TY - GEN
T1 - Data Collection from Privacy-Aware Users in the Presence of Social Learning
AU - Akbay, Abdullah Basar
AU - Wang, Weina
AU - Zhang, Junshan
N1 - Funding Information:
This research was supported in part by the National Science Foundation under Grant SaTC-1618768 and Army Research Office under Grant W911NF-16-1-0448.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - We study a model where a data collector obtains data from users through a payment mechanism to learn the underlying state from the elicited data. The private signal of each user represents her individual knowledge about the state. Through social interactions, each user can also learn noisy versions of her friends' signals, which is called group signals. Based on both her private signal and group signals, each user makes strategic decisions to report a privacy-preserved version of her data to the data collector. We develop a Bayesian game theoretic framework to study the impact of social learning on users' data reporting strategies and devise the payment mechanism for the data collector accordingly. Our findings reveal that, the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy. A generalized majority voting rule is applied by each user to her noisy group signals to determine which strategy to follow. When a user plays the ND strategy, she reports privacy-preserving data completely based on her group signals, independent of her private signal, which indicates that her privacy cost is zero. Both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation at a given payment budget. We derive bounds on the minimum total payment required to achieve a given level of state estimation accuracy.
AB - We study a model where a data collector obtains data from users through a payment mechanism to learn the underlying state from the elicited data. The private signal of each user represents her individual knowledge about the state. Through social interactions, each user can also learn noisy versions of her friends' signals, which is called group signals. Based on both her private signal and group signals, each user makes strategic decisions to report a privacy-preserved version of her data to the data collector. We develop a Bayesian game theoretic framework to study the impact of social learning on users' data reporting strategies and devise the payment mechanism for the data collector accordingly. Our findings reveal that, the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy. A generalized majority voting rule is applied by each user to her noisy group signals to determine which strategy to follow. When a user plays the ND strategy, she reports privacy-preserving data completely based on her group signals, independent of her private signal, which indicates that her privacy cost is zero. Both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation at a given payment budget. We derive bounds on the minimum total payment required to achieve a given level of state estimation accuracy.
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U2 - 10.1109/ALLERTON.2019.8919800
DO - 10.1109/ALLERTON.2019.8919800
M3 - Conference contribution
AN - SCOPUS:85077795377
T3 - 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
SP - 679
EP - 686
BT - 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
Y2 - 24 September 2019 through 27 September 2019
ER -