Negotiation for Incentive Driven Privacy-Preserving Information Sharing

Reyhan Aydogan; Pinar Øzturk; Yousef Razeghi

doi:10.1007/978-3-319-69131-2_31

Negotiation for Incentive Driven Privacy-Preserving Information Sharing

Reyhan Aydogan, Pinar Øzturk, Yousef Razeghi

Interactive Intelligence

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

Abstract

This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester’s data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case. However, the proposed architecture is domain independent.

Original language	English
Title of host publication	PRIMA 2017
Subtitle of host publication	Principles and Practice of Multi-Agent Systems - 20th International Conference - Proceedings
Editors	Bo An, Ana Bazzan, João Leite, Serena Villata, Leendert van der Torre
Place of Publication	Cham
Publisher	Springer
Pages	486-494
Number of pages	9
ISBN (Print)	978-3-319-69130-5
DOIs	https://doi.org/10.1007/978-3-319-69131-2_31
Publication status	Published - 5 Oct 2017
Event	PRIMA 2017: 20th International Conference on Principles and Practice of Multi-Agent Systems - Nice, France Duration: 30 Oct 2017 → 3 Nov 2017 Conference number: 20 https://prima2017.gforge.uni.lu/

Publication series

Name	Lecture Notes in Computer Science
Volume	10621
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	PRIMA 2017
Country/Territory	France
City	Nice
Period	30/10/17 → 3/11/17
Internet address	https://prima2017.gforge.uni.lu/

Keywords

Data and information sharing
Incentive-driven
Secrecy and privacy risk
Negotiation
Privacy-preserving agent systems

Access to Document

10.1007/978-3-319-69131-2_31

Cite this

Aydogan, R., Øzturk, P., & Razeghi, Y. (2017). Negotiation for Incentive Driven Privacy-Preserving Information Sharing. In B. An, A. Bazzan, J. Leite, S. Villata, & L. van der Torre (Eds.), PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference - Proceedings (pp. 486-494). (Lecture Notes in Computer Science; Vol. 10621). Springer. https://doi.org/10.1007/978-3-319-69131-2_31

Aydogan, Reyhan ; Øzturk, Pinar ; Razeghi, Yousef. / Negotiation for Incentive Driven Privacy-Preserving Information Sharing. PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference - Proceedings. editor / Bo An ; Ana Bazzan ; João Leite ; Serena Villata ; Leendert van der Torre. Cham : Springer, 2017. pp. 486-494 (Lecture Notes in Computer Science).

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abstract = "This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester{\textquoteright}s data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case. However, the proposed architecture is domain independent.",

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Aydogan, R, Øzturk, P & Razeghi, Y 2017, Negotiation for Incentive Driven Privacy-Preserving Information Sharing. in B An, A Bazzan, J Leite, S Villata & L van der Torre (eds), PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference - Proceedings. Lecture Notes in Computer Science, vol. 10621, Springer, Cham, pp. 486-494, PRIMA 2017, Nice, France, 30/10/17. https://doi.org/10.1007/978-3-319-69131-2_31

Negotiation for Incentive Driven Privacy-Preserving Information Sharing. / Aydogan, Reyhan; Øzturk, Pinar; Razeghi, Yousef.
PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference - Proceedings. ed. / Bo An; Ana Bazzan; João Leite; Serena Villata; Leendert van der Torre. Cham: Springer, 2017. p. 486-494 (Lecture Notes in Computer Science; Vol. 10621).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Aydogan, Reyhan

AU - Øzturk, Pinar

AU - Razeghi, Yousef

N1 - Conference code: 20

PY - 2017/10/5

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N2 - This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester’s data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case. However, the proposed architecture is domain independent.

AB - This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester’s data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case. However, the proposed architecture is domain independent.

KW - Data and information sharing

KW - Incentive-driven

KW - Secrecy and privacy risk

KW - Negotiation

KW - Privacy-preserving agent systems

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T3 - Lecture Notes in Computer Science

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EP - 494

BT - PRIMA 2017

A2 - An, Bo

A2 - Bazzan, Ana

A2 - Leite, João

A2 - Villata, Serena

A2 - van der Torre, Leendert

PB - Springer

CY - Cham

T2 - PRIMA 2017

Y2 - 30 October 2017 through 3 November 2017

ER -

Aydogan R, Øzturk P, Razeghi Y. Negotiation for Incentive Driven Privacy-Preserving Information Sharing. In An B, Bazzan A, Leite J, Villata S, van der Torre L, editors, PRIMA 2017: Principles and Practice of Multi-Agent Systems - 20th International Conference - Proceedings. Cham: Springer. 2017. p. 486-494. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-69131-2_31

Negotiation for Incentive Driven Privacy-Preserving Information Sharing

Abstract

Publication series

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Keywords

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Cite this