Rights statement: NOTICE: this is the author's version of a work that was accepted for publication in Information Sciences. 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, Agustin Espinosa, Ana Garcia-Fornes and Carles Sierra. Self-disclosure Decision Making based on Intimacy and Privacy. Information Sciences, Vol. 211 pp. 93-111 (2012). © ELSEVIER.
Accepted author manuscript, 457 KB, PDF document
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 30/11/2012 |
|---|---|
| <mark>Journal</mark> | Information Sciences |
| Volume | 211 |
| Number of pages | 19 |
| Pages (from-to) | 93-111 |
| Publication Status | Published |
| <mark>Original language</mark> | English |
Autonomous agents may encapsulate their principals' personal data attributes. These attributes may be disclosed to other agents during agent interactions, producing a loss of privacy. Thus, agents need self-disclosure decision-making mechanisms to autonomously decide whether disclosing personal data attributes to other agents is acceptable or not. Current self-disclosure decision-making mechanisms consider the direct benefit and the privacy loss of disclosing an attribute. However, there are many situations in which the direct benefit of disclosing an attribute is a priori unknown. This is the case in human relationships, where the disclosure of personal data attributes plays a crucial role in their development. In this paper, we present self-disclosure decision-making mechanisms based on psychological findings regarding how humans disclose personal information in the building of their relationships. We experimentally demonstrate that, in most situations, agents following these decision-making mechanisms lose less privacy than agents that do not use them. (C) 2012 Elsevier Inc. All rights reserved.