A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications
| Autor: | Boxin Wan, Minghui Yang, Junqi Guo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Information privacy
IoT wearable devices k-anonymity differential privacy data publishing Physics and Astronomy (miscellaneous) Computer science General Mathematics Data_MISCELLANEOUS Wearable computer 02 engineering and technology Data publishing Encryption Data acquisition 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) QA1-939 Differential privacy Wearable technology business.industry 020206 networking & telecommunications Chemistry (miscellaneous) 020201 artificial intelligence & image processing business Mathematics Computer network |
| Zdroj: | Symmetry; Volume 13; Issue 6; Pages: 1043 Symmetry, Vol 13, Iss 1043, p 1043 (2021) |
| ISSN: | 2073-8994 |
| DOI: | 10.3390/sym13061043 |
| Popis: | With the rapid development of the Internet of Things (IoT), wearable devices have become ubiquitous and interconnected in daily lives. Because wearable devices collect, transmit, and monitor humans’ physiological signals, data privacy should be a concern, as well as fully protected, throughout the whole process. However, the existing privacy protection methods are insufficient. In this paper, we propose a practical privacy-preserving mechanism for physiological signals collected by intelligent wearable devices. In the data acquisition and transmission stage, we employed existing asymmetry encryption-based methods. In the data publishing stage, we proposed a new model based on the combination and optimization of k-anonymity and differential privacy. An entropy-based personalized k-anonymity algorithm is proposed to improve the performance on processing the static and long-term data. Moreover, we use the symmetry of differential privacy and propose the temporal differential privacy mechanism for real-time data to suppress the privacy leakage while updating data. It is proved theoretically that the combination of the two algorithms is reasonable. Finally, we use smart bracelets as an example to verify the performance of our mechanism. The experiment results show that personalized k-anonymity improves up to 6.25% in terms of security index compared with traditional k-anonymity, and the grouping results are more centralized. Moreover, temporal differential privacy effectively reduces the amount of information exposed, which protects the privacy of IoT-based users. |
| Databáze: | OpenAIRE |
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