As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and use...As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.展开更多
AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions ...AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development.However,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI applications.Therefore,it is essential to verify the integrity of e-commerce data.Although existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in PDP.To address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded data.Additionally,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its practicality.Finally,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.展开更多
基金supported by the National Key R&D Program of China(No.2023YFB2703700)the National Natural Science Foundation of China(Nos.U21A20465,62302457,62402444,62172292)+4 种基金the Fundamental Research Funds of Zhejiang Sci-Tech University(Nos.23222092-Y,22222266-Y)the Program for Leading Innovative Research Team of Zhejiang Province(No.2023R01001)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ24F020008,LQ24F020012)the Foundation of State Key Laboratory of Public Big Data(No.[2022]417)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01119).
文摘As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
基金funded by the Taiwan Comprehensive University System and the National Science and Technology Council of Taiwan under grant number NSTC 111-2410-H-019-006-MY3Additionally,this work was financially/partially supported by the Advanced Institute of Manufacturing with High-tech Innovations(AIM-HI)from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan+1 种基金the National Natural Science Foundation of China,No.62402444the Zhejiang Provincial Natural Science Foundation of China,No.LQ24F020012.
文摘AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development.However,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI applications.Therefore,it is essential to verify the integrity of e-commerce data.Although existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in PDP.To address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded data.Additionally,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its practicality.Finally,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.
