Privacy Preservation in Distributed Systems Algorithms and Applications /
This book provides a discussion of privacy in the following three parts: Privacy Issues in Data Aggregation; Privacy Issues in Indoor Localization; and Privacy-Preserving Offloading in MEC. In Part 1, the book proposes LocMIA, which shifts from membership inference attacks against aggregated locatio...
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| Autors principals: | , , |
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| Autor corporatiu: | |
| Format: | Electrònic eBook |
| Idioma: | anglès |
| Publicat: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edició: | 1st ed. 2024. |
| Col·lecció: | Signals and Communication Technology,
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| Matèries: | |
| Accés en línia: | https://doi.org/10.1007/978-3-031-58013-0 |
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Taula de continguts:
- Introduction
- Part I Privacy lssues in Data Aggregation
- LocMIA: Membership Inference Attacks against Aggregated Location Data
- Synthesizing Privacy Preserving Traces: Enhancing Plausibility with Social Networks
- DAML: Practical Secure Protocol for Data Aggregation based Machine Learning
- Enhancing Privacy Preservation in Speech Data Publishing
- Part II Privacy Issues in Indoor Localization
- Lightweight Privacy-Preserving Scheme in WiFi Fingerprint-Based Indoor Localization
- P3LOC: A Privacy-Preserving Paradigm-Driven framework for Indoor Localization
- Preserving Privacy in WiFi Localization with Plausible Dummy Locations
- Part III Privacy-Preserving Offloading in MEC
- Deep Reinforcement Learning-based Joint Optimization of Delay and Privacy in Multiple-User MEC Systems
- Load Balancing for Energy-Harvesting Mobile Edge Computing
- Learning-based Joint Optimization of Energy-Delay and Privacyin Multiple-User Edge-Cloud Collaboration MEC Systems.