Fog Computing as Privacy Enabler

Abstract

Despite broad discussions on privacy challenges arising from fog computing, the authors argue that privacy and security requirements might actually drive the adoption of fog computing. They present four patterns of fog computing fostering data privacy and the security of business secrets, complementing existing cryptographic approaches. Their practical application is illuminated on the basis of three case studies.

Preprint, accepted for publication in IEEE Internet Computing. This is the authors' own version before final copy-editing by IEEE

References in corpus (1)

• HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning

Cited by in corpus (9)

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• Configurable Per-Query Data Minimization for Privacy-Compliant Web APIs
• RedCASTLE: Practically Applicable $k_s$-Anonymity for IoT Streaming Data at the Edge in Node-RED
• FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning