%0 Conference Proceedings
%A Stach, Christoph
%D 2019
%T VAULT: A Privacy Approach towards High-Utility Time Series Data
%E Rass, Stefan & Yee, George
%B Proceedings of the Thirteenth International Conference on Emerging Security Information, Systems and Technologies
%C Nice
%I IARIA
%P 41-46
%S SECURWARE '19
%8 October
%@ 978-1-61208-746-7
%3 inproceedings
%F securware_19_vault
%K privacy; time series; projection; selection; aggregation; interpolation; smoothing; information emphasization; noise
%X While the Internet of Things (IoT) is a key driver for Smart Services that greatly facilitate our everyday life, it also poses a serious threat to privacy. Smart Services collect and analyze a vast amount of (partly private) data and thus gain valuable insights concerning their users. To prevent this, users have to balance service quality (i.e., reveal a lot of private data) and privacy (i.e., waive many features). Current IoT privacy approaches do not reflect this discrepancy properly and are often too restrictive as a consequence. For this reason, we introduce VAULT, a new approach for the protection of private data. VAULT is tailored to time series data as used by the IoT. It achieves a good tradeoff between service quality and privacy. For this purpose, VAULT applies five different privacy techniques. Our implementation of VAULT adopts a Privacy by Design approach.
%Z SECURWARE 2019 Best Paper Award
%U https://thinkmind.org/index.php?view=article&articleid=securware_2019_3_10_30031