Research progress on big data security technology

Chen, Xingyuan; Gao, Yuanzhao; Huilin, Tang; Du, Xuehui

doi:10.1360/n112019-00077

Cited by 7 publications

(5 citation statements)

References 187 publications

(219 reference statements)

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“…jleaves.H i /j 1 jleaves.DGH i /j 1 ; q i is categorical (7) where jU i L i j is the value domain of the numeric attribute q i , and u i and l i are the upper and lower bounds of g i , respectively.…”

Section: Information Lossmentioning

confidence: 99%

“…Any disregard for privacy protection leads to high economic losses and incalculable reputational damage. Therefore the privacy preservation of data streams has attracted increasing attention from industry and academia [4][5][6][7] . Anonymization is a commonly used privacy-preserving method that allows the sharing of sensitive data with a guarantee that the individuals, i.e., the subjects of the data, cannot be recognized [8] .…”

Section: Introductionmentioning

confidence: 99%

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IDEA: A utility-enhanced approach to incomplete data stream anonymization

Lei¹,

Chen²,

Luo

et al. 2022

Tsinghua Sci. Technol.

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The prevalence of missing values in the data streams collected in real environments makes them impossible to ignore in the privacy preservation of data streams. However, the development of most privacy preservation methods does not consider missing values. A few researches allow them to participate in data anonymization but introduce extra considerable information loss. To balance the utility and privacy preservation of incomplete data streams, we present a utility-enhanced approach for Incomplete Data strEam Anonymization (IDEA). In this approach, a slide-window-based processing framework is introduced to anonymize data streams continuously, in which each tuple can be output with clustering or anonymized clusters. We consider the dimensions of attribute and tuple as the similarity measurement, which enables the clustering between incomplete records and complete records and generates the cluster with minimal information loss. To avoid the missing value pollution, we propose a generalization method that is based on maybe match for generalizing incomplete data. The experiments conducted on real datasets show that the proposed approach can efficiently anonymize incomplete data streams while effectively preserving utility.

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Section: Information Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IDEA: A utility-enhanced approach to incomplete data stream anonymization

Lei¹,

Chen²,

Luo

et al. 2022

Tsinghua Sci. Technol.

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“…To deal with the characteristics of big data introduced above, many enterprises and research institutions put forward specific big data system technology framework to implement data processing [11]. Among them, NIST summarized existing frameworks and presented a technologyand infrastructure-agnostic big data reference architecture (NBDRA) [12], which has great influence internationally [17]. NBDRA is organized around five major roles: system orchestrator, data provider, big data application provider, big data framework provider and data consumer.…”

Section: Related Work a Big Data Characteristicsmentioning

confidence: 99%

A Big Data Provenance Model for Data Security Supervision Based on PROV-DM Model

Gao

2020

IEEE Access

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Nowadays, big data has become a hot research topic. It gives fresh impetus to the economic and social development. However, the huge value of big data also makes it the focus of attacks. Big data security incidents occur frequently in recent years. The security supervision capacities for big data do not match its important role. Data provenance which describes the origins of data and the process by which it arrived the current state, is an effective approach for data supervision. For the full use of provenance in big data supervision, a provenance model which defines the concepts used to represent the provenance types and relations is required to be built in advance, but current provenance models do not adapt to big data scenarios well. In this paper, we comprehensively consider the characteristics of big data and the requirements of data security supervision, extend the widely used provenance model PROV-DM by subtyping and new relation definition, and propose a big data provenance model (BDPM) for data supervision. BDPM model supports the provenance representation of various data types and diverse data processing modes to represent the entire data transformation process through different components in the big data system, and defines new relations to enrich provenance analysis functions. Based on BDPM model, we introduce the constraints that should be satisfied in the construction of valid provenance graph and present the data security supervision methods via provenance graph analysis. Finally, we evaluated the satisfiability of BDPM model through a case study.

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“…Feng et al [8] discussed the related technical challenges on big data security and privacy protection, and explained that big data has become an important way to solve security problems while introducing security problems. Chen et al [9] sorted out the key technologies of big data security protection. By summarizing the current research results on big data security and privacy protection, it can know the research results in this field are mainly the application of the security technology and privacy protection technology of small-capacity data in big data environment, and because at present these technologies can not completely solve the security and privacy protection problems of small-capacity data, so, in application the security protect ability of these technologies to big data is limited.…”

Section: Introductionmentioning

confidence: 99%

Operation Mechanisms for Intelligent Logistics System: A Blockchain Perspective

Zhu

2019

IEEE Access

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Aiming at the current problems of security threats and privacy leak risks in the operation process of related data of intelligent logistics system, and the operation of system lacking of supervision and traceability, this paper proposes to use blockchain technology to resolve these problems. A scheme on applying blockchain in intelligent logistics system is proposed, including operation principle, consensus authentication mechanism, and data storage and access mechanism. By introducing and analyzing the related big data of intelligent logistics system, improving the scientific, rationality, and intelligence of the decision. The basic characteristic of blockchain is traceability. By constructing algorithm models, proposing the realization principles of consensus authentication mechanism. For different events, different ''multi-authentication centers'', intelligent contracts, and blockchain systems are constructed, improving the efficiency and supervision of the operation of intelligent logistics system. By constructing the correlations between the fundamental data that corresponding to different blockchain, and the correlations between the fundamental data that corresponding to the same blockchain, making the related data easier to collect and analyze. By constructing the storage and access mechanism, ensuring the security and confidentiality of the operation data of intelligent logistics system. This paper has provided thought for the application research of intelligent logistics system based on blockchain, and has positive reference value and guiding significance to the development of blockchain application research.

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Research progress on big data security technology

Cited by 7 publications

References 187 publications

IDEA: A utility-enhanced approach to incomplete data stream anonymization

IDEA: A utility-enhanced approach to incomplete data stream anonymization

A Big Data Provenance Model for Data Security Supervision Based on PROV-DM Model

Operation Mechanisms for Intelligent Logistics System: A Blockchain Perspective

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