A Data Trading Scheme with Efficient Data Usage Control for Industrial IoT

In this chapter, we first summarize an analysis of the differences between Grids and the previously dominant model of inter-organizational collaboration. Based on requirements derived thereof, we specify a security framework that demonstrates how well-established policy-based privacy management architectures can be extended to provide the required Grid-specific functionality. We also discuss the necessary steps for integration into existing service provider and service access point infrastructures. Special emphasis is put on privacy policies that can be configured by users themselves, and distinguishing between the initial data access phase and the later data usage control phase. We also discuss the challenges of practically applying the required changes to real-world infrastructures, including delegated administration, monitoring, and auditing.

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Distributed data usage control for web applications

Proceedings of the first ACM conference on Data and application security and privacy - CODASPY '11 ◽

10.1145/1943513.1943526 ◽

2011 ◽

Cited By ~ 25

Author(s):

Prachi Kumari ◽

Alexander Pretschner ◽

Jonas Peschla ◽

Jens-Michael Kuhn

Keyword(s):

Web Applications ◽

Distributed Data ◽

Usage Control ◽

Data Usage

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Big Data Management in Maritime Transport

Journal of Maritime & Transportation Science ◽

10.18048/2019.57.09. ◽

2019 ◽

Vol 57 (1) ◽

pp. 123-141

Author(s):

Marija Jović ◽

Edvard Tijan ◽

Rebecca Marx ◽

Berit Gebhard

Keyword(s):

Decision Making ◽

Big Data ◽

Deep Understanding ◽

Future Perspective ◽

Maritime Transport ◽

Data Usage ◽

Data Analyses ◽

Efficient Data ◽

Routing Monitoring ◽

Efficient Data Collection

As maritime transport produces a large amount of data from various sources and in different formats, authors have analysed current applications of Big Data by researching global applications and experiences and by studying journal and conference articles. Big Data innovations in maritime transport (both cargo and passenger) are demonstrated, mainly in the fields of seaport operations, weather routing, monitoring/tracking and security. After the analysis, the authors have concluded that Big Data analyses can provide deep understanding of causalities and correlations in maritime transport, thus improving decision making. However, there exist major challenges of an efficient data collection and processing in maritime transport, such as technology challenges, challenges due to competitive conditions etc. Finally, the authors provide a future perspective of Big Data usage in maritime transport.

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