Yuancheng Li* and Jiawen Yu Pages 564 - 574 ( 11 )
Background: In the power Internet of Things (IoT), power consumption data faces the risk of privacy leakage. Traditional privacy-preserving schemes cannot ensure data privacy on the system, as the secret key pairs shall be shared between all the interior nodes once leaked. In addition, the general schemes only support summation algorithms, resulting in a lack of extensibility.
Objective: To preserve the privacy of power consumption data, ensure the privacy of secret keys, and support multiple data processing methods we propose an improved power consumption data privacypreserving scheme.
Method: Firstly, we have established a power IoT architecture based on edge computing. Then the data is encrypted with the multi-key fully homomorphic algorithm to realize the operation of ciphertext, without the restrictions of calculation type. Through the improved decryption algorithm, ciphertext that can be separately decrypted in cloud nodes is generated, which contributes to reducing communication costs and preventing data leakage.
Results: The experimental results show that our scheme is more efficient than traditional schemes in privacy preservation. According to the variance calculation result, the proposed scheme has reached the application standard in terms of computational cost and is feasible for practical operation.
Discussion: In the future, we plan to adopt a secure multi-party computation based scheme so that data can be managed locally with homomorphic encryption, so as to ensure data privacy.
Conclusion: A privacy-preserving scheme based on improved multi-key fully homomorphic encryption is proposed for the power consumption data, and the experimental results demonstrate the effectiveness and advantage of the proposed scheme.
power Internet of Things, power consumption data, improved multi-key fully homomorphic encryption, privacypreserving, edge computing, re-encryption.
School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Huilongguan Town, Changping District, Beijing 102206, School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Huilongguan Town, Changping District, Beijing 102206