Talks and Poster Presentations (with Proceedings-Entry):
B. Sedlak, I. Murturi, S. Dustdar:
"Specification and Operation of Privacy Models for Data Streams on the Edge";
Talk: 6th IEEE International Conference on Fog and Edge Computing (ICFEC 2022) - Hybrid Conference,
Taormina, Italy;
2022-05-18
- 2022-05-19; in: "Proceedings of the 6th IEEE International Conference on Fog and Edge Computing (ICFEC 2022)",
L. Mashayekhy, S. Schulte, V. Cardellini, B. Kantarci, Y. Simmhan, B. Varghese (ed.);
IEEE,
(2022),
ISBN: 978-1-6654-9525-7;
78
- 82.
English abstract:
The growing number of Internet of Things (IoT) devices generates massive amounts of diverse data, including personal or confidential information (i.e., sensory, images, etc.) that is not intended for public view. Traditionally, predefined privacy policies are usually enforced in resource-rich environments such as the cloud to protect sensitive information from being released. However, the massive amount of data streams, heterogeneous devices, and networks involved affects latency, and the possibility of having data intercepted grows as it travels away from the data source. Therefore, such data streams must be transformed on the IoT device or within available devices (i.e., edge devices) in its vicinity to ensure privacy. In this paper, we present a privacy-enforcing framework that transforms data streams on edge networks. We treat privacy close to the data source, using powerful edge devices to perform various operations to ensure privacy. Whenever an IoT device captures personal or confidential data, an edge gateway in the deviceīs vicinity analyzes and transforms data streams according to a predefined set of rules. How and when data is modified is defined precisely by a set of triggers and transformations - a privacy model - that directly represents a stakeholderīs privacy policies. Our work answered how to represent such privacy policies in a model and enforce transformations on the edge.
Keywords:
Edge Computing, Privacy Models, Data Stream Transformations, Data Anonymization
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ICFEC54809.2022.00018
Related Projects:
Project Head Schahram Dustdar:
FogProtect
Created from the Publication Database of the Vienna University of Technology.