Abstract
With the intelligentization of the Internet of Vehicles(IoVs),Artificial Intelligence(AI)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 communi-cation 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 perfor-mance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.
基金项目
国家自然科学基金(U1836115)
国家自然科学基金(61922045)
国家自然科学基金(61877034)
国家自然科学基金(61772280)
江苏省自然科学基金(BK20181408)
Peng Cheng Laboratory Project of Guangdong Province(PCL2018KP004)
CICAEET fund()
PAPD fund()
NETL
NSTL
万方数据