The combination of blockchain and Internet of Things technology has made significant progress in smart agriculture,which provides substantial support for data sharing and data privacy protection.Nevertheless,achieving...The combination of blockchain and Internet of Things technology has made significant progress in smart agriculture,which provides substantial support for data sharing and data privacy protection.Nevertheless,achieving efficient interactivity and privacy protection of agricultural data remains a crucial issues.To address the above problems,we propose a blockchain-assisted federated learningdriven support vector machine(BAFL-SVM)framework to realize efficient data sharing and privacy protection.The BAFL-SVM is composed of the FedSVM-RiceCare module and the FedPrivChain module.Specifically,in FedSVM-RiceCare,we utilize federated learning and SVM to train the model,improving the accuracy of the experiment.Then,in FedPrivChain,we adopt homomorphic encryption and a secret-sharing scheme to encrypt the local model parameters and upload them.Finally,we conduct a large number of experiments on a real-world dataset of rice pests and diseases,and the experimental results show that our framework not only guarantees the secure sharing of data but also achieves a higher recognition accuracy compared with other schemes.展开更多
Large Language Models(LLMs)are complex artificial intelligence systems,which can understand,generate,and translate human languages.By analyzing large amounts of textual data,these models learn language patterns to per...Large Language Models(LLMs)are complex artificial intelligence systems,which can understand,generate,and translate human languages.By analyzing large amounts of textual data,these models learn language patterns to perform tasks such as writing,conversation,and summarization.Agents built on LLMs(LLM agents)further extend these capabilities,allowing them to process user interactions and perform complex operations in diverse task environments.However,during the processing and generation of massive data,LLMs and LLM agents pose a risk of sensitive information leakage,potentially threatening data privacy.This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding.Specifically,we conduct an in-depth survey about privacy threats,encompassing passive privacy leakage and active privacy attacks.Subsequently,we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness.Finally,we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.展开更多
In the Internet of Things(IoT),a large number of devices are connected using a variety of communication technologies to ensure that they can communicate both physically and over the network.However,devices face the ch...In the Internet of Things(IoT),a large number of devices are connected using a variety of communication technologies to ensure that they can communicate both physically and over the network.However,devices face the challenge of a single point of failure,a malicious user may forge device identity to gain access and jeopardize system security.In addition,devices collect and transmit sensitive data,and the data can be accessed or stolen by unauthorized user,leading to privacy breaches,which posed a significant risk to both the confidentiality of user information and the protection of device integrity.Therefore,in order to solve the above problems and realize the secure transmission of data,this paper proposed EBIAS,a secure and efficient blockchain-based identity authentication scheme designed for IoT devices.First,EBIAS combined the Elliptic Curve Cryptography(ECC)algorithm and the SHA-256 algorithm to achieve encrypted communication of the sensitive data.Second,EBIAS integrated blockchain to tackle the single point of failure and ensure the integrity of the sensitive data.Finally,we performed security analysis and conducted sufficient experiment.The analysis and experimental results demonstrate that EBIAS has certain improvements on security and performance compared with the previous schemes,which further proves the feasibility and effectiveness of EBIAS.展开更多
基金supported by the National Natural Science Foundation of China(62272256,62202250)the Major Program of Shandong Provincial Natural Science Foundation for the Fundamental Research(ZR2022ZD03)+3 种基金the National Science Foundation of Shandong Province(ZR2021QF079)the Talent Cultivation Promotion Program of Computer Science and Technology in Qilu University of Technology(Shandong Academy of Sciences)(2023PY059)the Pilot Project for Integrated Innovation of Science,Education and Industry of Qilu University of Technology(Shandong Academy of Sciences)(2022XD001)the Colleges and Universities 20 Terms Foundation of Jinan City(202228093).
文摘The combination of blockchain and Internet of Things technology has made significant progress in smart agriculture,which provides substantial support for data sharing and data privacy protection.Nevertheless,achieving efficient interactivity and privacy protection of agricultural data remains a crucial issues.To address the above problems,we propose a blockchain-assisted federated learningdriven support vector machine(BAFL-SVM)framework to realize efficient data sharing and privacy protection.The BAFL-SVM is composed of the FedSVM-RiceCare module and the FedPrivChain module.Specifically,in FedSVM-RiceCare,we utilize federated learning and SVM to train the model,improving the accuracy of the experiment.Then,in FedPrivChain,we adopt homomorphic encryption and a secret-sharing scheme to encrypt the local model parameters and upload them.Finally,we conduct a large number of experiments on a real-world dataset of rice pests and diseases,and the experimental results show that our framework not only guarantees the secure sharing of data but also achieves a higher recognition accuracy compared with other schemes.
基金supported in part by the National Natural Science Foundation of China(62402288 and 62302063)the China Postdoctoral Science Foundation,China(2024M751811).
文摘Large Language Models(LLMs)are complex artificial intelligence systems,which can understand,generate,and translate human languages.By analyzing large amounts of textual data,these models learn language patterns to perform tasks such as writing,conversation,and summarization.Agents built on LLMs(LLM agents)further extend these capabilities,allowing them to process user interactions and perform complex operations in diverse task environments.However,during the processing and generation of massive data,LLMs and LLM agents pose a risk of sensitive information leakage,potentially threatening data privacy.This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding.Specifically,we conduct an in-depth survey about privacy threats,encompassing passive privacy leakage and active privacy attacks.Subsequently,we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness.Finally,we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.
基金supported by the National Science Foundation of China(62272256,62202250)the Major Program of Shandong Provincial Natural Science Foundation for the Fundamental Research(ZR2022ZD03)+3 种基金the National Science Foundation of Shandong Province(ZR2021QF079)the Talent Cultivation Promotion Program of Computer Science and Technology in Qilu University of Technology(Shandong Academy of Sciences)(2023PY059)the Pilot Project for Integrated Innovation of Science,Education and Industry of Qilu University of Technology(Shandong Academy of Sciences)(2022XD001)the Colleges and Universities 20 Terms Foundation of Jinan City(202228093).
文摘In the Internet of Things(IoT),a large number of devices are connected using a variety of communication technologies to ensure that they can communicate both physically and over the network.However,devices face the challenge of a single point of failure,a malicious user may forge device identity to gain access and jeopardize system security.In addition,devices collect and transmit sensitive data,and the data can be accessed or stolen by unauthorized user,leading to privacy breaches,which posed a significant risk to both the confidentiality of user information and the protection of device integrity.Therefore,in order to solve the above problems and realize the secure transmission of data,this paper proposed EBIAS,a secure and efficient blockchain-based identity authentication scheme designed for IoT devices.First,EBIAS combined the Elliptic Curve Cryptography(ECC)algorithm and the SHA-256 algorithm to achieve encrypted communication of the sensitive data.Second,EBIAS integrated blockchain to tackle the single point of failure and ensure the integrity of the sensitive data.Finally,we performed security analysis and conducted sufficient experiment.The analysis and experimental results demonstrate that EBIAS has certain improvements on security and performance compared with the previous schemes,which further proves the feasibility and effectiveness of EBIAS.
