AT&T Labs Research - Miluzzo, Emiliano

I am an experimental researcher working at the intersection of mobile systems and applied machine learning in the "Mobile and Pervasive Systems Research" group. My research interests include mobile, pervasive, distributed computing, mobile sensing systems, and big data analysis. I hold a Ph.D. in Computer Science from Dartmouth College, and a M.Sc. and B.Sc. in Electrical Engineering from University of Rome La Sapienza, Italy.

Projects
Connecting Your World, The need to be connected is greater than ever, and AT&T Researchers are creating new ways for people to connect with one another and with their environments, whether it's their home, office, or car.

(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 2012 , 2012-06-25.

{When we think of mobile cloud computing today, we typically refer to empowering mobile devices -- in particular smartphones and tablets -- with the capabilities of stationary resources residing in giant data centers. But what happens when these mobile devices become as powerful as our personal computers or more? This paper presents our vision of a future in which mobile devices become a core component of mobile cloud computing architectures. We envision a world where mobile devices will be capable of forming mobile clouds, or mClouds, to accomplish tasks locally without relying, when possible, on costly and, sometimes, inefficient backend communication. We discuss a possible mClouds architecture, its benefits and tradeoffs, and the user incentive scheme to support the mCloud design.}

(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.

{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.}

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