Privacy-Preserving Anomaly Detection Through Sampled, Synthetic Data Generation

Fatema Rashid, Ali Miri

2024

Abstract

Anomaly detection techniques have been used successfully in various applications such as in security, financial, and medical domains. These techniques, and in particular those using advanced machine learning techniques require a high level of expertise, and the use of large volumes of data and increasing computational complexity. Outsourcing the expertise and the operational needs can provide an attractive option to many organizations. However data collected and used can include sensitive and confidential information which may require privacy protection due to legal, business or ethical considerations. We propose a novel and robust scheme that offers a flexible solution to users and organizations with varying computational and communication capabilities. Our solution would allow organizations to use semi-trusted third party cloud service providers services, while ensuring that these organizations can achieve their privacy requirement needs through the generation of synthetic data within with their computational/communication capabilities. We will demonstrate that not only does our scheme work for commonly used balanced data sets, but it is also suitable and it provides accurate results when applied to highly imbalanced data sets with extreme fluctuations in the high and low percentages of anomalies.

Paper Citation

in Harvard Style

Rashid F. and Miri A. (2024). Privacy-Preserving Anomaly Detection Through Sampled, Synthetic Data Generation. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 738-747. DOI: 10.5220/0012787100003767

in Bibtex Style

@conference{secrypt24,
author={Fatema Rashid and Ali Miri},
title={Privacy-Preserving Anomaly Detection Through Sampled, Synthetic Data Generation},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={738-747},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012787100003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Privacy-Preserving Anomaly Detection Through Sampled, Synthetic Data Generation
SN - 978-989-758-709-2
AU - Rashid F.
AU - Miri A.
PY - 2024
SP - 738
EP - 747
DO - 10.5220/0012787100003767
PB - SciTePress