TY - GEN
T1 - Incentive schemes for privacy-sensitive consumers
AU - Huang, Chong
AU - Sankar, Lalitha
AU - Sarwate, Anand D.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Markov decision processes
KW - Optimal policies
KW - Privacy
KW - Retailer-consumer interaction
UR - http://www.scopus.com/inward/record.url?scp=84958537805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958537805&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-25594-1_21
DO - 10.1007/978-3-319-25594-1_21
M3 - Conference contribution
AN - SCOPUS:84958537805
SN - 9783319255934
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 358
EP - 369
BT - Decision and Game Theory for Security - 6th International Conference, GameSec 2015, Proceedings
A2 - Khouzani, M.H.R.
A2 - Panaousis, Emmanouil
A2 - Theodorakopoulos, George
PB - Springer Verlag
T2 - 6th International Conference on Decision and Game Theory for Security, GameSec 2015
Y2 - 4 November 2015 through 5 November 2015
ER -