Context-Driven Granular Disclosure Control for Internet of Things Applications

Panah, Arezou Soltani; Yavari, Ali; Schyndel, Ron van; Georgakopoulos, Dimitrios; Yi, Xun

doi:10.1109/tbdata.2017.2737463

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…We also observed that the proposed phases of the data lifecycles also vary [56] while often for the same phase, different terms are used. For example, in the case of data collection, some studies use the term "generation" [95,96] while in other data lifecycles call it "receive" [17,90], "acquir" [55,63,73,97], or data "capture" [84]. So, there is a lot of confusion for the research community and practitioners to understand and use such phases and associate them with management practices in their organizations.…”

Section: Research Gapsmentioning

confidence: 99%

“…We assign a "mandatory" category to phase that has > 70% of appearance in the data lifecycles, and in other cases, we assigned it "optional" category as shown in Table 2. Additionally, we provide detailed information about the data lifecycles and their [15,53,58,134,143], receive [17], acquisition [13,117], retrieve [115,121], produce [140], acquire [55,63,90,97,147], capture [84,94,126,130,135,148], generation [26,56,95,96,[118][119][120], create [47, 109-111, 116, 124, 149] Collection P3 Integration [17,26,109,124,126,129,130,146,148,153,155], integrate [15,28,116,140,156], aggregate…”

Section: Approach To Dalifmentioning

confidence: 99%

“…• Sign off data sharing agreements between governments and other stakeholders that emphasize the legitimate basis and logic behind why public data is being shared [58,66,115]. • Ensure to take appropriate measures that enable individuals to control whom to share data and how much the owner is eager to share [67,97,114,142]. • Data providers focus on maintaining a balance between data availability and data redundancy when publishing data through various formats [79,93].…”

Section: Storage Phase Key Functionsmentioning

confidence: 99%

“…Protection phase This phase focuses on data protection in terms of data integrity, security, access control, and privacy [17,61,182]. The phase is being considered throughout the data lifecycle, i.e., from planning, collection to archive, and destruction phase to maintain data security and privacy protection against any accidental or malicious compromises to the GBDE [58,69,97,144,183]. Due to the quantity, variety, and sensitivity of the big data and its management through heterogeneous based technological solutions, data security and privacy protection become crucial.…”

Section: Data Quality Phasementioning

confidence: 99%

See 3 more Smart Citations

DaLiF: a data lifecycle framework for data-driven governments

2021

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The public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

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Section: Research Gapsmentioning

confidence: 99%

Section: Approach To Dalifmentioning

confidence: 99%

Section: Storage Phase Key Functionsmentioning

confidence: 99%

Section: Data Quality Phasementioning

confidence: 99%

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DaLiF: a data lifecycle framework for data-driven governments

2021

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“…The issue of how to protect the security of private information has aroused widespread concern. In April 2016, the European Union promulgated the "General Data Protection Regulation", which provides reference application standards for people's privacy protection [1] [2] [3]. First, Fridrich explored the two-dimensional standard Baker symmetric image encryption algorithm based on chaotic mapping and proposed [4], and has become a key research object of data encryption technology.…”

Section: Introductionmentioning

confidence: 99%

Image Encryption and Decryption Based on Chaotic Algorithm

Hu¹,

Tian²

2020

JAMP

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This paper analyzes the problems in image encryption and decryption based on chaos theory. This article introduces the application of the two-stage Logistic algorithm in image encryption and decryption, then by information entropy analysis it is concluded that the security of this algorithm is higher compared with the original image; And a new image encryption and decryption algorithm based on the combination of two-stage Logistic mapping and M sequence is proposed. This new algorithm is very sensitive to keys; the key space is large and its security is higher than two-stage Logistic mapping of image encryption and decryption technology.

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IoTSec: A Lightweight and Holistic IoT Security Based on IoT Data Contextualisation and Homomorphic Encryption

Yavari

Georgakopolous

2021

Next-Gen Digital Services. A Retrospective and Roadmap for Service Computing of the Future

Self Cite

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The Internet of Things incorporates billions of sensors, cameras, RFID and other machines that observe and/or affect the physical world, as well as IoT applications that harvest and analyse IoT data in the cloud, edge computers, and/or the IoT devices themselves. To realise its full potential IoT must ensure the security of the IoT data and related applications that support the IoT-based services and products that are provided to their consumers. Although IoT devices, networks, and computing resources support robust security standards and include related mechanisms that can secure IoT data within the scope of these IoT components, compositions of such point security solutions often fail to ensure the security of IoT data across the IoT ecosystem. In this article we will discuss the main challenges in securing IoT data acquisition, communication, analysis, actuation and illustrate the need for IoT security solutions that are both holistic and lightweight. In addition, we propose a holistic and lightweight IoT security mechanism that via a novel combination of contextualisation with homomorphic encryption prevents harmful outcomes from lack of IoT data security.

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Context-Driven Granular Disclosure Control for Internet of Things Applications

Cited by 8 publications

References 30 publications

DaLiF: a data lifecycle framework for data-driven governments

DaLiF: a data lifecycle framework for data-driven governments

Image Encryption and Decryption Based on Chaotic Algorithm

IoTSec: A Lightweight and Holistic IoT Security Based on IoT Data Contextualisation and Homomorphic Encryption

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