VAULT: A Privacy Approach towards High-Utility Time Series Data
C. Stach. Proceedings of the Thirteenth International Conference on Emerging Security Information, Systems and Technologies, page 41–46. Nice, IARIA, (October 2019)SECURWARE 2019 Best Paper Award.
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.
%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
}
