17th AIAI 2021, 25 - 27 June 2021, Greece

PFilter: Privacy-aware and secure data filtering at the edge for distributed edge analytics

Annanda Rath, Anna Hristoskova, Sarah Klein


  This paper is presenting a conceptual mechanism for lightweight privacy-aware and secure data access control and filtering. This mechanism can be deployed at an edge node in order to assure that all data coming in and going out of it is properly protected and filtered. Goal is to keep private data locally and limit its exposure to outside entities (e.g., Cloud backend, external application or other edge nodes) while preserving the performance and security requirements for edge analytics. The data filtering at the edge node is done in a way that it is not possible for outside entities to identify end-devices and the data associated with them.  

*** Title, author list and abstract as seen in the Camera-Ready version of the paper that was provided to Conference Committee. Small changes that may have occurred during processing by Springer may not appear in this window.