HCCRJun 18, 2021

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

arXiv:2106.10154v172 citations
Originality Synthesis-oriented
AI Analysis

It addresses privacy risks for mobile device users by synthesizing existing research, but it is incremental as it surveys rather than introduces new findings.

This survey examines the privacy vulnerabilities of mobile device sensors, detailing the types of personal data that can be extracted and reviewing metrics and methods to protect sensitive information while maintaining data utility.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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