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Data privacy in pricing: estimation bias and implications
Ningyuan Chen, Ming Hu, Jialin Li, Sheng Liu
Rotman School of Management, University of Toronto
We study privacy protection mechanisms inspired by recent regulatory regimes, limited data retention and customer self protection. Privacy protection affects the estimation of demand model and thus the charged prices. We find that the change of the resulting price and which customer groups benefit from the protection depend on the product type. A real dataset of online auto loans validates our theoretical findings. We also extend the framework to nonlinear demand functions and duopoly.
Privacy-preserving personalized recommender systems
Xingyu Fu1, Ningyuan Chen2, Pin Gao3, Yang Li4
1Hong Kong University of Science and Technology; 2Rotman School of Management, University of Toronto; 3Chinese University of Hong Kong; 4Ivey Business School, Western University
Personalized recommender systems face privacy concerns. We explore optimal design under local differential privacy constraints. Our findings suggest a coarse-grained threshold policy for recommendations. Pursuing privacy comes at an economic loss but may benefit consumers. Our study guides algorithm design and informs regulators on privacy policies.
Recommender systems under privacy protection
Can Kucukgul1, Shouqiang Wang2
1Rutgers, The State University of New Jersey; 2University of Texas Dallas
