Data governance for security in IoT &amp; cloud converged environments

Al-Ruithe, Majid; Mthunzi, Siyakha N.; Benkhelifa, Elhadj

doi:10.1109/aiccsa.2016.7945737

Cited by 17 publications

(20 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other works focus on data management from different viewpoints. For example, Al-Ruithe et al [ 27 ] detailed roles, responsibilities and policies in the context of IoT-Cloud converged environments and provide a generic framework for data governance and security. Similarly, Qin et al [ 14 ] provided a data management perspective on large-scale sensor environments applications posing non-functional requirements to meet the underlying timeliness, reliability and accuracy needs in addition to the functional needs of data collection.…”

Section: Related Workmentioning

confidence: 99%

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

Pérez‐Castillo

Carretero

Caballero

et al. 2018

Sensors

View full text Add to dashboard Cite

The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming.

show abstract

Section: Related Workmentioning

confidence: 99%

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

Pérez‐Castillo

Carretero

Caballero

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Several papers have also discussed the use of blockchain technology in data governance. For example, a study by Gao et al In summary, related work in data governance has focused on the development of frameworks and best practices for managing data, as well as the use of technologies such as IoT [42], ML, DL, FL, blockchain, and SDN to support data governance. Studies have discussed the use of blockchain technology in data governance, and the use of machine learning and deep learning in data governance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Federated learning (FL) is an extension of Machine Learning that enables multiple parties to train a model together without sharing their data, this can help to protect sensitive data. Blockchain is a technology that can be used to create tamper-proof records of transactions; this can help to ensure the integrity of data and make it easier to track changes [9,2,10,11]. Software-Defined Networking (SDN) can be used to create virtualized network infrastructure that can be easily managed and configured, this can help to improve the security and scalability of data networks [12,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

The Security of Data Governance in the Digital World

Khordadpour¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Consumer's perspective of IoT product lifespan. an IoT privacy tool or framework, which can address the concerns of the consumer [31,[40][41][42][43][44][45] compiled in Table 5.…”

Section: Consumer-centric Approachmentioning

confidence: 99%

Privacy of IoT-Enabled Smart Home Systems

Dasgupta¹,

Gill²,

Hussain³

2019

IoT and Smart Home Automation [Working Title]

View full text Add to dashboard Cite

Digital ecosystems are going through a period of change due to the advancement in technologies such as Internet of Things (IoT) as well as proliferation of less expensive hardware sensors. Through this chapter, we present current emerging trends in IoT in different industry sectors as well as discuss the key privacy challenges impeding the growth of IoT to reach its potential in the smart home context. The majority of the existing literature on IoT smart home platforms focuses on functionalities provided by smarter connected devices; however, it does not address the concerns from a consumer's viewpoint. Thus, the key questions are: What are the privacy concerns related to IoT, particularly from a "smart home device" consumer viewpoint? What are the existing remedial approaches for privacy management? This chapter proposes a framework to assist smart home user and IoT device manufacturer to make informed privacy management decisions. The findings of this research intend to help practitioners and researchers interested in the privacy of IoT-enabled smart systems.

show abstract

Data governance for security in IoT & cloud converged environments

Cited by 17 publications

References 23 publications

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

The Security of Data Governance in the Digital World

Privacy of IoT-Enabled Smart Home Systems

Contact Info

Product

Resources

About