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Sharing Personal Data via Incentive-based Negotiation: Preference Modeling and Empirical Analysis
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AI
DOI：10.1145/3770751.png)
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Abstract
En 中文
In an age where data is a pivotal asset for businesses, the ethical acquisition and use of personal information has become increasingly more significant. Empowering data providers with greater autonomy over their personal data is more important than ever. To address this, we propose a novel negotiation-based information-sharing framework that empowers individuals to actively negotiate the terms of their data sharing, addressing privacy concerns and ethical data usage. The framework enables users to determine what personal information they share and under what conditions, fostering a more balanced and transparent data exchange process. Our system allows data consumer agents to negotiate with their human users and can operate fully automatically, with agents representing data providers negotiating based on elicited preferences and needs. We propose novel preference modeling approaches and a negotiation framework to facilitate the bilateral sharing of information and incentives between data consumers and providers. User experiments demonstrate the efficacy of our negotiation approach and the effectiveness of the proposed preference models. Empirical results validate the benefits of the proposed framework.
Keywords:
Preference elicitation
automated negotiation
Journal
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4.1
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896
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1.9K