%0 Conference Proceedings
%T Privacy-Preserving Outlier Detection for Data Streams
%+ SAP Research
%+ University of Waterloo [Waterloo]
%A Böhler, Jonas
%A Bernau, Daniel
%A Kerschbaum, Florian
%Z Part 2: Privacy
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 31th IFIP Annual Conference on Data and Applications Security and Privacy (DBSEC)
%C Philadelphia, PA, United States
%Y Giovanni Livraga
%Y Sencun Zhu
%I Springer International Publishing
%3 Data and Applications Security and Privacy XXXI
%V LNCS-10359
%P 225-238
%8 2017-07-19
%D 2017
%R 10.1007/978-3-319-61176-1_12
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 11
%Z WG 11.3
%2 https://inria.hal.science/hal-01684375/document
%2 https://inria.hal.science/hal-01684375/file/453481_1_En_12_Chapter.pdf
%L hal-01684375
%U https://inria.hal.science/hal-01684375
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC11
%~ IFIP-WG11-3
%~ IFIP-DBSEC
%~ IFIP-LNCS-10359