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.

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 Cache-Based Privacy Preserving Solution for Location and Content Protection in Location-Based Services

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4651
Author(s):  
Yuanbo Cui ◽  
Fei Gao ◽  
Wenmin Li ◽  
Yijie Shi ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Personal Information ◽  
Single Point ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Content Protection ◽  
Query Privacy ◽  
Service Data

Location-Based Services (LBSs) are playing an increasingly important role in people’s daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users’ privacy, most of them just focus on the user’s location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user’s privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user’s privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users’ location and query privacy with a lower communication cost and better quality of service.

scholarly journals Privacy Preserving in LBS resilient to Location Injection Attacks

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3621-3625
Keyword(s):  
Location Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Injection Attacks ◽  
Point Of Interest ◽  
The Right ◽  
User Location

Location-based services have become indispensable in people's life with expeditious development of technology. Location-based services(LBS) refers to the services provided by the LBS servers with regards to area and point of interest. Alternatively, the LBS means getting the right information at the right place in time. Protecting user location privacy is the most challenging factor in LBS. This survey aims to present various mechanisms in preserving the user's location privacy and proposes a mechanism for preserving the privacy of user location and query against the location injection attacks. We will be discussing credibility based k- anonymity mechanism for preserving the location of the user and homomorphic encryption for preserving the query of the user resilient location injection attacks in this paper.

The Usage of Social Network to Students: Does It Improve Student’s Education Quality During COVID-19 Outbreak

2020 ◽  
Vol 17 (12) ◽  
pp. 5224-5228
Author(s):  
Indraah Kolandaisamy ◽  
Raenu Kolandaisamy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Education Quality ◽  
Quality Of Education ◽  
Online Classes ◽  
Outbreak Period ◽  
Technology Advancement ◽  
The Relationship

In the era of technology advancement and COVID-19 outbreak period, all physical classes have been converted to online classes through social network platforms. Having online classes through social networks are actually very comfortable and flexible for students as they can have their classes at various places. This paper is focuses on the relationship between usages of social network and the quality of education during COVID-19 outbreak.

Unraveling the Taste Fabric of Social Networks

2009 ◽  
pp. 1521-1546
Author(s):  
Hugo Liu ◽  
Pattie Maes ◽  
Glorianna Davenport
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Social Connectedness ◽  
Natural Language Text ◽  
Cross Domain ◽  
Semantic Mining ◽  
The Rich ◽  
Language Text

Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness—the rich meanings bottled within social network profiles themselves imply deeper patterns of culture and taste. If these latent semantic fabrics of taste could be harvested formally, the resultant resource would afford completely novel ways for representing and reasoning about web users and people in general. This paper narrates the theory and technique of such a feat—the natural language text of 100,000 social network profiles were captured, mapped into a diverse ontology of music, books, films, foods, etc., and machine learning was applied to infer a semantic fabric of taste. Taste fabrics bring us closer to improvisational manipulations of meaning, and afford us at least three semantic functions—the creation of semantically flexible user representations, cross-domain taste-based recommendation, and the computation of taste-similarity between people— whose use cases are demonstrated within the context of three applications—the InterestMap, Ambient Semantics, and IdentityMirror. Finally, we evaluate the quality of the taste fabrics, and distill from this research reusable methodologies and techniques of consequence to the semantic mining and Semantic Web communities.

scholarly journals Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Madhuri Siddula ◽  
Yingshu Li ◽  
Xiuzhen Cheng ◽  
Zhi Tian ◽  
Zhipeng Cai
Keyword(s):  
Social Networking Sites ◽  
Location Privacy ◽  
Location Based Services ◽  
Sensitive Information ◽  
Context Aware ◽  
Friendship Networks ◽  
Mobile Social Network ◽  
User Privacy ◽  
The Cost ◽  
Privacy Issues

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

scholarly journals Factors Affecting Loyalty of Mobile Social Networks' Users

2017 ◽  
Vol 13 (1) ◽  
pp. 66-81 ◽  
Author(s):  
Nastaran Hajiheydari ◽  
Babak Hazaveh Hesar Maskan ◽  
Mahdi Ashkani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Structural Equation ◽  
Online Survey ◽  
Equation Modeling ◽  
Mobile Social Networks ◽  
Mobile Social Network ◽  
Key Factors ◽  
Factors Affecting ◽  
Social Applications

Increasing world-wide trends of using mobile social networks and the rise of competition between different social applications makes it essential for social network providers and marketers to identify the key factors leading to user loyalty. The purpose of this paper is to identify the key factors that affect the loyalty of mobile social networks users. The proposed model was tested through structural equation modeling techniques and an online survey. The sample consisted of 388 mobile social networks users in Iran. The results indicate that sociability, entertainment and fashion are primary drivers of attitude toward a mobile social network. The results also show the significant role of attitude and satisfaction on consumer loyalty. This study helps both marketers and mobile social network providers know the key drivers of customer loyalty in order to tailor their marketing efforts and communication strategies.

scholarly journals An Efficient and Privacy-Preserving Multiuser Cloud-Based LBS Query Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lu Ou ◽  
Hui Yin ◽  
Zheng Qin ◽  
Sheng Xiao ◽  
Guangyi Yang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
User Privacy ◽  
User Revocation ◽  
And Performance ◽  
Cloud Infrastructures

Location-based services (LBSs) are increasingly popular in today’s society. People reveal their location information to LBS providers to obtain personalized services such as map directions, restaurant recommendations, and taxi reservations. Usually, LBS providers offer user privacy protection statement to assure users that their private location information would not be given away. However, many LBSs run on third-party cloud infrastructures. It is challenging to guarantee user location privacy against curious cloud operators while still permitting users to query their own location information data. In this paper, we propose an efficient privacy-preserving cloud-based LBS query scheme for the multiuser setting. We encrypt LBS data and LBS queries with a hybrid encryption mechanism, which can efficiently implement privacy-preserving search over encrypted LBS data and is very suitable for the multiuser setting with secure and effective user enrollment and user revocation. This paper contains security analysis and performance experiments to demonstrate the privacy-preserving properties and efficiency of our proposed scheme.

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