Formalizing federated learning and differential privacy for GIS systems in IIIf
Conference paper
Kammueller, F., Piras, L., Fields, B. and Nagarajan, R. 2024. Formalizing federated learning and differential privacy for GIS systems in IIIf. 3rd International Workshop on System Security Assurance. Bydgoszcz, Poland 19 - 20 Sep 2024 Springer.
Type | Conference paper |
---|---|
Title | Formalizing federated learning and differential privacy for GIS systems in IIIf |
Authors | Kammueller, F., Piras, L., Fields, B. and Nagarajan, R. |
Abstract | GIS systems, like Google maps or ArcGIS, are a ubiquitous central application but are highly privacy critical. In many GIS systems, inputs from various and diverse sensors potentially expose private information. However, in particular, in the context of public safety, the sensor inputs are crucial to provide timely information for example for weather related warning systems. The notion of Differential Privacy (DP) has become an ad hoc standard, most notably adopted for the US census. Federated Learning (FL) facilitates machine learning for mobile distributed scenarios. This paper proposes a notion of DP for FL for infrastructures with actors and policies formalized in the Isabelle Insider and Infrastructure framework (IIIf). To illustrate this extension of the IIIf by FL and DP on a practical example, we apply the extended framework to a case study from GIS systems for extreme weather warning to control privacy. |
Keywords | Security and Privacy; Software and Application Security; Human-centered computing - Visualisation; Theory of computation - Logic |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Sustainability |
Research Group | seta |
Conference | 3rd International Workshop on System Security Assurance |
ISSN | 0302-9743 |
Electronic | 1611-3349 |
Publisher | Springer |
Publication process dates | |
Accepted | 21 Jul 2024 |
Deposited | 02 Oct 2024 |
Output status | Accepted |
Accepted author manuscript | File Access Level Open |
Copyright Statement | This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: [insert DOI]. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-ma... |
Language | English |
https://repository.mdx.ac.uk/item/1q96z4
Restricted files
Accepted author manuscript
33
total views1
total downloads7
views this month0
downloads this month