Optimal roadside unit placement with location privacy enhancement in vehicular social network

2015 ◽  
Author(s):  
Xumin Huang ◽  
Jiawen Kang ◽  
Rong Yu
Keyword(s):  
Social Network ◽  
Location Privacy ◽  
Roadside Unit

scholarly journals A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network

2018 ◽  
Vol 129 ◽  
pp. 368-371 ◽  
Author(s):  
Lina Ni ◽  
Yanfeng Yuan ◽  
Xiao Wang ◽  
Mengmeng Zhang ◽  
Jinquan Zhang
Keyword(s):  
Social Network ◽  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Social Network

scholarly journals The Location Privacy Preserving of Social Network Based on RCCAM Access Control

2018 ◽  
Vol 35 (sup1) ◽  
pp. 68-75 ◽  
Author(s):  
Xueqin Zhang ◽  
Qianru Zhou ◽  
Chunhua Gu ◽  
Liangxiu Han
Keyword(s):  
Social Network ◽  
Access Control ◽  
Location Privacy ◽  
Privacy Preserving

scholarly journals RPAR: Location Privacy Preserving via Repartitioning Anonymous Region in Mobile Social Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jinquan Zhang ◽  
Yanfeng Yuan ◽  
Xiao Wang ◽  
Lina Ni ◽  
Jiguo Yu ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Location Privacy ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Mobile Social Network ◽  
Location Service ◽  
Simulation Results ◽  
User Location

Applying the proliferated location-based services (LBSs) to social networks has spawned mobile social network (MSN) services that allow users to discover potential friends around them. While enjoying the convenience of MSN services, the mobile users also are confronted with the risk of location disclosure, which is a severe privacy preserving concern. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a repartitioning anonymous region for location privacy preserving (RPAR) scheme based on the central anonymous location which minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location. Furthermore, our scheme enables the users to get more accurate query results, thus improving the quality of the location service. Simulation results show that our proposed scheme can effectively reduce the area of anonymous regions and minimize the traffic.

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

Sign in / Sign up

Export Citation Format

Share Document