Impact of privacy on free online service markets

Chong Huang, Lalitha Sankar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The emerging marketplace for online free services in which service providers (SPs) earn revenue from using consumer data in direct and indirect ways has led to significant privacy concerns. This begs understanding of the following question: can the marketplace sustain multiple SPs that offer privacy differentiated free services? This paper studies the impact of privacy on free online service markets by augmenting the classical Hotelling model for market segmentation analysis. A parametrized game-theoretic model is proposed which captures: (i) the fact that for the free service market, consumers value service not in monetized terms but by the quality of service (QoS); (ii) the differentiator of services is not product price but the privacy risk advertised by an SP; and (iii) consumer’s heterogeneous privacy preference for SPs. For the two-SP problem with uniformly distributed consumer privacy preference and linear SP profit function, the results suggest that: (i) when consumers place a higher value on privacy, it leads to a larger market share for the SP providing untargeted services and a “softened” competition between SPs; (ii) SPs offering high privacy risk services are sustainable only if they offer sufficiently high QoS; and (iii) SPs that are capable of differentiating on services that do not directly use consumer data gain larger market share. Similar results are observed when the consumer’s privacy preference is modeled as a truncated Gaussian distribution.

Original languageEnglish (US)
Title of host publicationDecision and Game Theory for Security - 9th International Conference, GameSec 2018, Proceedings
EditorsLinda Bushnell, Radha Poovendran, Tamer Basar
PublisherSpringer Verlag
Pages1-21
Number of pages21
ISBN (Print)9783030015534
DOIs
StatePublished - Jan 1 2018
Event9th International Conference on Decision and Game Theory for Security, GameSec 2018 - Seattle, United States
Duration: Oct 29 2018Oct 31 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11199 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Decision and Game Theory for Security, GameSec 2018
CountryUnited States
CitySeattle
Period10/29/1810/31/18

Fingerprint

Privacy
Quality of service
Gaussian distribution
Profitability
Market
Quality of Service
Market Segmentation
Truncated Distributions
Profit

Keywords

  • Free online services
  • Market segmentation
  • Privacy differentiated services
  • Quality of service

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Huang, C., & Sankar, L. (2018). Impact of privacy on free online service markets. In L. Bushnell, R. Poovendran, & T. Basar (Eds.), Decision and Game Theory for Security - 9th International Conference, GameSec 2018, Proceedings (pp. 1-21). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11199 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01554-1_1

Impact of privacy on free online service markets. / Huang, Chong; Sankar, Lalitha.

Decision and Game Theory for Security - 9th International Conference, GameSec 2018, Proceedings. ed. / Linda Bushnell; Radha Poovendran; Tamer Basar. Springer Verlag, 2018. p. 1-21 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11199 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Huang, C & Sankar, L 2018, Impact of privacy on free online service markets. in L Bushnell, R Poovendran & T Basar (eds), Decision and Game Theory for Security - 9th International Conference, GameSec 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11199 LNCS, Springer Verlag, pp. 1-21, 9th International Conference on Decision and Game Theory for Security, GameSec 2018, Seattle, United States, 10/29/18. https://doi.org/10.1007/978-3-030-01554-1_1
Huang C, Sankar L. Impact of privacy on free online service markets. In Bushnell L, Poovendran R, Basar T, editors, Decision and Game Theory for Security - 9th International Conference, GameSec 2018, Proceedings. Springer Verlag. 2018. p. 1-21. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-01554-1_1
Huang, Chong ; Sankar, Lalitha. / Impact of privacy on free online service markets. Decision and Game Theory for Security - 9th International Conference, GameSec 2018, Proceedings. editor / Linda Bushnell ; Radha Poovendran ; Tamer Basar. Springer Verlag, 2018. pp. 1-21 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{40297c6f9ec14331b0d29edbf4e8b665,
title = "Impact of privacy on free online service markets",
abstract = "The emerging marketplace for online free services in which service providers (SPs) earn revenue from using consumer data in direct and indirect ways has led to significant privacy concerns. This begs understanding of the following question: can the marketplace sustain multiple SPs that offer privacy differentiated free services? This paper studies the impact of privacy on free online service markets by augmenting the classical Hotelling model for market segmentation analysis. A parametrized game-theoretic model is proposed which captures: (i) the fact that for the free service market, consumers value service not in monetized terms but by the quality of service (QoS); (ii) the differentiator of services is not product price but the privacy risk advertised by an SP; and (iii) consumer’s heterogeneous privacy preference for SPs. For the two-SP problem with uniformly distributed consumer privacy preference and linear SP profit function, the results suggest that: (i) when consumers place a higher value on privacy, it leads to a larger market share for the SP providing untargeted services and a “softened” competition between SPs; (ii) SPs offering high privacy risk services are sustainable only if they offer sufficiently high QoS; and (iii) SPs that are capable of differentiating on services that do not directly use consumer data gain larger market share. Similar results are observed when the consumer’s privacy preference is modeled as a truncated Gaussian distribution.",
keywords = "Free online services, Market segmentation, Privacy differentiated services, Quality of service",
author = "Chong Huang and Lalitha Sankar",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-030-01554-1_1",
language = "English (US)",
isbn = "9783030015534",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1--21",
editor = "Linda Bushnell and Radha Poovendran and Tamer Basar",
booktitle = "Decision and Game Theory for Security - 9th International Conference, GameSec 2018, Proceedings",

