Spectrometer Model Building With Homomorphic Encryption
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Solution Overview
Problem
Creating models for spectroscopic instruments like spectrometers and spectroscopes requires detailed, hands-on work with sensitive customer data, posing privacy concerns and making it difficult to offer modeling as a service without compromising customer information.
Innovation Solution
A method using homomorphic encryption to encrypt reference spectra and parameters, allowing analysis without decoding, enabling model creation and evaluation by a second instance while preserving customer data privacy, utilizing AI or machine learning algorithms to select, adjust, or recreate models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed hands-on work with customer data is performed to create models, then model accuracy and customization are improved, but customer data privacy is compromised
Solution Approach 1:
The patent introduces an intermediary encryption layer between the customer's spectral data and the model creation process. A third party (encryption service) transforms the spectral data into encrypted form before it reaches the model builder, allowing analysis to proceed without exposing the actual spectrum. This intermediary encryption mechanism enables model accuracy to be maintained while customer data privacy is preserved, as the model builder works with encrypted representations rather than raw customer data.
Solution Approach 2:
The patent creates a copy of the spectral data in encrypted form that preserves the necessary mathematical properties for model training while hiding the original data content. The encrypted spectrum maintains the structural information needed for analysis but cannot be directly interpreted, effectively creating a functional copy that protects privacy. This allows the model to be trained on spectral characteristics without exposing the actual customer spectra.
2Loss of information
If encryption methods are applied to protect customer data, then data privacy is improved, but the complexity of the modeling process increases
Solution Approach 1:
The patent replaces complex manual encryption and data handling procedures with automated cryptographic algorithms. Instead of requiring manual security measures that increase operational complexity, the system uses standardized encryption protocols that automatically protect data while maintaining modeling capabilities. The encryption mechanism is integrated into the data flow, eliminating the need for separate complex security protocols and simplifying the overall modeling process while preserving privacy.
3Ease of manufacture
If customer data is disclosed to enable model creation, then modeling service can be provided, but trade secrets and sensitive information are revealed
Solution Approach 1:
The patent uses encryption as an intermediary layer that allows modeling services to be provided without disclosing customer trade secrets. The encrypted spectral data enables the model builder to perform analysis while the encryption prevents any potential leakage of sensitive information. This intermediary mechanism facilitates service provision while maintaining complete data security, allowing companies to outsource modeling without risking their trade secrets.
Solution Approach 2:
The patent creates encrypted copies of spectral data that enable modeling services to be performed without revealing the original data content. These encrypted copies preserve the mathematical relationships needed for accurate modeling while making the actual spectral information unintelligible to anyone without the decryption key. This allows comprehensive modeling services to be provided while completely protecting customer trade secrets and sensitive information.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables secure model creation and evaluation for spectroscopic instruments without revealing sensitive information, ensuring data privacy and facilitating service provision.
Implementation Method 1
encrypting the spectrum and/or the parameters by means of a homomorphic encryption method
Data Source
AI summary
A method for obtaining a model for a spectrometer or spectroscope, wherein a model comprises a relationship between a spectrum of a gas mixture or gas and parameters of the gas mixture or gas includes acquiring a reference spectrum of a predetermined gas or gas mixture using the spectrometer or spectroscope and parameters of the predetermined gas or gas mixture, encrypting the spectrum and/or the parameters using a homomorphic encryption method, and analyzing the spectrum and parameters without decoding the spectrum, or parameters. The method also includes performing one or more of the following steps with respect to a model based on the analysis, wherein a model contains a relationship between the spectrum and the parameters of the gas mixture: selection of a model from a large number of models created in advance, adjusting a previously created model, and recreating a model.