With the arrival of the era of big data,the audit thinking mode has been promoted to change.Under the influence of big data,audit will become an activity of continuous behavio Through cloud data,the staff can control ...With the arrival of the era of big data,the audit thinking mode has been promoted to change.Under the influence of big data,audit will become an activity of continuous behavio Through cloud data,the staff can control the operation status and risk assessment of the whole enterprise,timely analyze,control and respond to risks,and protect the enterprise to reduce risks.With the advent of the era of big data,audit data analysis is becoming more and more important.At the same time,a large amount of data analysis also brings challenges to auditors.Methods to deal and solve the challenges has become an urgent problem to be solved at present.This paper mainly studies the challenges and countermeasures brought by the changes of audit approaches and methods to audit data analysis under the background of big data,so as to continuously innovate and practice the improvement of audit technology and promote the healthy and rapid development of social economy.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
文章提出了一种基于多特征要素的网络安全审计中的特征数据关联方法。该方法以国际移动设备识别码(International Mobile Equipment Identity,IMEI)、国际移动用户识别码(International Mobile Subscriber Identification,IMSI)、移动终...文章提出了一种基于多特征要素的网络安全审计中的特征数据关联方法。该方法以国际移动设备识别码(International Mobile Equipment Identity,IMEI)、国际移动用户识别码(International Mobile Subscriber Identification,IMSI)、移动终端MAC(TERMINAL_MAC)地址三个特征要素为关联因子,通过持续更新完善特征信息串的方式,有效解决了在接入网络的移动终端MAC地址可能发生周期变化的情况下,构建移动终端用户唯一虚拟画像的问题。展开更多
现有区块链内容监管方案均采用事后治理方式,缺乏事前审计,且存在签名失效和多版本区块验证效率低的问题。针对这些问题,首先,设计了一种可动态调整可追责的数据审计方法,实现了对区块链交易数据的事前审计;其次,设计了一种编辑可控的...现有区块链内容监管方案均采用事后治理方式,缺乏事前审计,且存在签名失效和多版本区块验证效率低的问题。针对这些问题,首先,设计了一种可动态调整可追责的数据审计方法,实现了对区块链交易数据的事前审计;其次,设计了一种编辑可控的数字签名方案RCDSS(redaction-controlled digital signature scheme),解决了因编辑操作造成的签名失效问题;最后,设计了一种区块链数据一致性验证协议,实现了对查询结果的高效验证。安全分析和性能测试结果表明了其安全性和有效性。该方案在实现监管可控的情况下,仍然保持了较高的区块生成和验证效率,为区块链内容监管提供了一种新的解决思路。展开更多
基金Key Major of Audit Science in quality Engineering Project of Private Universities in 2020(Grant No.:HS2020ZLGC06)Supervisor System Research Project of Huashang College of Guangdong University of Finance and Economics in 2018(Grant No.:2018HSDS03)University Quality Engineering of Huashang College in 2021(Grant No.:HS2021ZLGC19)。
文摘With the arrival of the era of big data,the audit thinking mode has been promoted to change.Under the influence of big data,audit will become an activity of continuous behavio Through cloud data,the staff can control the operation status and risk assessment of the whole enterprise,timely analyze,control and respond to risks,and protect the enterprise to reduce risks.With the advent of the era of big data,audit data analysis is becoming more and more important.At the same time,a large amount of data analysis also brings challenges to auditors.Methods to deal and solve the challenges has become an urgent problem to be solved at present.This paper mainly studies the challenges and countermeasures brought by the changes of audit approaches and methods to audit data analysis under the background of big data,so as to continuously innovate and practice the improvement of audit technology and promote the healthy and rapid development of social economy.
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.
文摘文章提出了一种基于多特征要素的网络安全审计中的特征数据关联方法。该方法以国际移动设备识别码(International Mobile Equipment Identity,IMEI)、国际移动用户识别码(International Mobile Subscriber Identification,IMSI)、移动终端MAC(TERMINAL_MAC)地址三个特征要素为关联因子,通过持续更新完善特征信息串的方式,有效解决了在接入网络的移动终端MAC地址可能发生周期变化的情况下,构建移动终端用户唯一虚拟画像的问题。
文摘现有区块链内容监管方案均采用事后治理方式,缺乏事前审计,且存在签名失效和多版本区块验证效率低的问题。针对这些问题,首先,设计了一种可动态调整可追责的数据审计方法,实现了对区块链交易数据的事前审计;其次,设计了一种编辑可控的数字签名方案RCDSS(redaction-controlled digital signature scheme),解决了因编辑操作造成的签名失效问题;最后,设计了一种区块链数据一致性验证协议,实现了对查询结果的高效验证。安全分析和性能测试结果表明了其安全性和有效性。该方案在实现监管可控的情况下,仍然保持了较高的区块生成和验证效率,为区块链内容监管提供了一种新的解决思路。
