Data Tracking under Competition

Bimpikis, Kostas; Morgenstern, Ilan; Sabán, Daniela

doi:10.2139/ssrn.3808228

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Our paper also relates to the growing body of literature on the use of personal data by firms (e.g., Bergemann and Bonatti, 2015;Arrieta-Ibarra et al, 2018;Bergemann et al, 2018;Acemoglu et al, 2022;Bergemann and Bonatti, 2022;Bimpikis et al, 2023) by providing empirical evidence on the value of data in firm production. We also directly contribute to the economics of privacy literature Tucker, 2011, 2012;Acquisti et al, 2016;Athey et al, 2017;Choi et al, 2019;Montes et al, 2019;Ichihashi, 2020;Loertscher and Marx, 2020;Chen et al, 2021;Krähmer and Strausz, 2023) by evaluating the effects of the largest privacy regulation on important firm outcomes.…”

Section: Introductionmentioning

confidence: 92%

Data, Privacy Laws and Firm Production: Evidence from the GDPR

Demirer,

Jiménez Hernández,

et al. 2024

SSRN Journal

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Section: Introductionmentioning

confidence: 92%

Data, Privacy Laws and Firm Production: Evidence from the GDPR

Demirer,

Jiménez Hernández,

et al. 2024

SSRN Journal

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“…The impact of data tracking on the firms' competition and users has been studied in Bimpikis et al [2021] and Gur et al [2019]. The dynamic sale of data has been studied in Immorlica et al [2021] and the difference between static and dynamic mechanisms has been studied in Babaioff et al [2012] and Drakopoulos and Makhdoumi [2020].…”

Section: Related Literaturementioning

confidence: 99%

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

Fallah¹,

Makhdoumi²,

Malekian³

et al. 2022

Preprint

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We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest. We formulate this question as a Bayesian-optimal mechanism design problem, in which an individual can share her (verifiable) data in exchange for a monetary reward or services, but at the same time has a (private) heterogeneous privacy cost which we quantify using differential privacy. We consider two popular differential privacy settings for providing privacy guarantees for the users: central and local. In both settings, we establish minimax lower bounds for the estimation error and derive (near) optimal estimators for given heterogeneous privacy loss levels for users. Building on this characterization, we pose the mechanism design problem as the optimal selection of an estimator and payments that will elicit truthful reporting of users' privacy sensitivities. Under a regularity condition on the distribution of privacy sensitivities we develop efficient algorithmic mechanisms to solve this problem in both privacy settings. Our mechanism in the central setting can be implemented in time O(n log n) where n is the number of users and our mechanism in the local setting admits a Polynomial Time Approximation Scheme (PTAS).* We would like to thank Kunal Talwar for helpful comments.

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“…With widespread public concern of personal data security, several recent works in personalized pricing start to take the data privacy into consideration (see e.g., Tang et al 2020, Lei et al 2020, Bimpikis et al 2021). Among these literature, the most related work to this paper is , which also consider a dynamic personalized pricing problem with differential privacy guarantee.…”

Section: Related Literaturementioning

confidence: 99%

Differential Privacy in Personalized Pricing with Nonparametric Demand Models

Chen¹,

Miao²

2021

Preprint

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In the recent decades, the advance of information technology and abundant personal data facilitate the application of algorithmic personalized pricing. However, this leads to the growing concern of potential violation of privacy due to adversarial attack. To address the privacy issue, this paper studies a dynamic personalized pricing problem with unknown nonparametric demand models under data privacy protection.Two concepts of data privacy, which have been widely applied in practices, are introduced: central differential privacy (CDP) and local differential privacy (LDP), which is proved to be stronger than CDP in many cases.We develop two algorithms which make pricing decisions and learn the unknown demand on the fly, while satisfying the CDP and LDP gurantees respectively. In particular, for the algorithm with CDP guarantee, the regret is proved to be at most O(T (d+2)/(d+4) + ε −1 T d/(d+4) ). Here, the parameter T denotes the length of the time horizon, d is the dimension of the personalized information vector, and the key parameter ε > 0 measures the strength of privacy (smaller ε indicates a stronger privacy protection). On the other hand, for the algorithm with LDP guarantee, its regret is proved to be at most O(ε −2/(d+2) T (d+1)/(d+2) ), which is near-optimal as we prove a lower bound of Ω(ε −2/(d+2) T (d+1)/(d+2) ) for any algorithm with LDP guarantee.

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Data Tracking under Competition

Cited by 7 publications

References 37 publications

Data, Privacy Laws and Firm Production: Evidence from the GDPR

Data, Privacy Laws and Firm Production: Evidence from the GDPR

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

Differential Privacy in Personalized Pricing with Nonparametric Demand Models

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