ReviewSensors (Basel, Switzerland)2023
A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Bank data protection and fraud identification based on improved adaptive federated learning and WGAN.Scientific reports · 2025Article
- Personal health data protection and intelligent healthcare applications under generative adversarial network.Scientific reports · 2025Article
- Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research.Nature machine intelligence · 2024Article
- Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies.Sensors (Basel, Switzerland) · 2024Review
- Machine learning cryptography methods for IoT in healthcare.BMC medical informatics and decision making · 2024Article
- A Systematic Review on Privacy-Aware IoT Personal Data Stores.Sensors (Basel, Switzerland) · 2024Review
- Low-Fidelity Prototype of a Sensor-Dependent Interaction Platform: Formative Evaluation With Informal Caregivers of Older Adults With Cognitive Impairment.JMIR formative research · 2024Article
- Cybersecurity Analysis of Wearable Devices: Smartwatches Passive Attack.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.