Privacy implications of accelerometer data: a review of possible inferences <s> Keystroke Logging <s> Attacks that use side channels, such as sound and electromagnetic emanation, to infer keystrokes on physical keyboards are ineffective on smartphones without physical keyboards. We describe a new side channel, motion, on touch screen smartphones with only soft keyboards. Since typing on different locations on the screen causes different vibrations, motion data can be used to infer the keys being typed. To demonstrate this attack, we developed TouchLogger, an Android application that extracts features from device orientation data to infer keystrokes. TouchLogger correctly inferred more than 70% of the keys typed on a number-only soft keyboard on a smartphone. We hope to raise the awareness of motion as a significant side channel that may leak confidential data. <s> BIB001 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Keystroke Logging <s> Today's smartphones are shipped with various embedded motion sensors, such as the accelerometer, gyroscope, and orientation sensors. These motion sensors are useful in supporting the mobile UI innovation and motion-based commands. However, they also bring potential risks of leaking user's private information as they allow third party applications to monitor the motion changes of smartphones. In this paper, we study the feasibility of inferring a user's tap inputs to a smartphone with its integrated motion sensors. Specifically, we utilize an installed trojan application to stealthily monitor the movement and gesture changes of a smartphone using its on-board motion sensors. When the user is interacting with the trojan application, it learns the motion change patterns of tap events. Later, when the user is performing sensitive inputs, such as entering passwords on the touchscreen, the trojan application applies the learnt pattern to infer the occurrence of tap events on the touchscreen as well as the tapped positions on the touchscreen. For demonstration, we present the design and implementation of TapLogger, a trojan application for the Android platform, which stealthily logs the password of screen lock and the numbers entered during a phone call (e.g., credit card and PIN numbers). Statistical results are presented to show the feasibility of such inferences and attacks. <s> BIB002 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Keystroke Logging <s> We show that accelerometer readings are a powerful side channel that can be used to extract entire sequences of entered text on a smart-phone touchscreen keyboard. This possibility is a concern for two main reasons. First, unauthorized access to one's keystrokes is a serious invasion of privacy as consumers increasingly use smartphones for sensitive transactions. Second, unlike many other sensors found on smartphones, the accelerometer does not require special privileges to access on current smartphone OSes. We show that accelerometer measurements can be used to extract 6-character passwords in as few as 4.5 trials (median). <s> BIB003 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Keystroke Logging <s> Nowadays smartphones come embedded with multiple motion sensors, such as an accelerometer, a gyroscope and an orientation sensor. With these sensors, apps can gather more information and therefore provide end users with more functionality. However, these sensors also introduce the potential risk of leaking a user's private information because apps can access these sensors without requiring security permissions. By monitoring a device's motion, a malicious app may be able to infer sensitive information about the owner of the device. For example, related work has shown that sensitive information entered by a user on a device's touchscreen, such as numerical PINs or passwords, can be inferred from accelerometer and gyroscope data. ::: In this paper, we study these motion-based keystroke inference attacks to determine what information they need to succeed. Based on this study, we propose two novel approaches to defend against keystroke inference attacks: 1) Reducing sensor data accuracy; 2) Random keyboard layout generation. We present the design and the implementation of these two defences on the Android platform and show how they significantly reduce the accuracy of keystroke inference attacks. We also conduct multiple user studies to evaluate the usability and feasibility of these two defences. Finally, we determine the impact of the defences on apps that have legitimate reasons to access motion sensors and show that the impact is negligible. <s> BIB004 </s> Privacy implications of accelerometer data: a review of possible inferences <s> Keystroke Logging <s> Wearable technologies are today on the rise, becoming more common and broadly available to mainstream users. In fact, wristband and armband devices such as smartwatches and fitness trackers already took an important place in the consumer electronics market and are becoming ubiquitous. By their very nature of being wearable, these devices, however, provide a new pervasive attack surface threatening users privacy, among others. ::: In the meantime, advances in machine learning are providing unprecedented possibilities to process complex data efficiently. Allowing patterns to emerge from high dimensional unavoidably noisy data. ::: The goal of this work is to raise awareness about the potential risks related to motion sensors built-in wearable devices and to demonstrate abuse opportunities leveraged by advanced neural network architectures. ::: The LSTM-based implementation presented in this research can perform touchlogging and keylogging on 12-keys keypads with above-average accuracy even when confronted with raw unprocessed data. Thus demonstrating that deep neural networks are capable of making keystroke inference attacks based on motion sensors easier to achieve by removing the need for non-trivial pre-processing pipelines and carefully engineered feature extraction strategies. Our results suggest that the complete technological ecosystem of a user can be compromised when a wearable wristband device is worn. <s> BIB005
The input that users type into to their devices through touchscreens and keyboards contains highly sensitive information such as text messages, personal notes, login credentials and transaction details. Based on the observation that swipes, taps and keystrokes often correlate with distinctive hand movements of the user, it has been shown that inputs can be reconstructed using motion sensor data from handheld and wrist-worn devices BIB001  BIB002 . Some researchers have exclusively used accelerometer data for such keystroke inference attacks. Aviv et al. demonstrated that accelerometers in smartphones can be exploited to infer tap-and gesture-based input, including PINs and graphical password patterns  . Based on the same type of data, Owusu et al. were able to obtain entire sequences of text entered through a phone's touchscreen BIB003 . Through examining the source code of other existing approaches, it has been found that even multi-sensor attacks solely use acceleration information for tap detection, leading to the conclusion that defense mechanisms against these kinds of side channel attacks should focus on accelerometers BIB004 . Not only does the above imply that accelerometer data could offer sensitive insights into a user's communication and transactions: Beltramelli and Risi even warn that a user's entire technological ecosystem could be compromised when passwords are leaked through embedded sensors in consumer electronics BIB005 .