A Survey of Privacy Vulnerabilities of Mobile Device Sensors

Abstract

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.

References in corpus (6)

• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health
• Differentially Private Learning Needs Better Features (or Much More Data)
• BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks
• Privacy Leakage in Mobile Computing: Tools, Methods, and Characteristics
• GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning

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• Children Age Group Detection based on Human-Computer Interaction and Time Series Analysis
• Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics
• Beyond Permissions: Investigating Mobile Personalization with Simulated Personas