# Compromising location privacy through Wi-Fi RSSI tracking

## TL;DR

Wi-Fi signal strength can reveal user locations and habits, even when other data is restricted, posing privacy risks.

## Contribution

Demonstrates how Wi-Fi RSSI can be used to infer user presence and semantic context with high accuracy.

## Key findings

- RSSI data can infer user presence at common locations over time.
- Combining RSSI with time data achieves over 90% accuracy in predicting location context.
- Lack of RSSI protection increases fingerprinting and privacy risks.

## Abstract

The widespread availability of wireless networking, such as Wi-Fi, has led to the pervasiveness of always connected mobile devices. These devices are provided with several sensors that allow the collection of large amounts of data, which pose a threat to personal privacy. It is well known that Wi-Fi connectivity information (e.g. BSSID) can be used for inferring user locations. This has caused the imposition of limitations to the access to such data in mobile devices. However, other sources of information about wireless connectivity are available, such as the Received Signal Strength Indicator (RSSI). In this work, we show that RSSI can be used to infer the presence of a user at common locations throughout time. This information can be correlated with other features, such as the hour of the day, to further learn semantic context about such locations with a prediction performance above 90%. Our analysis shows the privacy implications of inferring user locations through Wi-Fi RSSI, but also emphasizes the fingerprinting risk that results from the lack of protection when accessing RSSI measurements.