Data Privacy Preservation on the Internet of Things
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Abstract (arXiv)
Recent developments in hardware and information technology have enabled the emergence of billions of connected, intelligent devices around the world exchanging information with minimal human involvement. This paradigm, known as the Internet of Things (IoT) is progressing quickly with an estimated 27 billion devices by 2025. This growth in the number of IoT devices and successful IoT services has generated a tremendous amount of data. However, this humongous volume of data poses growing concerns for user privacy. This introductory chapter has presented a brief survey of some of the existing data privacy-preservation schemes proposed by researchers in the field of the Internet of Things.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 55 references that are in the map. First posted on arXiv 1 Apr 2023.
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References · 10
- 2016Deep Learning with Differential Privacycited by 2,682
- 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
- 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
- 2020A Survey of Privacy Attacks in Machine Learningcited by 143
- 2017DeepSecure: Scalable Provably-Secure Deep Learningcited by 48
- 2017A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analyticscited by 30
- 2018Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloudcited by 28
- 2018Privacy-preserving Machine Learning through Data Obfuscationcited by 18
- 2020On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projectionscited by 3
- 2017Privacy-Preserving Personal Model Trainingcited by 1
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