Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Security and Communication Networks ◽

10.1155/2020/2017930 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Jing Liu ◽

Changbo Yuan ◽

Yingxu Lai ◽

Hua Qin

Keyword(s):

Real Time ◽

Data Protection ◽

Cloud Storage ◽

Distributed Storage ◽

Sensitive Information ◽

Sensitive Data ◽

Industrial Data ◽

Threat Model ◽

Industrial Internet ◽

Storage Scheme

Industrial Internet technology has developed rapidly, and the security of industrial data has received much attention. At present, industrial enterprises lack a safe and professional data security system. Thus, industries urgently need a complete and effective data protection scheme. This study develops a three-layer framework with local/fog/cloud storage for protecting sensitive industrial data and defines a threat model. For real-time sensitive industrial data, we use the improved local differential privacy algorithm M-RAPPOR to perturb sensitive information. We encode the desensitized data using Reed–Solomon (RS) encoding and then store them in local equipment to realize low cost, high efficiency, and intelligent data protection. For non-real-time sensitive industrial data, we adopt a cloud-fog collaborative storage scheme based on AES-RS encoding to invisibly provide multilayer protection. We adopt the optimal solution of distributed storage in local equipment and the cloud-fog collaborative storage scheme in fog nodes and cloud nodes to alleviate the storage pressure on local equipment and to improve security and recoverability. According to the defined threat model, we conduct a security analysis and prove that the proposed scheme can provide stronger data protection for sensitive data. Compared with traditional methods, this approach strengthens the protection of sensitive information and ensures real-time continuity of open data sharing. Finally, the feasibility of our scheme is validated through experimental evaluation.

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A Three-Layer Privacy Preserving Cloud Storage Scheme Based on Computational Intelligence in Fog Computing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f7377.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 451-455

Keyword(s):

Cloud Storage ◽

Distributed Storage ◽

Fog Computing ◽

Good Judgment ◽

Security Assurance ◽

Course Of Action ◽

Cloud Server ◽

Storage Scheme ◽

Cloud Servers ◽

Unstructured Information

Late years witness the improvement of distributed computing innovation. With the hazardous development of unstructured information, distributed storage innovation improves advancement. Notwithstanding, in current stockpiling pattern, client's information is completely put away in cloud servers. At the end of the day, clients lose their privilege of control on information and face security spillage chance. Conventional security assurance plans are normally founded on encryption innovation, yet these sorts of strategies can't viably oppose assault from within cloud server. So as to take care of this issue, we propose a three-layer stockpiling structure dependent on mist registering. The proposed structure can both exploit distributed storage and secure the protection of information. By at that point, we can place a little piece of information in neighborhood machine and mist server so as to ensure the security. In like manner, in context on computational information, this tally can figure the assignment degree put aside in cloud, darkness, and near to machine, autonomously. Through the theoretical security appraisal and primer assessment, the good judgment of our course of action has been supported, which is actually a historic Added to current dispersed amassing plot.

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Statistical Sensitive Data Protection and Inference Prevention with Decision Tree Methods

10.21236/ada465138 ◽

2003 ◽

Author(s):

LiWu Chang

Keyword(s):

Decision Tree ◽

Data Protection ◽

Sensitive Data ◽

Tree Methods ◽

Decision Tree Methods

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A new distributed storage scheme for cluster video server

Journal of Systems Architecture ◽

10.1016/j.sysarc.2004.09.001 ◽

2005 ◽

Vol 51 (2) ◽

pp. 79-94 ◽

Cited By ~ 5

Author(s):

Xiaofei Liao ◽

Hai Jin

Keyword(s):

Distributed Storage ◽

Video Server ◽

Storage Scheme

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Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽

10.3390/s21113715 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3715

Author(s):

Ioan Ungurean ◽

Nicoleta Cristina Gaitan

Keyword(s):

Internet Of Things ◽

Real Time ◽

Development Process ◽

Fog Computing ◽

Low Latency ◽

Practical Implementation ◽

Industrial Internet Of Things ◽

Industrial Internet ◽

Starting Point ◽

Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

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Fine-grained Data Deduplication and proof of storage Scheme in Public Cloud Storage

2021 International Conference on COMmunication Systems & NETworkS (COMSNETS) ◽

10.1109/comsnets51098.2021.9352742 ◽

2021 ◽

Author(s):

Hardik Gajera ◽

Manik Lal Das

Keyword(s):

Cloud Storage ◽

Public Cloud ◽

Data Deduplication ◽

Fine Grained ◽

Storage Scheme

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Software-defined data protection

Proceedings of the VLDB Endowment ◽

10.14778/3450980.3450986 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1167-1174

Author(s):

