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Miao Lin

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4

AAAI Conference 2016 Conference Paper

Cold-Start Heterogeneous-Device Wireless Localization

  • Vincent W. Zheng
  • Hong Cao
  • Shenghua Gao
  • Aditi Adhikari
  • Miao Lin
  • Kevin Chang

In this paper, we study a cold-start heterogeneous-device localization problem. This problem is challenging, because it results in an extreme inductive transfer learning setting, where there is only source domain data but no target domain data. This problem is also underexplored. As there is no target domain data for calibration, we aim to learn a robust feature representation only from the source domain. There is little previous work on such a robust feature learning task; besides, the existing robust feature representation proposals are both heuristic and inexpressive. As our contribution, we for the first time provide a principled and expressive robust feature representation to solve the challenging cold-start heterogeneous-device localization problem. We evaluate our model on two public real-world data sets, and show that it significantly outperforms the best baseline by 23. 1%–91. 3% across four pairs of heterogeneous devices.

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IJCAI Conference 2015 Conference Paper

Mobility Profiling for User Verification with Anonymized Location Data

  • Miao Lin
  • Hong Cao
  • Vincent Zheng
  • Kevin Chen-Chuan Chang
  • Shonali Krishnaswamy

Mobile user verification is to authenticate whether a given user is the legitimate user of a smartphone device. Unlike the current methods that commonly require users active cooperation, such as entering a short pin or a one-stroke draw pattern, we propose a new passive verification method that requires minimal imposition of users through modelling users subtle mobility patterns. Specifically, our method computes the statistical ambience features on WiFi and cell tower data from location anonymized data sets and then we customize Hidden Markov Model (HMM) to capture the spatialtemporal patterns of each user’s mobility behaviors. Our learned model is subsequently validated and applied to verify a test user in a time-evolving manner through sequential likelihood test. Experimentally, our method achieves 72% verification accuracy with less than a day’s data and a detection rate of 94% of illegitimate users with only 2 hours of selected data. As the first verification method that models users’ mobility pattern on locationanonymized smartphone data, our achieved result is significant showing the good possibility of leveraging such information for live user authentication.

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