The copyright protection method of 3D heritages model based on a set of certain parameters DWT

The copyright protection method of 3D weapon model based on discrete Hartley transform

Advances in Electrical and Electronics Engineering ◽

10.2495/iceee140461 ◽

2014 ◽

Author(s):

Qi Hu ◽

Lang Zhai

Keyword(s):

Copyright Protection ◽

Discrete Hartley Transform ◽

Hartley Transform ◽

Model Based ◽

Protection Method

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A Robust Blind Quantum Copyright Protection Method for Colored Images Based on Owner’s Signature

International Journal of Theoretical Physics ◽

10.1007/s10773-017-3412-9 ◽

2017 ◽

Vol 56 (8) ◽

pp. 2562-2578 ◽

Cited By ~ 14

Author(s):

Shahrokh Heidari ◽

Reza Gheibi ◽

Monireh Houshmand ◽

Koji Nagata

Keyword(s):

Copyright Protection ◽

Protection Method

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Privacy Protection Method for Vehicle Trajectory Based on VLPR Data

Journal of Advanced Transportation ◽

10.1155/2020/6026140 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

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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