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We present the first acoustic side-channel attack that recovers what users type on the virtual keyboard of their touch-screen smartphone or tablet. When a user taps the screen with a finger, the tap generates a sound wave that propagates on the screen surface and in the air. We found the devices microphone(s) can recover this wave and hear the fingers touch, and the waves distortions are characteristic of the taps location on the screen. Hence, by recording audio through the built-in microphone(s), a malicious app can infer text as the user enters it on their device. We evaluate the effectiveness of the attack with 45 participants in a real-world environment on an Android tablet and an Android smartphone. For the tablet, we recover 61% of 200 4-digit PIN-codes within 20 attempts, even if the model is not trained with the victims data. For the smartphone, we recover 9 words of size 7--13 letters with 50 attempts in a common side-channel attack benchmark. Our results suggest that it not always sufficient to rely on isolation mechanisms such as TrustZone to protect user input. We propose and discuss hardware, operating-system and application-level mechanisms to block this attack more effectively. Mobile devices may need a richer capability model, a more user-friendly notification system for sensor usage and a more thorough evaluation of the information leaked by the underlying hardware.
Design companies often outsource their integrated circuit (IC) fabrication to third parties where ICs are susceptible to malicious acts such as the insertion of a side-channel hardware trojan horse (SCT). In this paper, we present a framework for des
The interplay between security and reliability is poorly understood. This paper shows how triple modular redundancy affects a side-channel attack (SCA). Our counterintuitive findings show that modular redundancy can increase SCA resiliency.
GPUs are increasingly being used in security applications, especially for accelerating encryption/decryption. While GPUs are an attractive platform in terms of performance, the security of these devices raises a number of concerns. One vulnerability
We demonstrate the feasibility of database reconstruction under a cache side-channel attack on SQLite. Specifically, we present a Flush+Reload attack on SQLite that obtains approximate (or noisy) volumes of range queries made to a private database. W
Deep learning has been widely applied in many computer vision applications, with remarkable success. However, running deep learning models on mobile devices is generally challenging due to the limitation of computing resources. A popular alternative