Zero Knowledge Proofs for Smart Grid Privacy Verification
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Solution Overview
Problem
In smart grid systems, consumers' private data exposed through high-resolution smart-meter readings poses privacy concerns, as existing techniques for verifying demand response without collecting this data are often inefficient and insecure.
Innovation Solution
A method using zero knowledge proofs, where a computing device proves that private data meets a criterion without revealing the data, by transferring commitments and challenges, and evaluating encrypted predicate functions using garbled circuits and oblivious transfer protocols, ensuring the privacy of the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If service providers collect high-resolution smart-meter data to verify demand response compliance, then verification accuracy is improved, but consumer privacy is compromised
Solution Approach 1:
The patent introduces zero-knowledge proofs as an intermediary mechanism that enables verification of demand response compliance without exposing consumer data. The proof system acts as a mediator between the need for verification accuracy and the need for privacy protection, allowing service providers to confirm compliance while consumer-sensitive information remains encrypted and undisclosed.
Solution Approach 2:
The patent extracts only the necessary verification information from the complete smart-meter data set. Through selective disclosure and zero-knowledge proof mechanisms, the system separates the compliance verification aspect from the sensitive consumer behavior data, taking out only what is needed for verification while leaving the rest private.
2Object-affected harmful factors
If service providers do not collect high-resolution smart-meter data to protect consumer privacy, then privacy is preserved, but verification efficiency deteriorates
Solution Approach 1:
The patent replaces the traditional mechanical approach of data collection and analysis with cryptographic zero-knowledge proof mechanisms. Instead of physically collecting and processing smart-meter data to verify compliance, the system uses mathematical proofs that provide verification without data exposure, thereby maintaining privacy while improving verification efficiency.
Solution Approach 2:
The patent implements preliminary action by having consumers generate and submit zero-knowledge proofs before service providers need verification. This advance preparation of cryptographic evidence eliminates the need for subsequent data collection and analysis, significantly improving verification efficiency while maintaining privacy protection.
3Object-affected harmful factors
If existing techniques are used to verify demand response without collecting smart-meter data, then privacy is partially protected, but security and reliability are compromised
Solution Approach 1:
The patent employs a composite cryptographic approach combining multiple security mechanisms: commitment schemes, zero-knowledge proofs, and cryptographic hash functions. This composite structure provides robust security and reliability that surpasses individual techniques, ensuring both privacy protection and verification integrity through the synergistic combination of cryptographic primitives.
Data Source
AI summary
A first computing device transfers first and second commitments to a second computing device. The first commitment is for private data and a first random number and the second commitment is for second and third random numbers. The method includes producing a result by evaluating a predicate and a first support function of a garbled circuit. The result includes a first part and a second part, referred to as the predicate result. The method includes transferring the result to the second computing device; receiving a second challenge from the second computing device; and producing a second response from a second support function using the second challenge. The method includes transferring the second response to the second computing device. The predicate result over the private data is true if a result of a check function is equal to a third commitment of the first part and the second response.