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Security of Data Collected for Quality Control (Pilot)

By University of Groningen

Type of course:

Digital learning, Lesson

Language:

EN

Duration:

10 minutes

Workload:

2 hours

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

“In a world where data drives decisions, how secure is the data fueling your quality control?”

Welcome to a transformative course designed specifically for manufacturing professionals who handle sensitive data for quality control and anomaly detection. In an era of connected devices, advanced analytics, and collaborative data sharing, ensuring the security of your data isn’t just a compliance checkbox—it’s a critical aspect of maintaining your competitive edge, protecting intellectual property, and building trust in partnerships.

This course provides a roadmap for understanding and implementing robust security measures to protect the integrity, confidentiality, and availability of your quality control data. We’ll cover everything from data privacy regulations to API security, and from secure data sharing architectures to effective collaboration with external experts. By the end of this course, you’ll be equipped with actionable insights and best practices to safeguard data collected from sensors, production lines, and inspection processes, all while navigating the complexities of secure data sharing with trusted partners.

Engaging, practical, and packed with real-world examples, this training is your toolkit for confidently protecting your data assets and maximizing the value of data-driven quality control. Whether you’re overseeing data transfer protocols, establishing secure connections, or developing a comprehensive security policy, this course is your essential guide to mastering data security in modern manufacturing.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.


Learning outcomes

  1. By the end of this lesson, students will be able to recognize key data privacy regulations affecting quality control data in manufacturing.
  2. By the end of this lesson, students will be able to identify potential security risks and implement secure data sharing architectures, including API security and data encryption.
  3. By the end of this lesson, students will be able to describe secure connections and protocols for data sharing, ensuring protected collaboration with external experts and partners.

Topics

Automation and Sensoring, Automation and Robotics, Digital Transformation, Machine Learning

Provided by

Content created in 2024
+30 enrolled
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Course Includes

  • 1 Quiz

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