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 (21)

• Deep Learning with Differential Privacy
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Geo-Indistinguishability: Differential Privacy for Location-Based Systems
• Learning Differentially Private Recurrent Language Models
• Technical Privacy Metrics: a Systematic Survey
• A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
• A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
• DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
• PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy
• Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health
• Emotion-Recognition Using Smart Watch Sensor Data: Mixed-Design Study
• Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining
• From t-closeness to differential privacy and vice versa in data anonymization
• Differentially Private Learning Needs Better Features (or Much More Data)
• Utilizing Noise Addition for Data Privacy, an Overview
• BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks
• Privacy Leakage in Mobile Computing: Tools, Methods, and Characteristics
• Mobile Communication Signatures of Unemployment
• Exploiting Complexity in Pen- and Touch-based Signature Biometrics
• GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning
• DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks

Cited by in corpus (5)

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• AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours
• 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