}

TY - GEN

T1 - Impact of privacy on free online service markets

AU - Huang, Chong

AU - Sankar, Lalitha

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The emerging marketplace for online free services in which service providers (SPs) earn revenue from using consumer data in direct and indirect ways has led to significant privacy concerns. This begs understanding of the following question: can the marketplace sustain multiple SPs that offer privacy differentiated free services? This paper studies the impact of privacy on free online service markets by augmenting the classical Hotelling model for market segmentation analysis. A parametrized game-theoretic model is proposed which captures: (i) the fact that for the free service market, consumers value service not in monetized terms but by the quality of service (QoS); (ii) the differentiator of services is not product price but the privacy risk advertised by an SP; and (iii) consumer’s heterogeneous privacy preference for SPs. For the two-SP problem with uniformly distributed consumer privacy preference and linear SP profit function, the results suggest that: (i) when consumers place a higher value on privacy, it leads to a larger market share for the SP providing untargeted services and a “softened” competition between SPs; (ii) SPs offering high privacy risk services are sustainable only if they offer sufficiently high QoS; and (iii) SPs that are capable of differentiating on services that do not directly use consumer data gain larger market share. Similar results are observed when the consumer’s privacy preference is modeled as a truncated Gaussian distribution.

AB - The emerging marketplace for online free services in which service providers (SPs) earn revenue from using consumer data in direct and indirect ways has led to significant privacy concerns. This begs understanding of the following question: can the marketplace sustain multiple SPs that offer privacy differentiated free services? This paper studies the impact of privacy on free online service markets by augmenting the classical Hotelling model for market segmentation analysis. A parametrized game-theoretic model is proposed which captures: (i) the fact that for the free service market, consumers value service not in monetized terms but by the quality of service (QoS); (ii) the differentiator of services is not product price but the privacy risk advertised by an SP; and (iii) consumer’s heterogeneous privacy preference for SPs. For the two-SP problem with uniformly distributed consumer privacy preference and linear SP profit function, the results suggest that: (i) when consumers place a higher value on privacy, it leads to a larger market share for the SP providing untargeted services and a “softened” competition between SPs; (ii) SPs offering high privacy risk services are sustainable only if they offer sufficiently high QoS; and (iii) SPs that are capable of differentiating on services that do not directly use consumer data gain larger market share. Similar results are observed when the consumer’s privacy preference is modeled as a truncated Gaussian distribution.

KW - Free online services

KW - Market segmentation

KW - Privacy differentiated services

KW - Quality of service

UR - http://www.scopus.com/inward/record.url?scp=85055893585&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85055893585&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-01554-1_1

DO - 10.1007/978-3-030-01554-1_1

M3 - Conference contribution

SN - 9783030015534

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 21

BT - Decision and Game Theory for Security - 9th International Conference, GameSec 2018, Proceedings

A2 - Bushnell, Linda

A2 - Poovendran, Radha

A2 - Basar, Tamer

PB - Springer Verlag

ER -