A Study on Onsite–Offshore Data Security Model for Big Data Applications

Manjunath, T N; Pushpa, S.; Hegadi, Ravindra S.; Archana, R. A.

doi:10.1007/978-981-33-4046-6_23

Cited by 1 publication

(4 citation statements)

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“…There are many research programs on dynamic data masking [4,6,[11][12][13][14][15][16] , and several architectures have been proposed. We divide these programs into those in the agent-based mode and those in the kernel-based mode.…”

Section: Related Workmentioning

confidence: 99%

“…After receiving the modified query, the data source will output false (mixed) data. According to the query request rewriting method, researchers have proposed the rewriting technology based on rule engines [11] and syntax parsing [12,13] .…”

Section: Agent-based Modementioning

confidence: 99%

“…Informatica [11] and other manufacturers have adopted dynamic masking technology that matches and rewrites the query SQL by the rule engine in the masking agent gateway. The request is parsed via the connection of the rule engine with the rule tree.…”

Section: Agent-based Modementioning

confidence: 99%

“…For the "create table as select" and "insert (overwrite) into select" statements, it is required to obtain the name of the derived table and its masking mode (Lines 4-5). Next, for the output 11). Finally, we construct an SQL fragment on the basis of the masking operator function and replace the current AST node value with the fragment value (Lines 13-14, Step 2.3 in Figure 6).…”

Section: Service-database-tablementioning

confidence: 99%

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BDMasker: Dynamic Data Protection System for Open Big Data Environment

Tu¹,

Niu²,

Wang³

et al. 2023

International Journal of Software and Informatics

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Big data has become a national basic strategic resource, and the opening and sharing of data is the core of China's big data strategy. Cloud native technology and lake-house architecture are reconstructing the big data infrastructure and promoting data sharing and value dissemination. The development of the big data industry and technology requires stronger data security and data sharing capabilities. However, data security in an open environment has become a bottleneck, which restricts the development and utilization of big data technology. The issues of data security and privacy protection have become increasingly prominent both in the open source big data ecosystem and the commercial big data system. Dynamic data protection system under the open big data environment is now facing challenges in regards such as data availability, processing efficiency, and system scalability. This paper proposes the dynamic data protection system BDMasker for the open big data environment. Through a precise query analysis and query rewriting technology based on the query dependency model, it can accurately perceive but does not change the original business request, which indicates that the whole process of dynamic masking has zero impact on the business. Furthermore, its multi-engine-oriented unified security strategy framework realizes the vertical expansion of dynamic data protection capabilities and the horizontal expansion among multiple computing engines. The distributed computing capability of the big data execution engine can be used to improve the data protection processing performance of the system. The experimental results show that the precise SQL analysis and rewriting technology proposed by BDMasker is effective. The system has good scalability and performance, and the overall performance fluctuates within 3% in the TPC-DS and YCSB benchmark tests.

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Section: Related Workmentioning

confidence: 99%

Section: Agent-based Modementioning

confidence: 99%

Section: Agent-based Modementioning

confidence: 99%

Section: Service-database-tablementioning

confidence: 99%

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