Personal Data Privacy Semantics in Multi-Agent Systems Interactions

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Abstract

In recent years, we have witnessed the growth of applications relying on the use and processing of personal data, especially in the health and well-being domains. Users themselves produce these data (e.g., through self-reported data acquisition, or personal devices such as smartphones, smartwatches or other wearables). A key challenge in this context is to guarantee the protection of personal data privacy, respecting the rights of users for deciding about data reuse, consent to data processing and storage, anonymity conditions, or the right to withhold or delete personal data. With the enforcement of recent regulations in this domain, such as the GDPR, applications are required to guarantee compliance, challenging current practices for personal data management. In this paper, we address this problem in the context of decentralized personal data applications, which may need to interact and negotiate conditions of data processing and reuse. Following a distributed paradigm without a top-down organization, we propose an agent-based model in which personal data providers and data consumers are embedded into privacy-aware agents capable of negotiating and coordinating data reuse, consent, and policies, using semantic vocabularies for privacy and provenance.

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Calvaresi, D., Schumacher, M., & Calbimonte, J. P. (2020). Personal Data Privacy Semantics in Multi-Agent Systems Interactions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12092 LNAI, pp. 55–67). Springer. https://doi.org/10.1007/978-3-030-49778-1_5

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