Towards Pattern-Level Privacy Protection in Distributed Complex Event Processing

Majid Lotfian Delouee, Boris Koldehofe, Viktoriya Degeler

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
92 Downloads (Pure)

Abstract

In event processing systems, detected event patterns can reveal
privacy-sensitive information. In this paper, we propose
and discuss how to integrate pattern-level privacy protection in event-based systems. Compared to state-of-the-art approaches, we aim to enforce privacy independent of the particularities of specific operators. We accomplish this by supporting the flexible integration of multiple obfuscation techniques and studying deployment strategies for privacy-enforcing mechanisms. Moreover, we share ideas on how to model the adversary’s knowledge to better select appropriate obfuscation techniques for the discussed deployment strategies. Initial results indicate that flexibly choosing obfuscation techniques and deployment strategies is essential to conceal privacy-sensitive event patterns accurately.
Original languageEnglish
Title of host publicationProceedings of the 17th ACM International Conference on Distributed and Event-based Systems
Subtitle of host publicationDEBS '23
PublisherACM Press
Pages185-186
Number of pages2
ISBN (Print)979-8-4007-0122-1
DOIs
Publication statusPublished - Jun-2023
EventThe 17th ACM International Conference on Distributed and Event-Based Systems (DEBS'23) - University of Neuchatel, Neuchatel, Switzerland
Duration: 27-Jun-202330-Jun-2023
https://2023.debs.org/

Conference

ConferenceThe 17th ACM International Conference on Distributed and Event-Based Systems (DEBS'23)
Country/TerritorySwitzerland
CityNeuchatel
Period27/06/202330/06/2023
Internet address

Keywords

  • Privacy
  • Complex Event Processing
  • Pattern

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