Mobile Sensing: Efficient Sampling and Privacy Concerns

Speaker: 

Jayakrishnan Unnikrishnan

Affiliation: 

Ecole Polytechnique Federale de Lausanne
School of Computer and Communication Sciences
EPFL-IC-LCAV
Station 14
CH-1015 Lausanne
Switzerland

Time: 

Monday, 15 December 2014, 10:30 to 12:00

Venue: 

Organisers: 

Abstract: Sensing of spatial fields is traditionally studied in a setting where static sensors take measurements of the spatial field at their locations. However many modern applications like citizen sensing and robotic sensing employ moving sensors for spatial sensing. This emerging paradigm of mobile sensing requires us to rethink the classical notions of sampling and privacy. In this talk I will focus on the following aspects.

(1) A sampling theory for mobile sensing: We introduce the notion of path density, defined as the total distance traveled by the mobile sensors per unit spatial volume. We design sensor trajectories that are efficient in terms of path density for sampling spatially bandlimited fields, and obtain fundamental limits on the path density of mobile sensor trajectories that admit stable sampling. These limits are analogous to Landau-Nyquist rates for classical sampling (joint work with M. Vetterli, J.L. Romero, K. Grochenig).

(2) Privacy of mobility statistics: How private are anonymized mobility statistics? We study the de-anonymization of anonymized mobility statistics as a hypothesis testing problem, and identify the optimal scheme for matching anonymized location histograms to auxiliary observations of the users' locations. We apply the scheme to datasets of Wi-Fi traces, call data records, and web browsing histories to highlight the privacy concerns in collecting location information in citizen sensing schemes (joint work with F. Naini, P. Thiran, M. Vetterli).

In sum, citizen sensing opens up possibilities for efficient spatial sampling at the risk of raising privacy issues. I will quantify both aspects in this talk and present new algorithms and demonstrate their usefulness by applying them on real data.

Bio: Jayakrishnan Unnikrishnan received the B.Tech. degree in electrical engineering from the Indian Institute of Technology, Madras in 2005 and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2007 and 2010, respectively.

From 2010 to 2014, he worked as a postdoctoral researcher at the School of Computer and Communication Sciences, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland. His current research interests include signal processing, detection and estimation theory, and information theory.

Dr. Unnikrishnan is a recipient of the Vodafone Graduate Fellowship Award from the University of Illinois at Urbana-Champaign for 2007--2008 and the E.A. Reid Fellowship Award from the ECE department at the University of Illinois at Urbana-Champaign for 2010--2011.