While edge computing significantly impacts modern computing systems and applications, it also introduces ethical and social implications. This chapter explores two key topics, privacy and bias, central to these concerns. Edge servers process large volumes of data offloaded from end devices, which raises privacy issues but also offers unique opportunities to develop privacy-preserving mechanisms. We examine major privacy challenges in edge computing and discuss various privacypreserving solutions. The localized nature of edge computing can lead to significant data biases at individual edge nodes, resulting in misleading conclusions if applied elsewhere. Additionally, machine learning and deep learning algorithms deployed for autonomous decision-making at the edge may inherently carry biases. This chapter analyzes these biases, their causes and impacts on edge computing algorithms, and techniques for mitigating their effects.
📝 Practice Questions
1. Identify the key factors that contribute to biases in AI models deployed on edge devices and discuss how these biases might manifest in real-world applications.
2. Discuss the potential privacy concerns associated with collecting and processing data at the edge.
3. Analyze how biases in edge computing algorithms can impact different user groups, and outline steps to mitigate these biases.
4. Examine the legal measures necessary to ensure privacy protection in edge computing.
5. Discuss the technical challenges of implementing privacy-preserving techniques such as differential privacy and federated learning on edge devices, and how do these challenges affect privacy and bias.
📘 Course Projects
1. Evaluate the privacy concerns of a basic edge computing application.
2. Implement and evaluate a privacy-preserving algorithm.
3. Assess bias in a given dataset.
4. Implement and evaluate a bias mitigation algorithm.
5. Propose new strategies to minimize biases in a specific edge computing algorithm.
6. Design protocol for secure data aggregation in edge networks.
7. Create a framework for auditing decisions made by AI models on edge devices, ensuring transparency and accountability in AI-driven processes.
8. Implement edge computing for privacy-preserving applications in important domains such as healthcare, connected autonomous vehicles, and others.
9. Evaluate the potential of blockchain technology in enhancing privacy and reducing bias in edge computing applications.
10. Design a system for real-time privacy monitoring in edge IoT applications.
📚 Suggested Papers
1. Nicol Turner Lee, Paul Resnick, and Genie Barton. "Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms". In: Brookings Institute: Washington, DC, USA 2 (2019) | Paper.
2. Pasika Ranaweera, Anca Delia Jurcut, and Madhusanka Liyanage. "Survey on multi-access edge computing security and privacy". In: IEEE Communications Surveys & Tutorials 23. 2 (2021), pp. 1078–1124 | Paper.
3. Mark Ryan. "The future of transportation: Ethical, legal, social and economic impacts of self driving vehicles in the year 2025". In: Science and Engineering Ethics 26. 3 (2020), pp. 1185–1208 | Paper.
4. Kewei Sha et al. "On security challenges and open issues in Internet of Things". In: Future Generation Computer Systems 83 (2018), pp. 326–337 | Paper.
5. Kewei Sha et al. "A survey of edge computing-based designs for IoT security". In: Digital Communications and Networks 6. 2 (2020), pp. 195–202 | Paper.
