User-Defined Privacy Location-Sharing System in Mobile Online Social Networks

With the fusion of social networks and location-based services, location sharing is one of the most important services in mobile online social networks (mOSNs). In location-sharing services, users have to provide their location information to service provider. However, location information is sensitive to users, which may cause a privacy-preserving issue needs to be solved. In the existing research, location-sharing services, such as friends’ query, does not consider the attacks from friends. In fact, a user may not trust all of his/her friends, so just a part of his/her friends will be allowed to obtain the user’s location information. In addition, users’ location privacy and social network privacy should be guaranteed. In order to solve the above problems, we propose a new architecture and a new scheme called User-Defined Privacy Location-Sharing (UDPLS) system for mOSNs. In our scheme, the query time is almost irrelevant to the number of friends. We also evaluate the performance and validate the correctness of our proposed algorithm through extensive simulations

Image Labeling on a Network: Using Social-Network Metadata for Image Classification

Large-scale image retrieval benchmarks invariably consist of images from the Web. Many of these benchmarks are derived from online photo sharing networks, like Flickr, which in addition to hosting images also provide a highly interactive social community. Such communities generate rich metadata that can naturally be harnessed for image classification and retrieval. Here we study four popular benchmark datasets, extending them with social-network metadata, such as the groups to which each image belongs, the comment thread associated with the image, who uploaded it, their location, and their network of friends. Since these types of data are inherently relational, we propose a model that explicitly accounts for the interdependencies between images sharing common properties. We model the task as a binary labeling problem on a network, and use structured learning techniques to learn model parameters. We find that social-network metadata are useful in a variety of classification tasks, in many cases outperforming methods based on image content.Comment: ECCV 2012; 14 pages, 4 figure

A Privacy Protection Mechanism for Mobile Online Social Networks

A Location sharing system is the most critical component in mobile online social networks (MOSNS).Huge number of user\u27s location information will be stored by the service providers. In addition to the location privacy and social network privacy cannot be guaranteed to the user in the earlier work. Regarding the enhanced privacy against the inside attacker implemented by the service provider in (MOSNS), we initiate a new architecture with multiple servers .It introduces a protected solution which supports a location sharing among friends and strangers. The user friend set in each query is submitted to the location server it divides into multiple subset by the location server. If the user makes a query to the server the data can be retrieved only for the registered users instead of all. We use Three Layer of Security likely, High, Medium and Low for the Privacy implementation. Simultaneously with a location sharing it offers check ability of the searching results reoccurred from the servers. We also prove that the new construction is safe under the stronger security model with enhanced privacy

Modelling perceived risks to personal privacy from location disclosure on online social networks

As users increasingly rely on online social networks for their communication activities, personal location data processing through such networks poses significant risks to users’ privacy. Location tracks can be mined with other shared information to extract rich personal profiles. To protect users’ privacy, online social networks face the challenge of ensuring transparent communication to users of how their data are processed, and explicitly obtaining users’ informed consent for the use of this data. In this paper, we explore the complex nature of the location disclosure problem and its risks to personal privacy. We evaluate, with an experiment involving 715 participants, the contributing factors to the perception of such risks with scenarios that mimic (a) realistic modes of interaction, where users are not fully aware of the extent of their location-related data being processed, and (b) with devised scenarios that deliberately inform users of the data they are sharing and its visibility to others. The results are used to represent the users’ perception of privacy risks when sharing their location information online and to derive a possible model of privacy risks associated with this sharing behaviour. Such a model can inform the design of privacy-aware online social networks to improve users’ trust and to ensure compliance with legal frameworks for personal privacy

PPLS: a privacy-preserving location-sharing scheme in mobile online social networks

The recent proliferation of mobile devices has given rise to mobile online social networks (mOSNs), an emerging network paradigm that uses social networks as its main design element. As one of the most critical components in mOSNs, location sharing plays an important role in helping users share information and strengthen their social bonds, which however may compromise users’ privacy, including location information and social relationship details. To address these challenges, some solutions have been proposed. However, none of them considers the privacy of inter-user threshold distance, which effectively can be used to identify users, their friends, and location information, by malicious or undesired elements of the system. To overcome this limitation, we propose a secure distance comparison protocol. Furthermore, we present a privacy-preserving location-sharing scheme (PPLS), which allows users to build more complex access control policies. The safety of our scheme is validated by the security analysis and the experimental results demonstrate the efficiency of PPLS scheme.This work was supported by National Natural Science Foundation of China (Grant Nos. 61972037, 61402037, U1836212, 61872041) and Graduate Technological Innovation Project of Beijing Institute of Technology (Grant No. 2019CX10014)

