Privacy-Preserving Anomaly Detection Using Synthetic Data

Privacy-Preserving Anomaly Detection Using Synthetic Data

Abstract

With ever increasing capacity for collecting, storing, and processing of data, there is also a high demand for intelligent knowledge discovery and data analysis methods. While there have been impressive advances in machine learning and similar domains in recent years, this also gives rise to concerns regarding the protection of personal and otherwise sensitive data, especially if it is to be analysed by third parties, e.g. in collaborative settings, where it shall be exchanged for the benefit of training more powerful models. One scenario is anomaly detection, which aims at identifying rare items, events or observations, differing from the majority of the data. Such anomalous items, also referred to as outliers, often correspond to problematic cases, e.g. bank fraud, rare medical diseases, or intrusions, e.g. attacks on IT systems.

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Authors
  • Mayer, Rudolf
  • Hittmeir, Markus
  • Ekelhart, Andreas
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
Data and Applications Security and Privacy XXXIV
Divisions
Security and Privacy
Subjects
Computersicherheit
Angewandte Informatik
Event Location
Virtual Event
Event Type
Conference
Event Dates
25-26 June 2020
Series Name
Data and Applications Security and Privacy XXXIII
Publisher
Springer International Publishing
Page Range
pp. 195-207
Date
2021
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