Practical K Nearest Neighbor Query Scheme with Two-Party Guarantees in Road Networks

Zhou, Changli; Jiang, Wenxian; Tian, Hui

doi:10.1109/trustcom/bigdatase.2018.00181

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…Several remarkable overviews have summarized the research focuses of location privacy in mobile IoT and potential hotspots in the future [2], [6]- [8]. Normally, there are mainly two categories of location protections in locationbased mobile IoT: cloaking region [3], [4], [9], [10] and dummy [11]- [14].…”

Section: A Location Privacy Protections In Mobile Iotmentioning

confidence: 99%

“…On the other hand, dummies are pseudo locations instead of the actual sensing location by sensors in a mobile service request, dummies are usually generated around the actual location by smart components [11]. A typical service request with dummy is first proposed by Yiu [12], which is a valid solution to guarantee location privacy, but it has the issues of inaccuracy, failing to achieve k-anonymity and location diversity.…”

Section: A Location Privacy Protections In Mobile Iotmentioning

confidence: 99%

“…A typical service request with dummy is first proposed by Yiu [12], which is a valid solution to guarantee location privacy, but it has the issues of inaccuracy, failing to achieve k-anonymity and location diversity. To deal with these issues, Zhou has improved the service accuracy with dummy and presented a query scheme in mobile IoT scenario [11]. Gong and Huang [13], [14] have made significant improvements of Yiu's work in the aspect of achieving k-anonymity.…”

Section: A Location Privacy Protections In Mobile Iotmentioning

confidence: 99%

“…To the best of our knowledge, this is the first paper to deal with this issue. We will achieve top-K query maintaining PIR by introducing network coding (NC) [34] with intelligent vehicle cooperation in mobile IoT scenario based on our previous work [11].…”

Section: Challenges Of Top-k Pir Query In Mobile Iotmentioning

confidence: 99%

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A Top-K Query Scheme With Privacy Preservation for Intelligent Vehicle Network in Mobile IoT

et al. 2020

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The emergence of mobile Internet of Things (IoT) has brought much more convenience to our daily life than ever before, modern intelligent vehicles are usually equipped with various mobile IoT components or devices. However, when we are enjoying the convenience from the rapid development of mobile IoT services, our privacy can be misused by attackers in an easier way. In this paper, we devise a query scheme with intelligent vehicle privacy guarantees, which enables a vehicle to acquire accurate query service from the service provider (SP) without providing its explicit private information, such as location or query interest privacy. And more than that, we introduce network coding for the first time to make our scheme applicable to a more sophisticated top-K query by multiple vehicles cooperation in mobile IoT. Furthermore, we also consider the unnecessary reveal issue of service data at SP side since the data is its asset. Performance analysis and experiments verify the validity of our scheme and demonstrate a better accuracy and efficiency compared with existing solutions in mobile IoT scenario.INDEX TERMS Mobile Internet of Things (IoT), intelligent vehicle network, privacy preservation, top-K query, oblivious transfer (OT), network coding (NC), private information retrieval (PIR).

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Section: A Location Privacy Protections In Mobile Iotmentioning

confidence: 99%

Section: A Location Privacy Protections In Mobile Iotmentioning

confidence: 99%

Section: A Location Privacy Protections In Mobile Iotmentioning

confidence: 99%

Section: Challenges Of Top-k Pir Query In Mobile Iotmentioning

confidence: 99%

See 2 more Smart Citations

A Top-K Query Scheme With Privacy Preservation for Intelligent Vehicle Network in Mobile IoT

et al. 2020

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“…Hence, the centralized strategy is not suited in wireless sensor networks. (ii) Complex Queries-The existing system made an attempt in query processing in the shared fashion in wireless sensor networks like range query, curve query, and top-k query [7]. Moreover, the queries are more complicated than those used in the conventional wireless sensor networks in the latest IoT techniques.…”

Section: Introductionmentioning

confidence: 99%

Trust and QoS-Driven Query Service Provisioning Using Optimization

Narmatha¹,

Karthikeyan²

2023

Intelligent Automation &Amp; Soft Computing

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The growing advancements with the Internet of Things (IoT) devices handle an enormous amount of data collected from various applications like healthcare, vehicle-based communication, and smart city. This research analyses cloud-based privacy preservation over the smart city based on query computation. However, there is a lack of resources to handle the incoming data and maintain them with higher privacy and security. Therefore, a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment. A querying service model is proposed to handle the incoming data collected from various environments as the data is not so trusted and highly sensitive towards vulnerability. If handling privacy, other inter-connected metrics like efficiency are also essential, which must be considered to fulfil the privacy requirements. Therefore, this work provides a query-based service model and clusters the query to measure the relevance of frequently generated queries. Here, a Bag of Query (BoQ) model is designed to collect the query from various sources. Validation is done with a descriptive service provisioning model to cluster the query and extract the query's summary to get the final results. The processed data is preserved over the cloud storage system and optimized using an improved Grey Wolf Optimizer (GWO). It is used to attain global and local solutions regarding privacy preservation. The iterative data is evaluated without any over-fitting issues and computational complexity due to the tremendous data handling process. Based on this analysis, metrics like privacy, efficiency, computational complexity, the error rate is analyzed. The simulation is done with a MATLAB 2020a environment. The proposed model gives a better trade-off in contrast to existing approaches.

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K-Nearest Neighbor Privacy Protection Query for Distributed Storage in Location-based Service

Qin

Wang

2021

Wireless Pers Commun

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Practical K Nearest Neighbor Query Scheme with Two-Party Guarantees in Road Networks

Cited by 4 publications

References 16 publications

A Top-K Query Scheme With Privacy Preservation for Intelligent Vehicle Network in Mobile IoT

A Top-K Query Scheme With Privacy Preservation for Intelligent Vehicle Network in Mobile IoT

Trust and QoS-Driven Query Service Provisioning Using Optimization

K-Nearest Neighbor Privacy Protection Query for Distributed Storage in Location-based Service

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