Collaborative trajectory privacy preserving scheme in location-based services

Peng, Tao; Q, Liu; Meng, Dacheng; Wang, Guojun

doi:10.1016/j.ins.2016.08.010

Cited by 151 publications

(82 citation statements)

References 20 publications

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“…Moreover, we propose a slope one algorithm based on the fusion of trusted data and user similarity. The algorithm we proposed can applyed in many applications, such as the recommendation system for social networks (Peng et al 2017a;Cai et al 2017;Jiang et al 2016), or loaction-based services (Peng et al 2017b).…”

Section: Resultsmentioning

confidence: 99%

A trust-based collaborative filtering algorithm for E-commerce recommendation system

Jiang

Cheng

Yang

et al. 2018

J Ambient Intell Human Comput

183

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The rise of e-commerce has not only given consumers more choice but has also caused information overload. In order to quickly find favorite items from vast resources, users are eager for technology by which websites can automatically deliver items in which they may be interested. Thus, recommender systems are created and developed to automate the recommendation process. In the field of collaborative filtering recommendations, the accuracy requirement of the recommendation algorithm always makes it complex and difficult to implement one algorithm. The slope one algorithm is not only easy to implement but also works efficient and effective. However, the prediction accuracy of the slope one algorithm is not very high. Moreover, the slope one algorithm does not perform so well when dealing with personalized recommendation tasks that concern the relationship among users. To solve these problems, we propose a slope one algorithm based on the fusion of trusted data and user similarity, which can be deployed in various recommender systems. This algorithm comprises three procedures. First, we should select trusted data. Second, we should calculate the similarity between users. Third, we need to add this similarity to the weight factor of the improved slope one algorithm, and then, we get the final recommendation equation. We have carried out a number of experiments with the Amazon dataset, and the results prove that our recommender algorithm performs more accurately than the traditional slope one algorithm.

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Section: Resultsmentioning

confidence: 99%

A trust-based collaborative filtering algorithm for E-commerce recommendation system

Jiang

Cheng

Yang

et al. 2018

J Ambient Intell Human Comput

183

View full text Add to dashboard Cite

show abstract

“…In particular, we consider the solutions where users collaborate to hide information from the server [10,29,32,33], which share similarities with Fougere. In particular, Show et al [10] use communication over peer-to-peer (P2P) protocols, Shokri et al [32] use WiFi Access Point connection, and finally, Shokri et al [33] and Peng et al [29] propose to use Wi-Fi Direct communications. Yet, such approaches are not widely adopted by LBS solutions as they fail to demonstrate their effectiveness in a realistic deployment.…”

Section: Fig 1 Anatomy Of a Mobile Crowdsourcing Campaignmentioning

confidence: 99%

FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps

Meftah

Rouvoy

Chrisment

2019

Distributed Applications and Interoperable Systems

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“…However, it may leak privacy information through statistical inference, even though the perturbed data is hard to be reconstructed [8,[16][17][18][19][20][21][22][23]. However, many limitations exist for perturbation.…”

Section: Related Workmentioning

confidence: 99%

An information-aware visualization for privacy-preserving accelerometer data sharing

Xiao

Zhao

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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In the age of big data, plenty of valuable sensing data have been shared to enhance scientific innovation. However, this may cause unexpected privacy leakage. Although numerous privacy preservation techniques, such as perturbation, encryption, and anonymization, have been proposed to conceal sensitive information, it is usually at the cost of the application utility. Moreover, most of the existing works did not distinguished the underlying factors, such as data features and sampling rate, which contribute differently to utility and privacy information implied in the shared data. To well balance the application utility and privacy leakage for data sharing, we utilize mutual information and visualization techniques to analyze the impact of the underlying factors on utility and privacy, respectively, and design an interactive visualization tool to help users identify the appropriate solution to achieve the objectives of high application utility and low privacy leakage simultaneously. To illustrate the effectiveness of the proposed scheme and tool, accelerometer data collected from mobile devices have been adopted as an illustrative example. Experimental study has shown that feature selection and sampling frequency play dominant roles in reducing privacy leakage with much less reduction on utility, and the proposed visualization tool can effectively recommend the appropriate combination of features and sampling rates that can help users make decision on the trade-off between utility and privacy.

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Collaborative trajectory privacy preserving scheme in location-based services

Cited by 151 publications

References 20 publications

A trust-based collaborative filtering algorithm for E-commerce recommendation system

A trust-based collaborative filtering algorithm for E-commerce recommendation system

FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps

An information-aware visualization for privacy-preserving accelerometer data sharing

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