PinMe: Tracking a Smartphone User around the World (1802.01468v1)

Abstract: With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.

• The paper presents a novel method that leverages sensor and auxiliary data to infer user locations without GPS.
• It combines non-sensory inputs like IP addresses and time zones with sensor data to enable accurate tracking across various transport modes.
• Evaluation shows that PinMe achieves GPS-comparable accuracy with low error margins, highlighting critical privacy risks.

Evaluating Privacy Risks in Non-GPS Location Tracking: The PinMe Approach

The paper "PinMe: Tracking a Smartphone User around the World" by Arsalan Mosenia, Xiaoliang Dai, Prateek Mittal, and Niraj K. Jha, presents an innovative framework for tracking smartphone users without relying on traditional GPS services. This investigation addresses the critical concern of location privacy in an era dominated by ubiquitous smartphone usage.

Unlike prior efforts that required a predefined dataset or initial knowledge about the user's location, PinMe employs a novel technique that exploits various types of sensor and non-sensor data from the smartphone combined with publicly available auxiliary information. This approach broadens the capabilities of privacy-compromising attacks by eliminating fundamental limitations that hampered previous attempts at achieving high-accuracy location estimation without GPS.

Key Contributions and Methodology

The research introduces the PinMe mechanism, which capitalizes on a variety of seemingly innocuous data sources for accurate location estimation. These sources include:

• Non-sensory Data: Device's timezone, network status, and IP address.
• Sensory Data: Smartphone sensors such as accelerometers, magnetometers (heading), and barometers (air pressure).
• Auxiliary Information: Publicly accessible datasets such as elevation maps, weather reports, airports' specifications, trains' heading datasets, and transportation timetables.

PinMe is capable of deducing the user's location during multifarious activities: walking, traveling on a train, plane, or car. This versatility sets it apart from earlier models focused on single activity analysis. Moreover, the framework dispenses with the need for high sampling rates or tedious, extensive route-specific datasets, considerably enhancing its practical applicability and reducing detectability.

Evaluation and Results

Rigorous evaluations demonstrated PinMe's efficacy using real-world data from various smartphones. The framework consistently managed to infer location paths with performance metrics comparable to GPS-derived data. Noteworthy is the algorithm's low error margin when estimating driving pathways, relying on metrics that remained stable even with sensor noise or reduced sampling rates. PinMe's robustness and accuracy in real-world settings underscore its potential to operate as a covert, highly scalable attack tool against unsuspecting users.

Implications and Future Directions

The implications of this research are twofold: it reveals previously unappreciated vulnerabilities in smartphone location privacy and prompts reconsideration of safeguards and permissions around data usage by applications. PinMe exemplifies how innocuous-seeming data can compromise user privacy through inferential reconstruction of location.

In terms of practical future developments, the insights from PinMe's approach could contribute to the design of alternative navigation systems, potentially benefiting autonomous vehicle operations by providing a backup to GPS. This is particularly relevant considering ongoing concerns about GPS vulnerabilities to spoofing or jamming interference.

Defensive Strategies

To mitigate the risks posed by such privacy-invasive mechanisms, the paper suggests potential countermeasures. These include adaptive sampling rates for sensors, enhanced privacy models for permission requests, and sensor data manipulation algorithms. Nonetheless, each measure comes with trade-offs, reflecting the persistent challenge of balancing utility and privacy.

Conclusion

"PinMe: Tracking a Smartphone User around the World" offers a compelling exploration of non-GPS tracking possibilities, shedding light on subtle yet significant privacy threats inherent in modern smart devices. This work underscores an urgent need for advancing security protocols and privacy frameworks to safeguard sensitive user information in an ever-changing technological landscape.