A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications

Michela Chessa; Jens Grossklags; Patrick Loiseau

doi:10.1109/CSF.2015.14

A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications

Michela Chessa, Jens Grossklags, Patrick Loiseau

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

30 Scopus citations

Abstract

The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoffs scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibrium that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal.

Original language	English
Title of host publication	Proceedings - 2015 IEEE 28th Computer Security Foundations Symposium, CSF 2015
Editors	Patrick Kellenberger
Publisher	IEEE Computer Society
Pages	90-104
Number of pages	15
ISBN (Electronic)	9781467375382
DOIs	https://doi.org/10.1109/CSF.2015.14
State	Published - 4 Sep 2015
Externally published	Yes
Event	28th IEEE Computer Security Foundations Symposium, CSF 2015 - Verona, Italy Duration: 13 Jul 2015 → 17 Jul 2015

Publication series

Name	Proceedings of the Computer Security Foundations Workshop
Volume	2015-September
ISSN (Print)	1063-6900

Conference

Conference	28th IEEE Computer Security Foundations Symposium, CSF 2015
Country/Territory	Italy
City	Verona
Period	13/07/15 → 17/07/15

Keywords

Non-cooperative game
data analytics
non-monetary incentives
population estimate
privacy
public good

Access to Document

10.1109/CSF.2015.14

Cite this

Chessa, M., Grossklags, J., & Loiseau, P. (2015). A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications. In P. Kellenberger (Ed.), Proceedings - 2015 IEEE 28th Computer Security Foundations Symposium, CSF 2015 (pp. 90-104). Article 7243727 (Proceedings of the Computer Security Foundations Workshop; Vol. 2015-September). IEEE Computer Society. https://doi.org/10.1109/CSF.2015.14

Chessa, Michela ; Grossklags, Jens ; Loiseau, Patrick. / A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications. Proceedings - 2015 IEEE 28th Computer Security Foundations Symposium, CSF 2015. editor / Patrick Kellenberger. IEEE Computer Society, 2015. pp. 90-104 (Proceedings of the Computer Security Foundations Workshop).

@inproceedings{45854708de4842ffa3dd382820acaca6,

title = "A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications",

abstract = "The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoffs scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibrium that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal.",

keywords = "Non-cooperative game, data analytics, non-monetary incentives, population estimate, privacy, public good",

author = "Michela Chessa and Jens Grossklags and Patrick Loiseau",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 28th IEEE Computer Security Foundations Symposium, CSF 2015 ; Conference date: 13-07-2015 Through 17-07-2015",

year = "2015",

month = sep,

day = "4",

doi = "10.1109/CSF.2015.14",

language = "English",

series = "Proceedings of the Computer Security Foundations Workshop",

publisher = "IEEE Computer Society",

pages = "90--104",

editor = "Patrick Kellenberger",

booktitle = "Proceedings - 2015 IEEE 28th Computer Security Foundations Symposium, CSF 2015",

}

Chessa, M, Grossklags, J & Loiseau, P 2015, A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications. in P Kellenberger (ed.), Proceedings - 2015 IEEE 28th Computer Security Foundations Symposium, CSF 2015., 7243727, Proceedings of the Computer Security Foundations Workshop, vol. 2015-September, IEEE Computer Society, pp. 90-104, 28th IEEE Computer Security Foundations Symposium, CSF 2015, Verona, Italy, 13/07/15. https://doi.org/10.1109/CSF.2015.14

A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications. / Chessa, Michela; Grossklags, Jens; Loiseau, Patrick.
Proceedings - 2015 IEEE 28th Computer Security Foundations Symposium, CSF 2015. ed. / Patrick Kellenberger. IEEE Computer Society, 2015. p. 90-104 7243727 (Proceedings of the Computer Security Foundations Workshop; Vol. 2015-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications

AU - Chessa, Michela

AU - Grossklags, Jens

AU - Loiseau, Patrick

PY - 2015/9/4

Y1 - 2015/9/4

N2 - The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoffs scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibrium that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal.

AB - The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoffs scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibrium that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal.

KW - Non-cooperative game

KW - data analytics

KW - non-monetary incentives

KW - population estimate

KW - privacy

KW - public good

UR - http://www.scopus.com/inward/record.url?scp=84961327972&partnerID=8YFLogxK

U2 - 10.1109/CSF.2015.14

DO - 10.1109/CSF.2015.14

M3 - Conference contribution

AN - SCOPUS:84961327972

T3 - Proceedings of the Computer Security Foundations Workshop

SP - 90

EP - 104

BT - Proceedings - 2015 IEEE 28th Computer Security Foundations Symposium, CSF 2015

A2 - Kellenberger, Patrick

PB - IEEE Computer Society

T2 - 28th IEEE Computer Security Foundations Symposium, CSF 2015

Y2 - 13 July 2015 through 17 July 2015

ER -

Chessa M, Grossklags J, Loiseau P. A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications. In Kellenberger P, editor, Proceedings - 2015 IEEE 28th Computer Security Foundations Symposium, CSF 2015. IEEE Computer Society. 2015. p. 90-104. 7243727. (Proceedings of the Computer Security Foundations Workshop). doi: 10.1109/CSF.2015.14

A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this