Spatio-Temporal Location Privacy Quantification for Vehicular Networks

This study examines the translational deictic shifts in three Arabic translations of the English novel Wuthering Heights and the effect of this shift in the spatio-temporal point of view and stylistic features of the original. The study finds shift in spatial and temporal deixis that manifests a strong tendency towards increasing the ‘level of enunciation’ of narrators’ spatial and temporal location within the narrative. This shift brings the main narrator closer to the other characters in temporal, spatial, and mental space, hence increasing her involvement in events and empathy towards characters. At the same time, it distances the outside frame narrator, who has limited contact with characters, and increases his detachment and antipathy. In both cases more is revealed of narrator-character relationships and the narrator’s evaluations, leading to a more subjective narrative mood. It is hoped that the study will be applicable to different translated literary works to compare the findings and gain more understanding on the norms of English-Arabic fiction translation.

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Location Privacy in Wireless Vehicular Networks

Location Privacy in Mobile Applications - SpringerBriefs on Cyber Security Systems and Networks ◽

10.1007/978-981-13-1705-7_5 ◽

2018 ◽

pp. 77-97

Author(s):

Bo Liu ◽

Wanlei Zhou ◽

Tianqing Zhu ◽

Yong Xiang ◽

Kun Wang

Keyword(s):

Vehicular Networks ◽

Location Privacy

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Toward location privacy protection in Spatial crowdsourcing

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719830568 ◽

2019 ◽

Vol 15 (3) ◽

pp. 155014771983056 ◽

Cited By ~ 1

Author(s):

Hang Ye ◽

Kai Han ◽

Chaoting Xu ◽

Jingxin Xu ◽

Fei Gui

Keyword(s):

Privacy Protection ◽

Location Privacy ◽

Data Sets ◽

Relative Location ◽

Mathematical Transformation ◽

Real World Data ◽

Spatial Crowdsourcing ◽

Effectiveness And Efficiency ◽

Spatio Temporal ◽

Location Privacy Protection

Spatial crowdsourcing is an emerging outsourcing platform that allocates spatio-temporal tasks to a set of workers. Then, the worker moves to the specified locations to perform the tasks. However, it usually demands workers to upload their location information to the spatial crowdsourcing server, which unavoidably attracts attention to the privacy-preserving of the workers’ locations. In this article, we propose a novel framework that can protect the location privacy of the workers and the requesters when assigning tasks to workers. Our scheme is based on mathematical transformation to the location while providing privacy protection to workers and requesters. Moreover, to further preserve the relative location between workers, we generate a certain amount of noise to interfere the spatial crowdsourcing server. Experimental results on real-world data sets show the effectiveness and efficiency of our proposed framework.

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