Investment in privacy-preserving technologies under uncertainty

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationDecision and Game Theory for Security
Subtitle of host publicationSecond International Conference, GameSec 2011, Proceedings
PublisherSpringer Verlag
Pages219-238
Volume7037 LNCS
ISBN (print)9783642252792
Publication statusPublished - 2011
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7037 LNCS
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Conference

Title2nd International Conference on Decision and Game Theory for Security, GameSec 2011
PlaceUnited States
CityCollege Park, MD
Period14 - 15 November 2011

Abstract

Entrepreneurs face investment decisions on privacy-preserving technology (PPT) adoption as privacy-concerned consumers may decide whether to use firms' services based on the extent of privacy that firms are able to provide. Kantarcioglu et.al. (2010) [9] contributes to guidelines for entrepreneurs' adoption decisions through a novel framework, which combines copula functions and a Stackelberg leader-follower game with consumers taking the role of the follower (referred as static-copula-game model hereafter). The valuation requires a clearly defined bivariate distribution function of two random variables, the consumer's valuation of private information and the consumer's profitability to a firm. Copula functions are used to construct the bivariate distribution function from arbitrarily univariate marginals with various dependence structures fitting into different market/industry segments. This study extends the static-copula-game model to include project value uncertainty, simultaneously considering different market competition structures and the regulatory promise of random arrival of government mandatory adoption. The project value from the static-copula-game model is used as an estimate of the initial (current) project value for the stochastic evolution. By doing so, we retain the advantages of applying copulas and preserve the established valuation property exclusively applicable to the valuation of PPT adoption. The extension model makes several improvements including: (1) Reduce concerns about myopic PPT adoption decisions that may result when static valuation is employed. (2) Overcome the potential biased PPT adoption decision that may arise due to negligence of market competition impact. (3) Understand the regulatory influence of government mandatory adoption with uncertainty. We find that: (1) If one can link univariate marginals and dependence structures to industry groups, one can determine for which industries project value uncertainty has no impact on the entrepreneur's immediate PPT adoption decision. For these industries, there is no need for government intervention/regulation to accelerate/induce PPT adoption even though the project value is uncertain. (2) Under project value uncertainty, competition may suggest either a later or an earlier PPT adoption compared with the monopoly case. (3) The promise of government mandatory adoption has the potential to accelerate PPT adoption. The PPT adoption guidelines considering competition and regulatory promises of government mandatory adoption when the project value is uncertain bring useful recommendations to both entrepreneurs and policymakers. © 2011 Springer-Verlag.

Research Area(s)

  • Copulas, Government Intervention, Privacy-Preserving Technology, Random Competition, Stackelberg Game, Uncertainty

Citation Format(s)

Investment in privacy-preserving technologies under uncertainty. / Kantarcioglu, Murat; Bensoussan, Alain; Hoe, SingRu.
Decision and Game Theory for Security: Second International Conference, GameSec 2011, Proceedings. Vol. 7037 LNCS Springer Verlag, 2011. p. 219-238 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7037 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review