May 01, 2017

Extracting Mobile User Behavioral Similarity via Cell-Level Location Trace

  • Cao J.
  • Chen S.
  • Kennedy W.
  • Kim N.
  • Zhang Y.

We study mobile user behavior from cell-level location trace (CLLT). Since CLLT contains no GPS coordinates of mobile users, we infer approximate user locations from the cell locations they visit. We build upon the vast literature on user behavior analysis and demonstrate the ability to extract user behavior in the absence of the more precise GPS information. We focus on the ``leisure time'' behavior, i.e. activities outside home and office. In particular, we compare pairs of users and study their similarity or the lack of it. Such similarity comparison can be done directly using the users' actual cell-level locations and the times of their visits. We observe that a user's behavior on different days tends to be more similar to oneself than to others. We then compare users in terms of their activities irrespective of physical locations. We develop the notion of {em semantic cell type} which classifies the cells according to the consistency of points of interest within the cells. In this way, we can compare two users based on the type of cells they visit and extract similarity from there. As a result, from our understanding of the general profiles of the cells and the users, we are able to differentiate user behavior and cluster them in a meaningful way.

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Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

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How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...