Bibliography | Stach, Christoph: VAULT: A Privacy Approach towards High-Utility Time Series Data. In: Proceedings of the Thirteenth International Conference on Emerging Security Information, Systems and Technologies: SECURWARE 2019. University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology. pp. 1-6, english. IARIA, October 2019. Article in Proceedings (Conference Paper).
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CR-Schema | K.4.1 (Computers and Society Public Policy Issues) D.4.6 (Operating Systems Security and Protection)
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Keywords | Privacy; Time Series; Projection; Selection; Aggregation; Interpolation; Smoothing; Information Emphasization; Noise |
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.
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Contact | Senden Sie eine E-Mail an Christoph.Stach@ipvs.uni-stuttgart.de |
Department(s) | University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems
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Project(s) | PATRON
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Entry date | September 5, 2019 |
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