With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every day.How to securely share data between the IoV operator and various value-added service providers becomes one of the...With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every day.How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues.Due to its flexible and efficient fine-grained access control feature,Ciphertext-Policy Attribute-Based Encryption(CP-ABE)is suitable for data sharing in IoV.However,there are many flaws in most existing CP-ABE schemes,such as attribute privacy leakage and key misuse.This paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP).A partially hidden access structure is adopted to hide sensitive user attribute values,and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure.In addition,key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse.Since the main computation task is outsourced to the cloud,the burden of the user side is relatively low.Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.展开更多
With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machi...With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.展开更多
Nowadays, mobile agents are an effective paradigm for accessing the information in distributed applications, especially in a dynamic network environment such as Internet businesses. In such kind of Internet based appl...Nowadays, mobile agents are an effective paradigm for accessing the information in distributed applications, especially in a dynamic network environment such as Internet businesses. In such kind of Internet based applications, access must be secure and authentication takes a vital role to avoid malicious use of the system. This kind of security has been provided by several previously proposed algorithms based on RSA digital signature cryptography. However, the computational time for performing encryption and decryption operations in the past literatures is very high. In this paper, we propose an anonymous authentication scheme which potentially reduces the overall computation time needed for verifying the legitimacy of the users. Comparing with previous anonymous authentication schemes, our proposed scheme provides more security and it is effective in terms of computation cost. The experimental results show that the proposed method authenticates the users with low computational time significantly.展开更多
基金supported by the National Natural Science Foundation of China(No.62272076)。
文摘With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every day.How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues.Due to its flexible and efficient fine-grained access control feature,Ciphertext-Policy Attribute-Based Encryption(CP-ABE)is suitable for data sharing in IoV.However,there are many flaws in most existing CP-ABE schemes,such as attribute privacy leakage and key misuse.This paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP).A partially hidden access structure is adopted to hide sensitive user attribute values,and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure.In addition,key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse.Since the main computation task is outsourced to the cloud,the burden of the user side is relatively low.Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.
基金supported by the National Natural Science Foundation of China under Grants No.U1836115,No.61922045,No.61877034,No.61772280the Natural Science Foundation of Jiangsu Province under Grant No.BK20181408+2 种基金the Peng Cheng Laboratory Project of Guangdong Province PCL2018KP004the CICAEET fundthe PAPD fund.
文摘With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.
文摘Nowadays, mobile agents are an effective paradigm for accessing the information in distributed applications, especially in a dynamic network environment such as Internet businesses. In such kind of Internet based applications, access must be secure and authentication takes a vital role to avoid malicious use of the system. This kind of security has been provided by several previously proposed algorithms based on RSA digital signature cryptography. However, the computational time for performing encryption and decryption operations in the past literatures is very high. In this paper, we propose an anonymous authentication scheme which potentially reduces the overall computation time needed for verifying the legitimacy of the users. Comparing with previous anonymous authentication schemes, our proposed scheme provides more security and it is effective in terms of computation cost. The experimental results show that the proposed method authenticates the users with low computational time significantly.
