Rights statement: NOTICE: this is the author's version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published: Jose M. Such, Ana Garcia-Fornes, Agustin Espinosa and Joan Bellver. Magentix2: a Privacy-enhancing Agent Platform. Engineering Applications of Artificial Intelligence, Vol. 26 N. 1 pp. 96-109 (2013), © ELSEVIER.
Accepted author manuscript, 1.1 MB, PDF document
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Magentix2
T2 - a privacy-enhancing agent platform
AU - Such, Jose M.
AU - Garcia-Fornes, Ana
AU - Espinosa, Agustin
AU - Bellver, Joan
N1 - NOTICE: this is the author's version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published: Jose M. Such, Ana Garcia-Fornes, Agustin Espinosa and Joan Bellver. Magentix2: a Privacy-enhancing Agent Platform. Engineering Applications of Artificial Intelligence, Vol. 26 N. 1 pp. 96-109 (2013), © ELSEVIER.
PY - 2013/1
Y1 - 2013/1
N2 - Agent Platforms are the software that supports the development and execution of Multi-agent Systems. There are many Agent Platforms developed by the agent community, but they hardly consider privacy. This leads to agent-based applications that invade users’ privacy. Privacy can be threatened by two main information activities: information collection and information processing. Information collection can be prevented using traditional security mechanisms. Information processing can be prevented by minimizing data identifiability, i.e., the degree by which personal information can be directly attributed to a particular individual. However, minimizing data identifiability may directly affect other crucial issues in Multi-agent Systems, such as accountability, trust, and reputation. In this paper, we present the support that the Magentix2 Agent Platform provides for preserving privacy. Specifically, it provides mechanisms to avoid information collection and information processing when they are not desired. Moreover, Magentix2 provides these mechanisms without compromising accountability, trust, and reputation. We also provide in this paper an application built on top of Magentix2 that exploits its support for preserving privacy. Finally, we provide an extensive evaluation of the support that Magentix2 provides for preserving privacy based on that application. We specifically test whether or not privacy loss can be minimized by using the support that Magentix2 provides, whether or not this support introduces a bearable performance overhead, and whether or not existing trust and reputation models can be implemented on top of Magentix2.
AB - Agent Platforms are the software that supports the development and execution of Multi-agent Systems. There are many Agent Platforms developed by the agent community, but they hardly consider privacy. This leads to agent-based applications that invade users’ privacy. Privacy can be threatened by two main information activities: information collection and information processing. Information collection can be prevented using traditional security mechanisms. Information processing can be prevented by minimizing data identifiability, i.e., the degree by which personal information can be directly attributed to a particular individual. However, minimizing data identifiability may directly affect other crucial issues in Multi-agent Systems, such as accountability, trust, and reputation. In this paper, we present the support that the Magentix2 Agent Platform provides for preserving privacy. Specifically, it provides mechanisms to avoid information collection and information processing when they are not desired. Moreover, Magentix2 provides these mechanisms without compromising accountability, trust, and reputation. We also provide in this paper an application built on top of Magentix2 that exploits its support for preserving privacy. Finally, we provide an extensive evaluation of the support that Magentix2 provides for preserving privacy based on that application. We specifically test whether or not privacy loss can be minimized by using the support that Magentix2 provides, whether or not this support introduces a bearable performance overhead, and whether or not existing trust and reputation models can be implemented on top of Magentix2.
KW - Privacy
KW - Agent Platforms
KW - Multi-agent Systems
KW - Security
KW - Trust
KW - Reputation
U2 - 10.1016/j.engappai.2012.06.009
DO - 10.1016/j.engappai.2012.06.009
M3 - Journal article
VL - 26
SP - 96
EP - 109
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
SN - 0952-1976
IS - 1
ER -