%0 Conference Paper %1 securware_19_vault %A Stach, Christoph %B Proceedings of the Thirteenth International Conference on Emerging Security Information, Systems and Technologies %C Nice %D 2019 %E Rass, Stefan %E Yee, George %I IARIA %K aggregation information_emphasization interpolation noise privacy projection selection smoothing time_series %P 41–46 %T VAULT: A Privacy Approach towards High-Utility Time Series Data %U https://thinkmind.org/index.php?view=article&articleid=securware_2019_3_10_30031 %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. %@ 978-1-61208-746-7

@inproceedings{securware_19_vault, abstract = {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.}, added-at = {2020-09-21T11:45:55.000+0200}, address = {Nice}, author = {Stach, Christoph}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/24754d77d8e7530789823aa6afdd46aea/christophstach}, booktitle = {Proceedings of the Thirteenth International Conference on Emerging Security Information, Systems and Technologies}, editor = {Rass, Stefan and Yee, George}, interhash = {eea72d5fbe5f29fe4ece3a895700c8d5}, intrahash = {4754d77d8e7530789823aa6afdd46aea}, isbn = {978-1-61208-746-7}, keywords = {aggregation information_emphasization interpolation noise privacy projection selection smoothing time_series}, month = oct, note = {SECURWARE 2019 Best Paper Award}, pages = {41–46}, publisher = {IARIA}, series = {SECURWARE '19}, timestamp = {2020-09-21T09:45:55.000+0200}, title = {VAULT: A Privacy Approach towards High-Utility Time Series Data}, url = {https://thinkmind.org/index.php?view=article&articleid=securware_2019_3_10_30031}, year = 2019 }