Sensor Guardian: prevent privacy inference on Android sensors

Bai, Xiaolong; Yin, Jie; Wang, Yu-Ping

doi:10.1186/s13635-017-0061-8

Cited by 14 publications

(11 citation statements)

References 35 publications

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“…Depending on the assumed sensitivity of their data and other factors, sensors in IoT devices enjoy different levels of legal and technical protection. Specific protection mechanisms and access policies can vary significantly between devices of different manufacturers [6]. Further insights into the sensitivity and protection of different types of sensor data are provided in Sect.…”

Section: Sensors In Iot Devices 21 Classificationmentioning

confidence: 99%

“…While cameras, microphones and navigation systems like GPS are commonly perceived as privacy-sensitive [26,32] and require explicit user permission in current mobile operating systems [6], many inconspicuous sensors such as accelerometers, gyroscopes and barometers are less well-understood in terms of their privacy implications, and also less protected [57]. Countless embedded sensors in consumer electronics, such as smartphones, smartwatches, and smart home appliances, can be freely accessed by various possibly untrusted parties ranging from device manufacturers [48] and service providers [59] to third-party apps installed on mobile devices [6] and even website operators [8].…”

Section: Introductionmentioning

confidence: 99%

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Unexpected Inferences from Sensor Data: A Hidden Privacy Threat in the Internet of Things

Kröger

2019

IFIP Advances in Information and Communication Technology

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A growing number of sensors, embedded in wearables, smart electric meters and other connected devices, is surrounding us and reaching ever deeper into our private lives. While some sensors are commonly regarded as privacysensitive and always require user permission to be activated, others are less protected and less worried about. However, experimental research findings indicate that many seemingly innocuous sensors can be exploited to infer highly sensitive information about people in their vicinity. This paper reviews existing evidence from the literature and discusses potential implications for consumer privacy. Specifically, the analysis reveals that certain insufficiently protected sensors in smart devices allow inferences about users' locations, activities and real identities, as well as about their keyboard and touchscreen inputs. The presented findings call into question the adequacy of current sensor access policies. It is argued that most data captured by smart consumer devices should be classified as highly sensitive by default. An introductory overview of sensors commonly found in these devices is also provided, along with a proposed classification scheme.

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Section: Sensors In Iot Devices 21 Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unexpected Inferences from Sensor Data: A Hidden Privacy Threat in the Internet of Things

Kröger

2019

IFIP Advances in Information and Communication Technology

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“…Over the last decade, due to the accelerated proliferation in the smartphone market users demand different functionalities that meet their needs, and they use smartphones not only to make phone calls and send messages, but also for multiple activities [51][52][53].…”

Section: Magnetic Sensormentioning

confidence: 99%

Smartphones Magnetic Sensors within Physics Lab

Escobar¹,

Ramirez-Vazquez²,

González-Rubio³

et al. 2018

Preprint

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Current smartphones incorporate different types of sensors that allow us to know our spatial position, they give us information about pressure, speed, acceleration, time, acoustic level, and other different physical magnitudes. These smartphones measure each component of the magnetic field, bearing in mind that any current perpendicular to a magnetic field produces a small potential difference, transversal to the said current, being this voltage easily measurable by Hall sensors. With the implementation of three Hall sensors, and an appropriate app, we can measure the three components of the magnetic field vector, and with this we can obtain information and deduce properties of the physical systems considered. In this paper we are exploring the use of smartphones in a physics laboratory for freshman students. To do this, we have measured, using Hall sensors, the magnetic field created by a linear quadrature, and we have obtained, first of all, its dependence on the distance between the quadrupole and the magnetic sensor. The second purpose of this work is to show that the laboratory is a powerful tool that increases the significant learning of freshman students through advanced technological tools.

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“…Today, all sorts of mobile devices, including smartphones, tablet PCs, smartwatches, digital cameras, wearable fitness trackers, game controllers, and virtual reality headsets, are equipped with built-in microelectromechanical accelerometers [1]. Studies even suggest that accelerometers are the most widely used sensor in wearable devices [2] and also the sensor that is most frequently accessed by mobile apps [3].…”

Section: Introductionmentioning

confidence: 99%

“…While some sensors, such as microphones, cameras and GPS, are widely perceived as privacy-sensitive [4,5] and require explicit user permission to be activated in current mobile operating systems [3], accelerometers are less well-understood in terms of their privacy implications, and also much less protected [6,7]. Even scholarly literature has largely ignored potential issues in this field, with researchers describing accelerometer data as "not particularly sensitive" [8] or even "privacy preserving" [9].…”

Section: Introductionmentioning

confidence: 99%

Privacy implications of accelerometer data

Kröger

Raschke

Bhuiyan

2019

Proceedings of the 3rd International Conference on Cryptography, Security and Privacy

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Accelerometers are sensors for measuring acceleration forces. They can be found embedded in many types of mobile devices, including tablet PCs, smartphones, and smartwatches. Some common uses of built-in accelerometers are automatic image stabilization, device orientation detection, and shake detection. In contrast to sensors like microphones and cameras, accelerometers are widely regarded as not privacy-intrusive. This sentiment is reflected in protection policies of current mobile operating systems, where third-party apps can access accelerometer data without requiring security permission. It has been shown in experiments, however, that seemingly innocuous sensors can be used as a side channel to infer highly sensitive information about people in their vicinity. Drawing from existing literature, we found that accelerometer data alone may be sufficient to obtain information about a device holder's location, activities, health condition, body features, gender, age, personality traits, and emotional state. Acceleration signals can even be used to uniquely identify a person based on biometric movement patterns and to reconstruct sequences of text entered into a device, including passwords. In the light of these possible inferences, we suggest that accelerometers should urgently be re-evaluated in terms of their privacy implications, along with corresponding adjustments to sensor protection mechanisms.

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Sensor Guardian: prevent privacy inference on Android sensors

Cited by 14 publications

References 35 publications

Unexpected Inferences from Sensor Data: A Hidden Privacy Threat in the Internet of Things

Unexpected Inferences from Sensor Data: A Hidden Privacy Threat in the Internet of Things

Smartphones Magnetic Sensors within Physics Lab

Privacy implications of accelerometer data

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