Zsolt István ◽

Soujanya Ponnapalli ◽

Vijay Chidambaram

Keyword(s):

Data Processing ◽

Data Protection ◽

Distributed Storage ◽

Hardware Design ◽

Large Data ◽

Hardware Interface ◽

Future Data ◽

Specialized Hardware

Most modern data processing pipelines run on top of a distributed storage layer, and securing the whole system, and the storage layer in particular, against accidental or malicious misuse is crucial to ensuring compliance to rules and regulations. Enforcing data protection and privacy rules, however, stands at odds with the requirement to achieve higher and higher access bandwidths and processing rates in large data processing pipelines. In this work we describe our proposal for the path forward that reconciles the two goals. We call our approach "Software-Defined Data Protection" (SDP). Its premise is simple, yet powerful: decoupling often changing policies from request-level enforcement allows distributed smart storage nodes to implement the latter at line-rate. Existing and future data protection frameworks can be translated to the same hardware interface which allows storage nodes to offload enforcement efficiently both for company-specific rules and regulations, such as GDPR or CCPA. While SDP is a promising approach, there are several remaining challenges to making this vision reality. As we explain in the paper, overcoming these will require collaboration across several domains, including security, databases and specialized hardware design.

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Smart Tool*/Smart Tool - Intelligent sensor-based tool management

wt Werkstattstechnik online ◽

10.37544/1436-4980-2016-03-10 ◽

2016 ◽

Vol 106 (03) ◽

pp. 106-110

Author(s):

E. Abele ◽

T. Grosch ◽

E. Schaupp

Keyword(s):

Tool Wear ◽

Real Time ◽

Industrie 4.0 ◽

Tool Management ◽

Intelligent Sensor ◽

Individual Level ◽

Tool Holder ◽

Industrial Internet ◽

Current Location

Im Kontext von Industrie 4.0 bietet eine Optimierung des Werkzeugmanagements zahlreiche Potentiale. Durch ein Track & Trace-System, welches in den gesamten Werkzeugkreislauf integriert wird, lässt sich der aktuelle Aufenthaltsort der Werkzeuge auf Individuumsebene in Echtzeit bestimmen. Eine im Werkzeughalter untergebrachte Sensorik liefert zusätzliche Informationen über den aktuellen Zustand der Werkzeuge, beispielsweise den Verschleiß.   In the context of the industrial internet (Industrie 4.0), tool management offers great potential. With a track&trace-system integrated in the whole tool cycle the current location of tools can be determined at individual level in real time. Furthermore, sensors placed in the tool holder provide information about the tool`s current condition, e.g. tool wear.

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IIOT and Real Time Data Analytics - Maximizing the Impact on Safety and Productivity

10.2118/205610-ms ◽

2021 ◽

Author(s):

Ryan Daher ◽

Nesma Aldash

Keyword(s):

Real Time ◽

Case Studies ◽

Data Analytics ◽

Contact Tracing ◽

Time Data ◽

Seamless Integration ◽

Location Services ◽

Industrial Internet ◽

Potential Applications ◽

The Impact

Abstract With the global push towards Industry 4.0, a number of leading companies and organizations have invested heavily in Industrial Internet of Things (IIOT's) and acquired a massive amount of data. But data without proper analysis that converts it into actionable insights is just more information. With the advancement of Data analytics, machine learning, artificial intelligence, numerous methods can be used to better extract value out of the amassed data from various IIOTs and leverage the analysis to better make decisions impacting efficiency, productivity, optimization and safety. This paper focuses on two case studies- one from upstream and one from downstream using RTLS (Real Time Location Services). Two types of challenges were present: the first one being the identification of the location of all personnel on site in case of emergency and ensuring that all have mustered in a timely fashion hence reducing the time to muster and lessening the risks of Leaving someone behind. The second challenge being the identification of personnel and various contractors, the time they entered in productive or nonproductive areas and time it took to complete various tasks within their crafts while on the job hence accounting for efficiency, productivity and cost reduction. In both case studies, advanced analytics were used, and data collection issues were encountered highlighting the need for further and seamless integration between data, analytics and intelligence is needed. Achievements from both cases were visible increase in productivity and efficiency along with the heightened safety awareness hence lowering the overall risk and liability of the operation. Novel/Additive Information: The results presented from both studies have highlighted other potential applications of the IIOT and its related analytics. Pertinent to COVID-19, new application of such approach was tested in contact tracing identifying workers who could have tested positive and tracing back to personnel that have been in close proximity and contact therefore reducing the spread of COVID. Other application of the IIOT and its related analytics has also been tested in crane, forklift and heavy machinery proximity alert reducing the risk of accidents.

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