A proof-of-concept for a new type of privacy attack, dubbed “calibration fingerprinting,” uses data from Apple iPhone sensors to construct a globally unique fingerprint for any given mobile user. Researchers said that this provides an unusually effective means to track people as they browse across the mobile web and move between apps on their phones.
Further, the approach also affects Pixel phones from Google, which run on Android.
A research team from the University of Cambridge in the UK released their findings this week, showing that data gathered from the accelerometer, gyroscope and magnetometer sensors found in the smartphones can be used to generate the calibration fingerprint in less than a second – and that it never changes, even after a factory reset.
The attack also can be launched by any website a person visits via a mobile browser, or any app, without needing explicit confirmation or consent from the target.
In Apple’s case, the issue results from a weakness in iOS 12.1 and earlier, so iPhone users should update to the latest OS version as soon as possible. Google has not yet addressed the problem, according to the researchers.
Next-Gen Device Fingerprinting
A device fingerprint allows websites to detect return visits or track users, and in its innocuous form, can be used to protect against identity theft or credit-card fraud; advertisers often also rely on this to build a user profile to serve targeted ads.
However, any iOS devices with the iOS version below 12.2, including the latest iPhone XS, iPhone XS Max and iPhone XR, it’s possible to get around those protections, by taking advantage of the fact that motion sensors used in modern smartphones use something called microfabrication to emulate the mechanical parts found in traditional sensor devices, according to the paper.
“MEMS sensors are usually less accurate than their optical counterparts due to various types of error,” the team said. “In general, these errors can be categorized as deterministic and random. Sensor calibration is the process of identifying and removing the deterministic errors from the sensor.”
Websites and apps can access the data from sensors, without any special permission from the users. In analyzing this freely accessible information, the researchers found that it was possible to infer the per-device factory calibration data which manufacturers embed into the firmware of the smartphone to compensate for these systematic manufacturing errors. That calibration data can then be used as the fingerprint, because despite perceived homogeneity, every Apple iPhone is just a little bit different – even if two devices are from the same manufacturing batch.
“We found that the gyroscope and magnetometer on iOS devices are factory-calibrated and the calibration data differs from device-to-device,” the researchers said. “Extracting the calibration data typically takes less than one second and does not depend on the position or orientation of the device.”
To create a globally unique calibration footprint requires adding in a little more information, however, for instance from traditional fingerprinting sources.
“We demonstrated that our approach can produce globally unique fingerprints for iOS devices from an installed app — around 67 bits of entropy for the iPhone 6S,” they said. “Calibration fingerprints generated by a website are less unique (~42 bits of entropy for the iPhone 6S), but they are orthogonal to existing fingerprinting techniques and together they are likely to form a globally unique fingerprint for iOS devices.”
A longitudinal study also showed that the calibration fingerprint, which the researchers dubbed “SensorID,” doesn’t change over time or vary with conditions.
“We have not observed any change in the SensorID of our test devices in the past half year,” they wrote. “Our dataset includes devices running iOS 9/10/11/12. We have tested compass calibration, factory reset, and updating iOS (up until iOS 12.1); the SensorID always stays the same. We have also tried measuring the sensor data at different locations and under different temperatures; we confirm that these factors do not change the SensorID either.”
In terms of how applicable the SensorID approach is, the research team found that both mainstream browsers (Safari, Chrome, Firefox and Opera) and privacy-enhanced browsers (Brave and Firefox Focus) are vulnerable to the attack, even with the fingerprinting protection mode turned on.
Further, motion sensor data is accessed by 2,653 of the Alexa top 100,000 websites, the research found, including more than 100 websites exfiltrating motion-sensor data to remote servers.
“This is troublesome since it is likely that the SensorID can be calculated with exfiltrated data, allowing retrospective device fingerprinting,” the researchers wrote.
However, it’s possible to mitigate the calibration fingerprint attack on the vendor side by adding uniformly distributed random noise to the sensor outputs before calibration is applied at the factory level – something Apple did starting with iOS 12.2.
“Alternatively, vendors could round the sensor outputs to the nearest multiple of the nominal gain,” the paper said.
“This could help protect Android devices and iOS devices that no longer receive updates from Apple,” according to the paper.
Google Pixel Devices
Although most of the research focused on iPhone, Apple is not the only vendor affected: The team found that the accelerometer of Google Pixel 2 and Pixel 3 can also be fingerprinted by the approach.
That said, the fingerprint has less individual entropy and is unlikely to be globally unique – meaning other kinds of fingerprinting data would also need to be gathered for full device-specific tracking.
Also, the paper noted that other Android devices that are also factory calibrated might be vulnerable but were outside the scope of testing.
While Apple addressed the issue, Google, which was notified in December about the attack vector, is still in the process of “investigating this issue,” according to the paper.
Threatpost has reached out to the internet giant for comment.
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