Differentially private publication of general time-serial trajectory data

Hua, Jingyu; Gao, Yue

doi:10.1109/infocom.2015.7218422

Cited by 72 publications

(64 citation statements)

References 16 publications

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“…ensure the availability of the trajectory data after the differential privacy mechanism, we try to process the location set in the original dataset by dividing the regions and merging them. Hua J [44] pointed out that the k-means method can be used for clustering in the location set, but the traditional k-means method needs to determine the number of clusters k in advance. And the selection of the initial center point of k-means cluster has a great influence on clustering effect and time.…”

Section: Differential Privacy Spatial Divisionmentioning

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: Differential Privacy Spatial Divisionmentioning

confidence: 99%

“…Obviously, the smaller the Hausdorff distance, the higher the usability. We use the DPR algorithm in [44] as a comparison experiment. DPR is similar to the algorithm framework of our proposal.…”

Section: Utility Metricmentioning

confidence: 99%

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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“…Hua et al [62] proposed a differentially private algorithm for location generalization in time-series trajectory data (human mobility traces) using exponential mechanism for processing. The algorithm combines nodes using the trajectory distances to merge sites at the same time points.…”

Section: Privacy and Securitymentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. While these data share some common properties with traditional big data, they have their own unique characteristics and provide numerous advantages for academic research and practical applications. This article reviews the recent advances and trends in the field of wireless big data. Due to space constraints, this survey is not intended to cover all aspects in this field, but to focus on the data aided transmission, data driven network optimization and novel applications. It is expected that the survey will help the readers to understand this exciting and emerging research field better. Moreover, open issues and promising future directions are also identified.

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“…Due to the fact that the fog client typically offloads its tasks to the nearest fog node, attackers can refer that the location of fog clients resulting in a location leak. Hua et al 24 proposed a location data processing technology based on differential privacy. One way to do this is by using virtual locations for privacy protection.…”

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confidence: 99%

Differential privacy–based location privacy enhancing in edge computing

Miao

Jing

Song

2018

Concurrency and Computation

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In the era of edge computing, real-time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location-based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy-aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP-H) two-dimensional spatial data query transmission is proposed.First, a noise quadtree is established and the privacy budget is divided according to the tree level.Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two-dimensional data in the area can be converted into one-dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D, ) − LP, the average runtime can be reduced by 15%-20%, and the average relative error is reduced by 20%. KEYWORDS differential privacy, Hilbert curve, location-based service, mobile edge computing, privacy aware, quadtree INTRODUCTIONWith the development of the Internet of Things (IoT) and cloud computing, the amount of data on the edge network is rapidly growing. Therefore, it is more efficient to process the data at the edge of the network. However, the development of network bandwidth is slower compared with the powerful computing ability of cloud services. The amount of data is growing rapidly, and time consumption in data transmission has become the main challenge that restricts the cloud computing applications. In cloud computing model, the devices at the edge often only act as consumers; however, people often generate data from the devices they use. 1 This shift from data consumers to data consumers/producers requires more functionality on the edge node. However, at the edge of the network, user privacy and data security are among the most important requirements. If IoT is deployed in the home, some privacy information can be obtained from the user data, such as by reading user electric meter and water meter data to determine whether there are people in the room. By obtaining location data, people can estimate someone's home address, lifestyle, social relationships, and more. Therefore, the disclosure of this personal information to attackers can pose a serious threat to the privacy of users. The development of edge computing will promote a variety of intelligent applications, which were impractical in the past due to network ...

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Differentially private publication of general time-serial trajectory data

Cited by 72 publications

References 16 publications

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

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

Survey of wireless big data

Differential privacy–based location privacy enhancing in edge computing

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