TY  - CONF
AU  - Stach, Christoph
A2  - Rass, Stefan
A2  - Yee, George
T1  - VAULT: A Privacy Approach towards High-Utility Time Series Data
T2  - Proceedings of the Thirteenth International Conference on Emerging Security Information, Systems and Technologies
PB  - IARIA
AD  - Nice
Y1  - 2019/october
SP  - 41
EP  - 46
UR  - https://thinkmind.org/index.php?view=article&articleid=securware_2019_3_10_30031
KW  - privacy; time series; projection; selection; aggregation; interpolation; smoothing; information emphasization; noise
N2  - 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.
ER  -