A caching and spatial<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e566" altimg="si254.gif"><mml:mi>K</mml:mi></mml:math>-anonymity driven privacy enhancement scheme in continuous location-based services

Zhang, Shaobo; Li, Xiong; Tan, Zhiyuan; Peng, Tao; Wang, Guojun

doi:10.1016/j.future.2018.10.053

Cited by 157 publications

(78 citation statements)

References 29 publications

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“…Zhou et al in [35] researched on the task scheduling onto a heterogeneous multiprocessor system while considering the quality of security. In [36], a privacy enhancement scheme was proposed by Zhang et al to reduce the risk of exposure of user' information to untrusted location service provider. To handle the problem of user information leakage when distributed service recommendations are made, a recommendation approach named DistSR Amplify-LSH was proposed in [37].…”

Section: Related Workmentioning

confidence: 99%

Flexible automatic detection for security management of dispatching data network

et al. 2020

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Power grid dispatching is among the forefront issues in the power industry for it can highly influence the efficiency of electricity-related industries. At present, power grid dispatching is usually managed manually, which is quite time-consuming with the continuous growth of scale and complexity of electric power systems. To mitigate the time cost of power grid dispatching, an automatic power grid dispatching system is proposed. The system contains devices configuration detection, flexible and automatic device detection and vertical encryption to ensure the efficiency, adaptability and security of power gird dispatching. In this paper, modeling and algorithm details of the automatic power grid dispatching system are presented, and the effectiveness and efficiency of the proposed system was evaluated with power grid data in Kunming, China.

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Section: Related Workmentioning

confidence: 99%

Flexible automatic detection for security management of dispatching data network

et al. 2020

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“…The experimental results show that the destination prediction method has strong predictive ability and has effective protection against destination inference attacks. Zhang S proposed an enhanced user privacy scheme through caching and spatial-anonymity (CSKA) in continuous LBS [24]. It uses multilevel caching to reduce the risk of user information being exposed to untrusted location service providers.…”

Section: Related Workmentioning

confidence: 99%

Location recommendation privacy protection method based on location sensitivity division

Yin

et al. 2019

J Wireless Com Network

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Location-based recommendation services can provide users with convenient services, but this requires monitoring and collecting a large amount of location information. In order to prevent location information from being leaked after monitoring and collection, location privacy must be effectively protected. Therefore, this paper proposes a privacy protection method based on location sensitivity for location recommendation. This method uses location trajectories and check-in frequencies to set a threshold so as to classify location sensitivity levels. The corresponding privacy budget is then assigned based on the sensitivity to add Laplace noise that satisfies the differential privacy. Experimental results show that this method can effectively protect the user's location privacy and reduce the impact of differential privacy noise on service quality.

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“…As an effective privacy-preservation technique, K-anonymity is successfully applied in [15] to protect the sensitive data of users. The authors in [16] recruit Kanonymity technique to generalize the location information that users left in the past so as to protect the users' location privacy when making a recommendation decision.…”

Section: K-anonymitymentioning

confidence: 99%

Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education

Chen

Yan

2019

Wireless Communications and Mobile Computing

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Service recommendation has become one of the most effective approaches to quickly extract insightful information from big educational data. However, the sparsity of educational service quality data (from multiple platforms or parties) used to make service recommendations often leads to few even null recommended results. Moreover, to protect sensitive business information and obey laws, preserving user privacy during the abovementioned multisource data integration process is a very important but challenging requirement. Considering the above challenges, this paper integrates Locality-Sensitive Hashing (LSH) with hybrid Collaborative Filtering (HCF) techniques for robust and privacy-aware data sharing between different platforms involved in the cross-platform service recommendation process. Furthermore, to minimize the “False negative” recommended results incurred by LSH and enhance the success of recommended results, we propose two optimization strategies to reduce the probability that similar neighbours of a target user or similar services of a target service are overlooked by mistake. Finally, we conduct a set of experiments based on a real distributed service quality dataset, i.e., WS-DREAM, to validate the feasibility and advantages of our proposed recommendation approach. The extensive experimental results show that our proposal performs better than three competitive methods in terms of efficiency, accuracy, and successful rate while guaranteeing privacy-preservation.

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A caching and spatialK-anonymity driven privacy enhancement scheme in continuous location-based services

Cited by 157 publications

References 29 publications

Flexible automatic detection for security management of dispatching data network

Flexible automatic detection for security management of dispatching data network

Location recommendation privacy protection method based on location sensitivity division

Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education

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