# Mobile Sensor Data Anonymization

arXiv: https://arxiv.org/abs/1810.11546 · HTML: https://mlsanity.com/paper/1810.11546/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 30 of 70)

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- [Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data](https://mlsanity.com/paper/2403.01133/) (2024)
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- [Generating Sparse Counterfactual Explanations For Multivariate Time Series](https://mlsanity.com/paper/2206.00931/) (2022)
- [MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels](https://mlsanity.com/paper/2404.01958/) (2024)
- [Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework](https://mlsanity.com/paper/2508.03989/) (2025)
- [Dynamic Black-box Backdoor Attacks on IoT Sensory Data](https://mlsanity.com/paper/2511.14074/) (2025)
- [Self-Supervised WiFi-Based Activity Recognition](https://mlsanity.com/paper/2104.09072/) (2021)
- [(Un)fair devices: Moving beyond AI accuracy in personal sensing](https://mlsanity.com/paper/2303.15585/) (2023)
- [Large Language Model-Guided Semantic Alignment for Human Activity Recognition](https://mlsanity.com/paper/2410.00003/) (2024)
- [Delta: A Cloud-assisted Data Enrichment Framework for On-Device Continual Learning](https://mlsanity.com/paper/2410.18378/) (2024)
- [MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT](https://mlsanity.com/paper/2411.12126/) (2024)
- [Privacy Assessment of Federated Learning using Private Personalized Layers](https://mlsanity.com/paper/2106.08060/) (2021)
- [Anonymization for Skeleton Action Recognition](https://mlsanity.com/paper/2111.15129/) (2021)
- [Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease](https://mlsanity.com/paper/2410.21326/) (2024)
- [DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs](https://mlsanity.com/paper/2507.13737/) (2025)
- [RAVEN: Query-Guided Representation Alignment for Question Answering over Audio, Video, Embedded Sensors, and Natural Language](https://mlsanity.com/paper/2505.17114/) (2025)
- [The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification](https://mlsanity.com/paper/2603.20352/) (2026)
- [PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference](https://mlsanity.com/paper/2211.06716/) (2022)
- [MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective](https://mlsanity.com/paper/2405.14981/) (2024)
- [DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification](https://mlsanity.com/paper/2501.18116/) (2025)
- [HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2512.10807/) (2025)
- [Enhancing Efficiency in Multidevice Federated Learning through Data Selection](https://mlsanity.com/paper/2211.04175/) (2022)
- [On Neural Inertial Classification Networks for Pedestrian Activity Recognition](https://mlsanity.com/paper/2502.17520/) (2025)
- [HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets](https://mlsanity.com/paper/2503.13542/) (2025)
- [Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning](https://mlsanity.com/paper/2505.06454/) (2025)

## Abstract (arXiv)

Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%.