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Probabilistic Metric Spaces for Privacy by Design Machine Learning Algorithms: Modeling Database Changes
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
Department of Information and Communications Engineering, CYBERCAT-Center for Cybersecurity Research of Catalonia, Universitat Aut`onoma de Barcelona, Spain.
2018 (English)In: Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings / [ed] Joaquin Garcia-Alfaro, Jordi Herrera-Joancomartí, Giovanni Livraga, Ruben Rios, Cham: Springer, 2018, Vol. 11025, p. 422-430Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning, data mining and statistics are used to analyze the data and to build models from them. Data privacy for big data needs to find a compromise between data analysis and disclosure risk. Privacy by design machine learning algorithms need to take into account the space of models and the relationship between the data that generates the models and the models themselves. In this paper we propose the use of probabilistic metric spaces for comparing these models.

Place, publisher, year, edition, pages
Cham: Springer, 2018. Vol. 11025, p. 422-430
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11025
Keywords [en]
Data privacy, Integral privacy, Probabilistic metric spaces
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-17534DOI: 10.1007/978-3-030-00305-0_30ISI: 000477970100030ISBN: 978-3-030-00305-0 (electronic)ISBN: 978-3-030-00304-3 (print)OAI: oai:DiVA.org:his-17534DiVA, id: diva2:1343180
Conference
2nd International Workshop on Cryptocurrencies and Blockchain Technology (CBT) / 13th International Workshop on Data Privacy Management (DPM), September 6-7, 2018, Barcelona, Spain
Note

Also part of the Security and Cryptology book sub series (LNSC, volume 11025)

Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-08-16Bibliographically approved

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fulltext(281 kB)31 downloads
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