# A Survey of Privacy Vulnerabilities of Mobile Device Sensors

arXiv: https://arxiv.org/abs/2106.10154 · HTML: https://mlsanity.com/paper/2106.10154/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2021.

## Built on by (most cited first, 18 of 18)

- [An Overview of Privacy-enhancing Technologies in Biometric Recognition](https://mlsanity.com/paper/2206.10465/) (2022)
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- [The Eye of Sherlock Holmes: Uncovering User Private Attribute Profiling via Vision-Language Model Agentic Framework](https://mlsanity.com/paper/2505.19139/) (2025)
- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation](https://mlsanity.com/paper/2206.02502/) (2022)
- [TypeFormer: Transformers for Mobile Keystroke Biometrics](https://mlsanity.com/paper/2212.13075/) (2022)
- [Mobile Keystroke Biometrics Using Transformers](https://mlsanity.com/paper/2207.07596/) (2022)
- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)](https://mlsanity.com/paper/2210.03072/) (2022)
- [Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation](https://mlsanity.com/paper/2311.06000/) (2023)
- [Mobile Behavioral Biometrics for Passive Authentication](https://mlsanity.com/paper/2203.07300/) (2022)
- [BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection](https://mlsanity.com/paper/2207.13394/) (2022)
- [AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours](https://mlsanity.com/paper/2309.06308/) (2023)
- [KVC-onGoing: Keystroke Verification Challenge](https://mlsanity.com/paper/2412.20530/) (2024)
- [FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities](https://mlsanity.com/paper/2502.15839/) (2025)
- [Beyond Permissions: Investigating Mobile Personalization with Simulated Personas](https://mlsanity.com/paper/2511.01336/) (2025)

## Abstract (arXiv)

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions a represented for further advancements in the field.