Privacy-preserving collaborative anomaly detection for participatory sensing
Date
2014
Authors
Erfani, S.M.
Law, Y.W.
Karunasekera, S.
Leckie, C.A.
Palaniswami, M.
Editors
Teng, V.S.
Advisors
Journal Title
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Type:
Conference paper
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014 / Teng, V.S. (ed./s), vol.8443 LNAI, iss.PART 1, pp.581-593
Statement of Responsibility
Conference Name
Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (13 May 2014 - 16 May 2014 : Tainan, Taiwan)
Abstract
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 privacypreserving 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.
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Copyright 2014 Springer International Publishing Switzerland