving Hidden State Estimation via Data Permutation
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Solution Overview
Problem
Existing methods for determining hidden states of a system from noisy data face challenges due to information and capability asymmetries between clients and servers, while also needing to address privacy concerns for both parties.
Innovation Solution
A method using a hidden Markov model where the server assists the client in estimating underlying system states without needing explicit state transition distribution knowledge, and the client partially conceals data and server knowledge to maintain privacy, with data permutation and chaff insertion for privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the server assists the client in estimating hidden states using a two-party coordination system, then the classification accuracy and computational capability are improved, but privacy concerns for both parties are exacerbated
Solution Approach 1:
The patent introduces a third-party mediator (the server) that facilitates hidden state estimation between the client and the classification system. The server acts as an intermediary that processes data without directly accessing private information, enabling accurate estimation while maintaining privacy through controlled information flow and cryptographic techniques.
Solution Approach 2:
The system segments the classification task into multiple parts: the client provides noisy observations and partial model knowledge, the server provides computational resources and additional model parameters, and they coordinate through encrypted communications. This segmentation allows each party to contribute what they have while protecting what they need to protect.
2Reliability
If the client shares noisy data with the server for joint classification, then the classification performance is improved through information asymmetry resolution, but the client's data privacy is compromised
Solution Approach 1:
The system creates encrypted copies of the data and model parameters that can be processed without revealing the original content. The server works with encrypted representations of the data, allowing classification performance to improve while the actual private data remains concealed and inaccessible to the server.
Solution Approach 2:
The patent transforms the data and model parameters into different mathematical representations (e.g., encrypted forms, transformed feature spaces) that preserve the necessary information for accurate classification while rendering the original private parameters inaccessible. This allows the server to assist with classification without accessing the client's original data.
3Measurement precision
If the server shares its exclusive knowledge of system parameters with the client, then the classification accuracy is improved, but the server's intellectual property and privacy are compromised
Solution Approach 1:
The server's exclusive knowledge of system parameters is transmitted through an intermediary encryption layer. The server shares encrypted model parameters that can be used for accurate classification without revealing the actual parameter values. This allows the client to benefit from the server's knowledge while the server's intellectual property remains protected.
Solution Approach 2:
The server creates encrypted copies of its model parameters that can be transmitted and used for classification without exposing the original parameters. The client receives these encrypted copies that preserve the necessary classification accuracy while the server's exclusive knowledge remains concealed.
4Power
If a two-party coordination system is implemented between client and server, then the classification capability is improved through resource sharing, but the system complexity and computational overhead increase
Solution Approach 1:
The server is designed as a multi-functional platform that handles encryption, model parameter storage, classification computation, and privacy preservation. By consolidating these functions into a single universal system, the patent reduces the overall complexity that would otherwise be distributed across multiple separate components.
Solution Approach 2:
The system incorporates self-service mechanisms where the client can independently manage their data, the server automatically processes classifications, and the system self-adjusts encryption and privacy parameters. This automation reduces the operational complexity of coordinating between two parties while maintaining enhanced computational capability.
Data Source
AI summary
A method classifies data to determine hidden states of a system, by first randomly permuting the data and inserting client to generate private data. A server classifies the private data according to a hidden Markov model (HMM) to obtain permuted noisy estimates of the states and the chaff, which are returned to the client. The client then removes the chaff, inverts the permuted noisy estimates to obtain unpermuted noisy estimates of the states.