Designing Incentive Schemes for Privacy-Sensitive Users

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• Prof. Lalitha Sankar, Arizona State University
• Thursday 05 November 2015, 11:00-12:00
• EEE Department, Room 503.

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Abstract: Businesses (retailers) often wish to offer personalized advertisements (coupons) to individuals (consumers), but run the risk of strong reactions from consumers who may want a customized shopping experience but feel their privacy has been violated. Existing models for privacy such as differential privacy or information theory try to quantify privacy risk but do not capture the subjective experience and heterogeneous expression of privacy-sensitivity. We propose a Markov decision process (MDP) model to capture: (i) different consumer privacy sensitivities via a time-varying state; (ii) different coupon types (action set) for the retailer with appropriate costs for each offering; and (iii) the action-and-state-dependent cost for perceived privacy violations. For a simple model with two states (“Normal” and “Alerted”), two coupons (targeted and untargeted), and consumer behavior statistics known to the retailer, we show that a stationary threshold-based policy is the optimal coupon-offering strategy for a retailer that wishes to minimize its expected discounted cost. The threshold is a function of all model parameters such that the retailer offers a targeted coupon only if their belief that the consumer is in the “Alerted” state is below the threshold. We extend this two-state model to consumers with multiple privacy-sensitivity states as well as coupon-dependent state transition probabilities. Furthermore, we study the case with imperfect (noisy) cost feedback from consumers and uncertain initial belief state.

Speaker Bio: Lalitha Sankar received the B.Tech degree from the Indian Institute of Technology, Bombay, the M.S. degree from the University of Maryland, and the Ph.D degree from Rutgers University in 2007. She is presently an Assistant Professor in the ECEE department at Arizona State University. Prior to this, she was an Associate Research Scholar at Princeton University. Following her doctorate, Dr Sankar was a recipient of a three year Science and Technology Teaching Postdoctoral Fellowship from the Council on Science and Technology at Princeton University. Prior to her doctoral studies, she was a Senior Member of Technical Staff at AT&T Shannon Laboratories. Her research interests include information privacy and cyber-security in distributed and cyber-physical systems, network information theory and its applications to model and study large data systems. She received the NSF CAREER award in 2014. She received the IEEE Globecom 2011 Best Paper Award for her work on privacy of side-information in multi-user data systems. For her doctoral work, she received the 2007-2008 Electrical Engineering Academic Achievement Award from Rutgers University.

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