Abstract
Recent advances in wireless sensor networks for ubiquitous health and activity monitoring systems have triggered the possibility of addressing human needs in smart environments through recognizing human real-time activities. While the nature of streams in such networks requires efficient recognition techniques, it is also subject to suspicious inference-based privacy attacks. In this paper, we propose a framework that efficiently recognizes human activities in smart homes based on spatiotemporal mining technique. In addition, we propose a technique to enhance the privacy of the collected human sensed activities using a modified version of micro-aggregation approach. An extensive validation of our framework has been performed on benchmark data sets yielding quite promising results in terms of accuracy and privacy-utility tradeoff.
Original language | English (US) |
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Article number | 7886290 |
Pages (from-to) | 3848-3859 |
Number of pages | 12 |
Journal | IEEE Access |
Volume | 5 |
DOIs | |
State | Published - 2017 |
Externally published | Yes |
Keywords
- Internet of Things
- data mining
- data privacy
- healthcare
- smart home
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering