Homomorphic Encryption for Secure Fleet Data Aggregation
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Solution Overview
Problem
Fleet management systems face challenges in securely aggregating data from multiple untrusted users while ensuring privacy and integrity, as existing solutions either compromise on security or have high computational overhead, especially when customers encrypt their data with private keys and service providers need to derive insights without learning fine-grained information.
Innovation Solution
A method and system utilizing additive homomorphic encryption and stateful encryption schemes to enable secure aggregation of performance indicators on a cloud platform, where the service provider can only decrypt aggregate values and not individual data items, ensuring that neither the computation cluster nor the service provider learns sensitive information, with verification mechanisms to ensure correctness and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored and processed on an untrusted third-party platform, then remote storage and analysis capabilities are improved, but security and privacy of individual data items deteriorate
Solution Approach 1:
The encryption key is segmented into two parts: a first key component held by the user and a second key component held by the service provider. This segmentation allows the service provider to process encrypted data without having access to the full decryption key, thus maintaining security while enabling remote processing capabilities.
Solution Approach 2:
Homomorphic encryption acts as an intermediary mechanism that allows computations to be performed on encrypted data without decryption. The service provider can process data in encrypted form, and results remain encrypted until decrypted by the user, thus enabling secure remote processing.
2Loss of information
If service providers can compute aggregate functions on encrypted data, then actionable insights are improved, but computational overhead and system complexity worsen
Solution Approach 1:
Different operations are optimized for different locations in the system. The service provider performs efficient aggregation operations on encrypted data using homomorphic properties, while the user performs the more complex decryption operation locally on their own data, distributing computational burden according to local capabilities and needs.
Solution Approach 2:
Data is pre-encrypted using homomorphic encryption before being sent to the service provider. This preliminary encryption enables the service provider to perform aggregate computations directly on the encrypted data without needing to decrypt it first, reducing their computational overhead while preserving privacy.
3Reliability
If customers encrypt their data with their own private keys, then privacy protection is improved, but the ability of service providers to derive insights without learning fine-grained information worsens
Solution Approach 1:
The system uses asymmetric encryption where the user's public key is used to encrypt data, but only the user's private key can decrypt it. The service provider has no capability to decrypt the data, creating an asymmetric relationship where privacy is strongly protected while the service provider can still perform aggregate computations on the encrypted data using homomorphic properties.
Solution Approach 2:
The traditional mechanical approach of decrypting data for processing is replaced with homomorphic encryption, which allows mathematical operations to be performed directly on encrypted data. This substitution eliminates the need for the service provider to learn fine-grained information while still enabling them to derive aggregate insights.
Data Source
Figure 1
AI summary
The invention relates to a method for aggregation of a performance indicator of a device comprising the steps of: concatenating a respective first data item to a plurality of second data items in the device; encrypting the plurality of concatenated second data items relevant for computing the performance indicator using a first encryption key in the device, wherein the first encryption key is based on an additive homomorphic encryption scheme; sending the encrypted concatenated second data items to a computation cluster; computing the performance indicator on the computation cluster using the encrypted concatenated second data items and computing an aggregate value regarding the performance indicator by summing up the encrypted concatenated second data items; sending the aggregate value to a server of a service provider of the device; decrypting the aggregate value using a second encryption key on the server of the service provider; and verifying the decrypted result by checking whether the decrypted sum computed by summing up the encrypted concatenated second data items comprises a predetermined value. The present invention also relates to a corresponding system and corresponding computer program product comprising one or more computer readable media having computer executable instructions for performing the steps of the method.