Real time implementation of k fake location generation algorithm to protect location privacy in location based services

Kini, Apurva K.; Kulkarni, Shrirang Ambaji

doi:10.1109/icacci.2017.8125831

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…They concentrated on the mobility similarity metric and ensured the tradeoff between location privacy and utility. Kini et al [21] considered a k-anonymity-based location generation algorithm that takes into account the data characteristics in real world and implemented the algorithm in realistic mobility scenarios, but the authors studied the complexity and communication overhead of the algorithm without discussing the effect of privacy protection. Peng et al [22] proposed an enhanced location privacy preserving (ELPP) scheme that doesn't require fully trusted entity.…”

Section: Related Workmentioning

confidence: 99%

“…In the past few years, many protection schemes have been proposed to protect the trajectories for continuous LBS or the trajectory publication [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. The data of a trajectory is composed of a location dataset which includes a series of locations in chronological order, thus, the trajectory protection means protecting the chronological location data.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory Protection Schemes Based on a Gravity Mobility Model in IoT

Liu

Zhang

et al. 2019

Electronics

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With the proliferation of the Internet-of-Things (IoT), the users’ trajectory data containing privacy information in the IoT systems are easily exposed to the adversaries in continuous location-based services (LBSs) and trajectory publication. Existing trajectory protection schemes generate dummy trajectories without considering the user mobility pattern accurately. This would cause that the adversaries can easily exclude the dummy trajectories according to the obtained geographic feature information. In this paper, the continuous location entropy and the trajectory entropy are defined based on the gravity mobility model to measure the level of trajectory protection. Then, two trajectory protection schemes are proposed based on the defined entropy metrics to protect the trajectory data in continuous LBSs and trajectory publication, respectively. Experimental results demonstrate that the proposed schemes have a higher level than the enhanced dummy-location selection (enhance-DLS) scheme and the random scheme.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory Protection Schemes Based on a Gravity Mobility Model in IoT

Liu

Zhang

et al. 2019

Electronics

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“…About the location of devices, article [16] has implemented a real-time model to protect the location privacy of the user by considering characteristics of the real world. The aim of article [17] is to acknowledge the combined power of big data, position, motion sensors present in an everyday mobile phone while defocusing the use of GPS and lessening the privacy concern.…”

Section: Related Workmentioning

confidence: 99%

Data collection scheme with minimum cost and location of emotional recognition edge devices

Jin

Qian

Chen

2019

Pers Ubiquit Comput

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This paper develops a real-time and reliable data collection system for big scale emotional recognition systems. Based on the data sample set collected in the initialization stage and by considering the dynamic migration of emotional recognition data, we design an adaptive Kth average device clustering algorithm for migration perception. We define a sub-modulus weight function, which minimizes the sum of the weights of the subsets covered by a cover to achieve high-precision device positioning. Combining the energy of the data collection devices and the energy of the wireless emotional device, we balance the data collection efficiency and energy consumption, and define a minimum access number problem based on energy and storage space constraints. By designing an approximate algorithm to solve the approximate minimum Steiner point problem, the continuous collection of emotional recognition data and the connectivity of data acquisition devices are guaranteed under the energy constraint of wireless devices. We validate the proposed algorithms through simulation experiments using different emotional recognition systems and different data scale. Furthermore, we analyze the proposed algorithms in terms of topology for devices classification, location accuracy, and data collection efficiency by comparing with the Bayesian classifier-based expectation maximization algorithm, the background difference-based moving target detection arithmetic averaging algorithm, and the Hungarian algorithm for solving the assignment problem.

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