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VAULT: A Privacy Approach Towards High-Utility Time Series Data

Authors:
Christoph Stach

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.

Pages: 41 to 46

Copyright: Copyright (c) IARIA, 2019

Publication date: October 27, 2019

Published in: conference

ISSN: 2162-2116

ISBN: 978-1-61208-746-7

Location: Nice, France

Dates: from October 27, 2019 to October 31, 2019