Privacy-preserving governmental data publishing: A fog-computing-based differential privacy approach

2019 ◽  
Vol 90 ◽  
pp. 158-174 ◽  
Author(s):  
Chunhui Piao ◽  
Yajuan Shi ◽  
Jiaqi Yan ◽  
Changyou Zhang ◽  
Liping Liu
Keyword(s):  
Differential Privacy ◽  
Fog Computing ◽  
Privacy Preserving ◽  
Data Publishing

scholarly journals PCP: A Privacy-Preserving Content-Based Publish–Subscribe Scheme With Differential Privacy in Fog Computing

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 17962-17974 ◽  
Author(s):  
Qixu Wang ◽  
Dajiang Chen ◽  
Ning Zhang ◽  
Zhe Ding ◽  
Zhiguang Qin
Keyword(s):  
Differential Privacy ◽  
Fog Computing ◽  
Privacy Preserving

scholarly journals A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1043
Author(s):  
Junqi Guo ◽  
Minghui Yang ◽  
Boxin Wan
Keyword(s):  
Data Privacy ◽  
Differential Privacy ◽  
Rapid Development ◽  
Wearable Devices ◽  
Privacy Preserving ◽  
Physiological Signals ◽  
Data Publishing ◽  
Time Data ◽  
Daily Lives ◽  
Whole Process

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.

scholarly journals Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Huo ◽  
Chengtao Yong ◽  
Yanfei Lu
Keyword(s):  
Real Time ◽  
Data Aggregation ◽  
Differential Privacy ◽  
Fog Computing ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Data Availability ◽  
Time Data ◽  
Real Time Data ◽  
Cloud Servers

In the Internet of Things (IoT), aggregation and release of real-time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real-time stream data aggregation framework with adaptive ω-event differential privacy (Re-ADP). Based on adaptive ω-event differential privacy, the framework can protect any data collected by sensors over any dynamic ω time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.

Privacy-preserving Streaming Truth Discovery in Crowdsourcing with Differential Privacy

2021 ◽  
pp. 1-1
Author(s):  
Dan Wang ◽  
Ju Ren ◽  
Zhibo Wang ◽  
Xiaoyi Pang ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Privacy Preserving ◽  
Truth Discovery
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