scholarly journals Trajectory Privacy Protection Method Based on Location Service in Fog Computing

2019 ◽  
Vol 147 ◽  
pp. 463-467 ◽  
Author(s):  
Guangshun Li ◽  
Yanmin Yin ◽  
Junhua Wu ◽  
Shuaishuai Zhao ◽  
Dandan Lin
Keyword(s):  
Privacy Protection ◽  
Fog Computing ◽  
Location Service ◽  
Protection Method

scholarly journals Privacy Protection Scheme for Data Aggregation in Wireless Sensor Networks Based on PRDA+ Protocol

2018 ◽  
Vol 14 (11) ◽  
pp. 40
Author(s):  
Bohua Guo ◽  
Yanwu Zhang
Keyword(s):  
Data Aggregation ◽  
Privacy Protection ◽  
Random Number ◽  
Polynomial Regression ◽  
Homomorphic Encryption ◽  
Weighted Average ◽  
Base Station ◽  
Wireless Sensor ◽  
Protection Scheme ◽  
Protection Method

<p class="0abstract"><span lang="EN-US">To improve the data aggregation privacy protection scheme in wireless sensor network (WSN), a new scheme is put forward based on the privacy protection of polynomial regression and the privacy protection method based on the homomorphic encryption. The polynomial data aggregation (PRDA+) protocol is also proposed. In this scheme, the node and the base station will pre-deploy a secret key, and the random number generator encrypts the random number for the seed through the private key, which protects the privacy of the data. Then, by comparing the decrypted aggregate data through the correlation between the two metadata, the integrity protection of the data is realized. A weighted average aggregation scheme that can be verified is proposed. In view of the different importance of user information, the corresponding weights are set for each sensor node. EL Gamal digital signature is used to authenticate sensor nodes. The results show that the signature verification algorithm enables the scheme to resist data tampering and data denial, and to trace the source of erroneous data.</span></p>

Blockchain-based Data Integrity and Privacy Protection Method for Swarm Robotics

2021 ◽  
pp. 698-707
Author(s):  
Bangyin Li ◽  
Yutao Chen ◽  
Zhiqiang Zuo ◽  
Jie Huang
Keyword(s):  
Privacy Protection ◽  
Swarm Robotics ◽  
Data Integrity ◽  
Protection Method

scholarly journals Privacy Protection Method for Vehicle Trajectory Based on VLPR Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Hua Chen ◽  
Chen Xiong ◽  
Jia-meng Xie ◽  
Ming Cai
Keyword(s):  
Mathematical Model ◽  
Data Acquisition ◽  
Privacy Protection ◽  
Rapid Development ◽  
Original Data ◽  
License Plate ◽  
Model Based ◽  
Protection Method ◽  
Novel Approach ◽  
Vehicle Trajectory

With the rapid development of data acquisition technology, data acquisition departments can collect increasingly more data. Various data from government agencies are gradually becoming available to the public, including license plate recognition (VLPR) data. As a result, privacy protection is becoming increasingly significant. In this paper, an adversary model based on passing time, color, type, and brand of VLPR data is proposed. Through experimental analysis, the tracking probability of a vehicle’s trajectory can be more than 94% if utilizing the original data. To decrease the tracking probability, a novel approach called the (m, n)-bucket model based on time series is proposed since previous works, such as those using generalization and bucketization models, cannot deal with data with multiple sensitive attributes (SAs) or data with time correlations. Meanwhile, a mathematical model is established to expound the privacy protection principle of the (m, n)-bucket model. By comparing the average calculated linking probability of all individuals and the actual linking probability, it is shown that the mathematical model that is proposed can well expound the privacy protection principle of the (m, n)-bucket model. Extensive experiments confirm that our technique can effectively prevent trajectory privacy disclosures.

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