A Scalable Data Hiding Scheme Using Hilbert Space Curve and Chaos

Yadav, Gyan Singh; Ojha, Aparajita

doi:10.1109/trustcom.2015.463

Cited by 2 publications

(3 citation statements)

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“…Using Hilbert curve to divide the space is relatively fast and capable of resisting speculative attacks [35]. However, due to the uneven distribution of spatial points and sparsity, the space of the Hilbert curve partition is slightly larger than the space based on the KNN method [36], which results in larger errors in the calculation of spatial point anonymity and relatively high computational time. Therefore, we need an efficient method that maintains both the spatial nature of the Hilbert curve and the ability to produce relatively small partition.…”

Section: Hilbert Curvementioning

confidence: 99%

“…But, in fact, the division in d should be better. These two comparative examples in Figure 4 illustrate that Hoa Ngo's dividing method based on the number of grids proposed in [36] is prone to be inaccurate for some distributions, so we recommend using a more accurate method to divide location set, which can indicate the difference in distribution.…”

Section: Differential Privacy Spatial Divisionmentioning

confidence: 99%

“…If the trajectory's length is , the number of all possible trajectories is [| |/ ] . Suppose that the number of partitions is 80 in each̃; the total number of possible trajectories with the length 36, like our setting in experiment, which we have to consider is 80 36 . It is obviously unacceptable if we add Laplace noise on all of these trajectories.…”

Section: Differential Private Datamentioning

confidence: 99%

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Research on Trajectory Data Releasing Method via Differential Privacy Based on Spatial Partition

Han

Xiong

Zhang

2018

Security and Communication Networks

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A number of security and privacy challenges of cyber system are arising due to the rapidly evolving scale and complexity of modern system and networks. The cyber system is a fundamental ingredient for Internet of Things (IoT) and smart city which are driven by huge amount of data. These data carry a lot of information for mining and analysis, especially trajectory data. If unprotected trajectory data is released, it may disclose user’s personal privacy, such as home, religion, and behavior mode, which will endanger their personal security. Until now, many methods for protecting trajectory information have been proposed. However, these methods have the following deficiencies: (i) they cannot defend against speculative attacks if the attacker’s background knowledge is maximized; (ii) when studying the problem, they made some strong assumptions that did not match the reality; (iii) the implementation algorithm is complicated and the time complexity is high, which means that data cannot be executed quickly when the amount is large. So, in this paper, we propose a spatial partition based method to publish trajectory data via differential privacy. First, by exponential mechanism, we divide location set at the same time into different partitions fast and accurately. Then we propose another effective method to release trajectory in a differential private manner. We design experiment based on the real-life dataset and compare it with existing method. The results show that the trajectory dataset released by our algorithm has better usability while ensuring privacy.

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Section: Hilbert Curvementioning

confidence: 99%

Section: Differential Privacy Spatial Divisionmentioning

confidence: 99%

Section: Differential Private Datamentioning

confidence: 99%

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