PP-IDE Behavioural Device Fingerprinting Dataset for Privacy-Preserving Identity Establishment
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| _version_ | 1866901141068972032 |
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| author | Selvam, Muthupavithran Wang, shuo Rui, Liu Haque, Safwana Singh, Amit cui, zhan Rajarajan, Muttukrishnan |
| author_facet | Selvam, Muthupavithran Wang, shuo Rui, Liu Haque, Safwana Singh, Amit cui, zhan Rajarajan, Muttukrishnan |
| contents | <p>This dataset provides a processed behavioural device-fingerprinting representation designed to support research on privacy-preserving identity establishment and continuous device authentication. The dataset is derived from controlled execution-response measurements collected from 11 Raspberry Pi devices operating under fixed experimental conditions.</p> <p>Measurements originate from 3 Raspberry Pi 3 Model B+ devices and 8 Raspberry Pi 4 Model B devices. Data collection was conducted in a controlled headless execution environment with fixed CPU frequency configuration, CPU core isolation, and reduced scheduling interference to ensure reproducibility of behavioural response signals.</p> <p>Each device was evaluated across 4 CPU cores and multiple reboot cycles. The dataset contains execution-dependent behavioural signals, including:</p> <ul> <li>Temperature-dependent execution responses</li> <li>GPU-related performance counter behaviour</li> <li>CPU hash execution response signals</li> <li>Pseudo-random execution response signals</li> <li>True random number generation: behavioural signals</li> </ul> <p>A comprehensive feature engineering pipeline was applied, incorporating:</p> <ul> <li>Temporal rolling statistical features</li> <li>Exponential moving dynamics</li> <li>Signed behavioural interaction features</li> <li>Robust statistical transformations</li> <li>Behavioural deviation modelling</li> <li>Representation-learning-oriented feature construction</li> </ul> <p>Unlike the CD2A dataset, which utilises a limited handcrafted feature representation focused primarily on QPU frequency behaviour, the PP-IDE dataset provides an expanded behavioural feature space designed to support:</p> <ul> <li>Privacy-preserving federated learning research</li> <li>Behavioural identity establishment modelling</li> <li>Continuous authentication system design</li> <li>Adversarial robustness evaluation</li> <li>Hardware-rooted behavioural fingerprint analysis</li> </ul> <p>Persistent hardware identifiers have been removed and replaced with stable pseudonymous device labels to preserve device-level behavioural consistency while ensuring privacy protection.</p> <p>This dataset is intended for research in behavioural device authentication, privacy-preserving machine learning, federated behavioural modelling, and security evaluation of embedded and IoT systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19202451 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | PP-IDE Behavioural Device Fingerprinting Dataset for Privacy-Preserving Identity Establishment Selvam, Muthupavithran Wang, shuo Rui, Liu Haque, Safwana Singh, Amit cui, zhan Rajarajan, Muttukrishnan Behavioural Device Fingerprinting Privacy-Preserving Identity Establishment Federated Learning for IoT Security Differential Privacy in Device Authentication IoT Device Security Temporal Behaviour Modelling Reboot Stability Analysis Performance Counter Fingerprinting Device Behaviour Modelling <p>This dataset provides a processed behavioural device-fingerprinting representation designed to support research on privacy-preserving identity establishment and continuous device authentication. The dataset is derived from controlled execution-response measurements collected from 11 Raspberry Pi devices operating under fixed experimental conditions.</p> <p>Measurements originate from 3 Raspberry Pi 3 Model B+ devices and 8 Raspberry Pi 4 Model B devices. Data collection was conducted in a controlled headless execution environment with fixed CPU frequency configuration, CPU core isolation, and reduced scheduling interference to ensure reproducibility of behavioural response signals.</p> <p>Each device was evaluated across 4 CPU cores and multiple reboot cycles. The dataset contains execution-dependent behavioural signals, including:</p> <ul> <li>Temperature-dependent execution responses</li> <li>GPU-related performance counter behaviour</li> <li>CPU hash execution response signals</li> <li>Pseudo-random execution response signals</li> <li>True random number generation: behavioural signals</li> </ul> <p>A comprehensive feature engineering pipeline was applied, incorporating:</p> <ul> <li>Temporal rolling statistical features</li> <li>Exponential moving dynamics</li> <li>Signed behavioural interaction features</li> <li>Robust statistical transformations</li> <li>Behavioural deviation modelling</li> <li>Representation-learning-oriented feature construction</li> </ul> <p>Unlike the CD2A dataset, which utilises a limited handcrafted feature representation focused primarily on QPU frequency behaviour, the PP-IDE dataset provides an expanded behavioural feature space designed to support:</p> <ul> <li>Privacy-preserving federated learning research</li> <li>Behavioural identity establishment modelling</li> <li>Continuous authentication system design</li> <li>Adversarial robustness evaluation</li> <li>Hardware-rooted behavioural fingerprint analysis</li> </ul> <p>Persistent hardware identifiers have been removed and replaced with stable pseudonymous device labels to preserve device-level behavioural consistency while ensuring privacy protection.</p> <p>This dataset is intended for research in behavioural device authentication, privacy-preserving machine learning, federated behavioural modelling, and security evaluation of embedded and IoT systems.</p> |
| title | PP-IDE Behavioural Device Fingerprinting Dataset for Privacy-Preserving Identity Establishment |
| topic | Behavioural Device Fingerprinting Privacy-Preserving Identity Establishment Federated Learning for IoT Security Differential Privacy in Device Authentication IoT Device Security Temporal Behaviour Modelling Reboot Stability Analysis Performance Counter Fingerprinting Device Behaviour Modelling |
| url | https://doi.org/10.5281/zenodo.19202451 |