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Privacy-Preserving Outlier Detection for Data Streams

Abstract : In cyber-physical systems sensors data should be anonymized at the source. Local data perturbation with differential privacy guarantees can be used, but the resulting utility is often (too) low. In this paper we contribute an algorithm that combines local, differentially private data perturbation of sensor streams with highly accurate outlier detection. We evaluate our algorithm on synthetic data. In our experiments we obtain an accuracy of 80% with a differential privacy value of $$\epsilon = 0.1$$ for well separated outliers.
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Jonas Böhler, Daniel Bernau, Florian Kerschbaum. Privacy-Preserving Outlier Detection for Data Streams. 31th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC), Jul 2017, Philadelphia, PA, United States. pp.225-238, ⟨10.1007/978-3-319-61176-1_12⟩. ⟨hal-01684375⟩

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