Samsung Pay, a widely-used mobile payment service, enables users to pay using just their smartphone thanks to Magnetic Secure Transmission (MST). This technology facilitates communication between smartphones and magnetic card terminals by transmitting payment tokens through magnetic waves. Intriguingly, such magnetic waves inherently produce a distinct sound pattern (calledMST sound) containing payment information, which opens up new opportunities for both potential attackers and payment users. That is, MST sound can serve either as a new side channel for attackers to eavesdrop on MST transactions or as an easily accessible communication channel that enhances the payment experience for users. Inspired by these possibilities, we aim to deeply explore the potential of MST sound across these two dimensions, presenting two frameworks with different objectives: MagSnoop and M2APay. The first is the inference framework, which accurately, robustly, and efficiently infers payment tokens by listening to MST sounds. The second is the payment framework, which helps users establish a secure communication channel between MST-supported smartphones and microphone-equipped smartphones by shielding the vulnerability inherent in MST sound. Our experiments with prototypes of these frameworks achieved high accuracy in token inference and data transmission. Furthermore, both MagSnoop and M2APay are capable of accurately decoding tokens in diverse payment environments, including noisy environments and real-world scenarios.

As various computing devices, such as smartphones, IoT devices, smart speakers etc, becomes omnipresent in our daily lives, interest in ubiquitous computing interfaces is increasing. In response to this, various studies have introduced on-surface input techniques that leverage the surface of surrounding objects as touch interfaces. However, most of them struggle to support ubiquitous interaction due to their dependency on specific hardware or environments. In this work, we propose${{\sf S \text{-}UbiTap}}$, an input method that turns any flat solid surface into a touch input space by listening to sound (i.e., with microphones already present in the commodity devices). More specifically, we develop a novel touch localization technique that leverages the physical phenomenon, calleddispersion, which is the characteristic of sound as it travels through solid surfaces, and address the challenges that limit existing acoustic-based solutions in terms of portability, accuracy, usability, robustness, scalability, and responsiveness. Our extensive experiments with a prototype of${{\sf S \text{-}UbiTap}}$show that we can support sub-centimeter accuracy on various types of surfaces with minor user calibration effort. In addition, the accuracy is maintained even when the size of the touch input space increases. In our experience with real-world users,${{\sf S \text{-}UbiTap}}$significantly improves usability and robustness, thus enabling the emergence of more exciting applications.

Samsung Pay, one of the most representative mobile payment services, allows mobile users to make payment transactions almost anywhere using only their smartphone. This is thanks to MST (Magnetic Secure Transmission) that supports communication between smartphones and payment terminals for magnetic cards by transferring payment tokens via magnetic waves. Several attack methods have targeted this new technology by eavesdropping on magnetic fields to intercept the tokens, but with the use of dedicated hardware. This paper raises new security concerns for mobile payment users in a different, yet more effective way; by introducing MagSnoop, a novel framework that infers payment tokens from listening to MST sounds generated during the activation of MST payment transactions. More specifically, we first explore the principle, causing the generation of MST sounds, and the fundamental characteristics of these sounds. We then use these observations to infer payment tokens with a high degree of accuracy, robustness, applicability, and data efficiency. Our experiments with a prototype of MagSnoop demonstrate that it can support high accuracy in token inference (more than 77.8%). In addition, MagSnoop can maintain a reasonable level of accuracy regardless of the payment environments (e.g., 69.2% with a noise level of 50 dBA) and even in the real world (an inference success rate of 68.0% with 15 real-world users).