att_abstract={{This paper shows that the location of screen taps on modern smartphones and tablets can be identified from accelerometer and gyroscope readings. Our findings have serious implications, as we demonstrate that an attacker can launch a background process on commodity smartphones and tablets, and silently monitor the user�s inputs, such as keyboard presses and icon taps. While precise tap detection is non-trivial, requiring machine learning algorithms to identify fingerprints of closely spaced keys, sensitive sensors on modern devices aid the process. We present TapPrints, a framework for inferring the location of taps on mobile device touch- screens using motion sensor data combined with machine learning analysis. By running tests on two different off-the-shelf smartphones and a tablet computer we show that identifying tap locations on the screen and inferring English letters could be done with up to 90% and 80% accuracy, respectively. By optimizing the core tap detection capability with additional information, such as contextual priors, we are able to further magnify the core threat.}},
	att_authors={em269d, av8693, sb799t},
	att_copyright_notice={{(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in The 10th International Conference on Mobile Systems, Applications and Services (MobiSys 2012) {{, 2012-06-26}}.
	att_tags={Smartphone sensing,  tap location inference, smartphone and tablet security},
	author={Emiliano Miluzzo and Alexander Varshavsky and Suhrid Balakrishnan and Romit Roy Choudhury},
	institution={{The 10th International Conference on Mobile Systems,
Applications and Services (MobiSys 2012)}},
	title={{TapPrints: Your Finger Taps have Fingerprints}},