PowerSpy: Location Tracking using Mobile Device Power Analysis
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Abstract (arXiv)
Modern mobile platforms like Android enable applications to read aggregate power usage on the phone. This information is considered harmless and reading it requires no user permission or notification. We show that by simply reading the phone's aggregate power consumption over a period of a few minutes an application can learn information about the user's location. Aggregate phone power consumption data is extremely noisy due to the multitude of components and applications that simultaneously consume power. Nevertheless, by using machine learning algorithms we are able to successfully infer the phone's location. We discuss several ways in which this privacy leak can be remedied.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Feb 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2021Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Surveycited by 4
- 2025RAID: An In-Training Defense against Attribute Inference Attacks in Recommender Systemscited by 1
- 2026Rethinking Side-Channel Analysis: Automated Discovery and Analysis of Side-Channel Leakage with LLM-Assisted Agentscited by 0
- 2026Rethinking Side-Channel Analysis: Automated Discovery and Analysis of Side-Channel Leakage with LLM-Assisted Agents17 May 2026
- 2025RAID: An In-Training Defense against Attribute Inference Attacks in Recommender Systems15 Apr 2025
- 2021Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey13 May 2021