# PowerSpy: Location Tracking using Mobile Device Power Analysis

arXiv: https://arxiv.org/abs/1502.03182 · HTML: https://mlsanity.com/paper/1502.03182/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2015.

## Built on by (most cited first, 3 of 3)

- [Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey](https://mlsanity.com/paper/2105.06300/) (2021)
- [RAID: An In-Training Defense against Attribute Inference Attacks in Recommender Systems](https://mlsanity.com/paper/2504.11510/) (2025)
- [Rethinking Side-Channel Analysis: Automated Discovery and Analysis of Side-Channel Leakage with LLM-Assisted Agents](https://mlsanity.com/paper/2605.17406/) (2026)

## 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.