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Efficient Secure Data provenance Scheme in Multimedia Outsourcing and Sharing

Computers, Materials & Continua 2018年第7期56卷 1-17页

作者： Zhen Yang Yongfeng Huang Xing Li Wenyu Wang Department of Electronic Engineering Tsinghua UniversityBeijing100084China Tsinghua National Laboratory for Information Science and Technology Beijing100084China Information Networking Institute Carnegie Mellon UniversityPittsburghPA15213USA

To cope with privacy leakage caused by multimedia outsourcing and sharing,Data provenance is used to analyze leaked multimedia and provide reactive accountability.Existing schemes of multimedia provenance are based on watermarking protocols.In an outsourcing scenario,existing schemes face two severe challenges:1)when data leakage occurs,there exists a probability that Data provenance results can be repudiated,in which case Data provenance tracking fails;and 2)when outsourced data are shared,data encryption transfer causes key management burden outside the schemes,and privacy leakage threatens users.In this paper,we propose a novel Data provenance scheme with an improved LUT-based fingerprinting protocol,which integrates an asymmetric watermarking protocol,robust watermark algorithm and homomorphic encryption and digital signatures to achieve full non-repudiation provenance.We build an in-scheme stream cipher to protect outsourced multimedia data from privacy leakage and complicated key management.Our scheme is also lightweight and easy to deploy.Extensive security and performance analysis compares our scheme with the state of the art.The results show that our scheme has not only better provenance security and data confidentiality but also higher efficiency for multimedia outsourcing,sharing and provenance.

关键词： Data provenance asymmetric fingerprint protocol digital watermarking multimedia outsourcing

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PBWA:A provenance-Based What-If Analysis Approach for data Mining Processes

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Chinese Journal of Electronics 2017年第5期26卷 986-992页

作者： KE Jie DONG Hongbin TAN Chengyu LIANG Yiwen Computer School Wuhan University International School of Software Wuhan University

This paper presents a provenance-based what-if analysis approach(PBWA) for data mining processes,so decision makers can examine the latest mining result under hypothetical business contexts.It fills the gap that data mining only reveals past status of enterprises with historical data.provenance information is a kind of metadata of data mining processes.PBWA uses it to identify relevant operation path and intermediate results that is affected by hypothetical business contexts.It refreshes the mining result by partially rerunning the affected portions.Different from previous studies only for relational operations,PBWA can take more general operations into account.Besides,it focuses on the whole mining processes.Experiments demonstrate that when the affected ratio is less than 74% and 87% in different contexts,PBWA can achieve better time performance.

关键词： data mining What-if analysis Data provenance provenance(PROV)

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TDLens:Toward an Empirical Evaluation of provenance Graph-Based Approach to Cyber Threat Detection

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China Communications 2022年第10期19卷 102-115页

作者： Rui Mei Hanbing Yan Qinqin Wang Zhihui Han Zhuohang Lyu Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China National Computer Network Emergency Response Technical Team/Coordination Center of China(CNCERT/CC) Beijing 100029China

To combat increasingly sophisticated cyber attacks,the security community has proposed and deployed a large body of threat detection approaches to discover malicious behaviors on host systems and attack payloads in network traffic.Several studies have begun to focus on threat detection methods based on provenance data of host-level event tracing.On the other side,with the significant development of big data and artificial intelligence technologies,large-scale graph computing has been widely used.To this end,kinds of research try to bridge the gap between threat detection based on host log provenance data and graph algorithm,and propose the threat detection algorithm based on system provenance graph.These approaches usually generate the system provenance graph via tagging and tracking of system events,and then leverage the characteristics of the graph to conduct threat detection and attack investigation.For the purpose of deeply understanding the correctness,effectiveness,and efficiency of different graph-based threat detection algorithms,we pay attention to mainstream threat detection methods based on provenance graphs.We select and implement 5 state-of-the-art threat detection approaches among a large number of studies as evaluation objects for further analysis.To this end,we collect about 40GB of host-level raw log data in a real-world IT environment,and simulate 6 types of cyber attack scenarios in an isolated environment for malicious provenance data to build our evaluation datasets.The crosswise comparison and longitudinal assessment interpret in detail these detection approaches can detect which attack scenarios well and why.Our empirical evaluation provides a solid foundation for the improvement direction of the threat detection approach.

关键词： cyber threat detection causality dependency graph Data provenance

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FAIR data Reuse-the Path through data Citation

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data Intelligence 2020年第1期2卷 78-86,305页

作者： Paul Groth Helena Cousijn Tim Clark Carole Goble Informatics Institute University of AmsterdamAmsterdam 1090 GHThe Netherlands dataCite Welfengarten 1BHannover 30167Germany data Science Institute University of VirginiaCharlottesvilleVA 22903-1738USA Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK

One of the key goals of the FAIR guiding principles is defined by its final principle-to optimize data sets for reuse by both humans and machines.To do so,data providers need to implement and support consistent machine readable metadata to describe their data sets.This can seem like a daunting task for data providers,whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used.Additionally,for existing data sets it is often unclear what steps should be taken to enable maximal,appropriate reuse.data citation already plays an important role in making data findable and accessible,providing persistent and unique identifiers plus metadata on over 16 million data sets.In this paper,we discuss how data citation and its underlying infrastructures,in particular associated metadata,provide an important pathway for enabling FAIR data reuse.

关键词： FAIR data data citation Research objects Data provenance

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