Incentive schemes for privacy-sensitive consumers

Chong Huang, Lalitha Sankar, Anand D. Sarwate

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Businesses (retailers) often offer personalized advertisements (coupons) to individuals (consumers). While proving a customized shopping experience, such coupons can provoke strong reactions from consumers who feel their privacy has been violated. Existing models for privacy try to quantify privacy risk but do not capture the subjective experience and heterogeneous expression of privacy-sensitivity. We use a Markov decision process (MDP) model for this problem. Our model captures different consumer privacy sensitivities via a time-varying state, different coupon types via an action set for the retailer, and a cost for perceived privacy violations that depends on the action and state. The simplest version of our model has two states (“Normal” and “Alerted”), two coupons (targeted and untargeted), and consumer behavior dynamics known to the retailer.We show that the optimal coupon-offering strategy for a retailer that wishes to minimize its expected discounted cost is a stationary threshold-based policy. The threshold is a function of all model parameters: the retailer offers a targeted coupon if their belief that the consumer is in the “Alerted” state is below the threshold. We extend our model and results to consumers with multiple privacy-sensitivity states as well as coupon-dependent state transition probabilities.

Original languageEnglish (US)
Title of host publicationDecision and Game Theory for Security - 6th International Conference, GameSec 2015, Proceedings
EditorsM.H.R. Khouzani, Emmanouil Panaousis, George Theodorakopoulos
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319255934
StatePublished - 2015
Event6th International Conference on Decision and Game Theory for Security, GameSec 2015 - London, United Kingdom
Duration: Nov 4 2015Nov 5 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th International Conference on Decision and Game Theory for Security, GameSec 2015
Country/TerritoryUnited Kingdom


  • Markov decision processes
  • Optimal policies
  • Privacy
  • Retailer-consumer interaction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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