Advertisement
Published in:
2014 | OriginalPaper | Chapter
Privacy-Preserving Collaborative Anomaly Detection for Participatory Sensing
Authors : Sarah M. Erfani, Yee Wei Law, Shanika Karunasekera, Christopher A. Leckie, Marimuthu Palaniswami
Published in: Advances in Knowledge Discovery and Data Mining
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
In collaborative anomaly detection, multiple data sources submit their data to an on-line service, in order to detect anomalies with respect to the wider population. A major challenge is how to achieve reasonable detection accuracy without disclosing the actual values of the participants’ data. We propose a lightweight and scalable privacy-preserving collaborative anomaly detection scheme called Random Multiparty Perturbation (RMP), which uses a combination of nonlinear and participant-specific linear perturbation. Each participant uses an individually perturbed uniformly distributed random matrix, in contrast to existing approaches that use a common random matrix. A privacy analysis is given for Bayesian Estimation and Independent Component Analysis attacks. Experimental results on real and synthetic datasets using an auto-encoder show that RMP yields comparable results to non-privacy preserving anomaly detection.