An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments

Social recommendation has been popular and successful in various urban sustainable applications such as online sharing, products recommendation and shopping services. These applications allow users to form several implicit social networks through their daily social interactions. The users in such social networks can rate some interesting items and give comments. The majority of the existing studies have investigated the rating prediction and recommendation of items based on user-item bipartite graph and user-user social graph, so called social recommendation. However, the spatial factor was not considered in their recommendation mechanisms. With the rapid development of the service of location-based social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. This paper proposes spatial social union (SSU), an approach of similarity measurement between two users that integrates the interconnection among users, items and locations. The SSU-aware location-sensitive recommendation algorithm is then devised. We evaluate and compare the proposed approach with the existing rating prediction and item recommendation algorithms subject to a real-life data set. Experimental results show that the proposed SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of user's preference and location.This work was supported by the National Natural Science Foundation of China under Grant 61372187. G. Min’s work was partly supported by the EU FP7 CLIMBER project under Grant Agreement No. PIRSES-GA-2012-318939. L. T. Yang is the corresponding author

Tourism communities and social ties: the role of online and offline tourist social networks in building social capital and sustainable practice.

Mobile connectivity enables the adoption of new ways to connect with social networks which are changing how we might, and could, seek support. In the tourism domain we increasingly blend online and offline presence to engage with social networks in the spatial location, at a distance and across time. This paper explores the forms of community that exist in physical tourism contexts, contexts not previously analysed through a community lens, and explores how mobile technology is creating connections within and beyond existing social networks. It examines how sustainable tourism can be enhanced by mobile connectivity through new space-time practices and using ephemeral interpersonal relationships to harness niche groups to create bottom-up social systems interested in sharing experiences, ideas and resources. Special attention is given to the concept of gelling socialities which proposes a less ridged network structure, and to the need to understand the increasingly liquid social dynamics of mobile social interactions. The paper adds to the theories surrounding community, social ties and tourism’s value to society. It draws on data from in-depth interviews undertaken while designing and testing a collaborative travel app. It contributes to growing research into the new technologies increasingly available for sustainable tourism marketing and implementation

Law, Norms, Piracy and Online Anonymity – Practices of de-identification in the global file sharing community

Purpose The purpose of this study is to better understand online anonymity in the global file-sharing community in the context of social norms and copyright law. The study describes the respondents in terms of use of Virtual Private Networks (VPNs) or similar services with respect to age, gender, geographical location, as well as analysing the correlation with file-sharing frequencies. Design/methodology/approach This study, to a large extent, collected descriptive data through a web-based survey. This was carried out in collaboration with the BitTorrent tracker The Pirate Bay (TPB), which allowed us to link the survey from the main logo of their site. In 72 hours, we received over 75,000 responses, providing the opportunity to compare use of anonymity services with factors of age, geographical region, file-sharing frequency, etc. Findings Overall, 17.8 per cent of the respondents used a VPN or similar service (free or paid). A core of high-frequency uploaders is more inclined to use VPNs or similar services than the average file sharer. Online anonymity practices in the file-sharing community depend on how legal and social norms correlate (more enforcement means more anonymity). Research limitations/implications The web-based survey was in English and mainly attracted visitors on The Pirate Bay’s web site. This means that it is likely that those who do not have the language skills necessary were excluded from the survey. Practical implications This study adds to the knowledge of online anonymity practices in terms of traceability and identification, and therefore describes some of the conditions for legal enforcement in a digital environment. Social implications This study adds to the knowledge of how the Internet is changing in terms of a polarization between stronger means of legally enforced identification and a growing awareness of how to be more untraceable. Originality/value The scale of the survey, with over 75,000 respondents from most parts of the world, has likely not been seen before on this topic. The descriptive study of anonymity practices in the global file-sharing community is therefore likely unique

Improving Security and Privacy in Online Social Networks

Online social networks (OSNs) have gained soaring popularity and are among the most popular sites on the Web. With OSNs, users around the world establish and strengthen connections by sharing thoughts, activities, photos, locations, and other personal information. However, the immense popularity of OSNs also raises significant security and privacy concerns. Storing millions of users\u27 private information and their social connections, OSNs are susceptible to becoming the target of various attacks. In addition, user privacy will be compromised if the private data collected by OSNs are abused, inadvertently leaked, or under the control of adversaries. as a result, the tension between the value of joining OSNs and the security and privacy risks is rising.;To make OSNs more secure and privacy-preserving, our work follow a bottom-up approach. OSNs are composed of three components, the infrastructure layer, the function layer, and the user data stored on OSNs. For each component of OSNs, in this dissertation, we analyze and address a representative security/privacy issue. Starting from the infrastructure layer of OSNs, we first consider how to improve the reliability of OSN infrastructures, and we propose Fast Mencius, a crash-fault tolerant state machine replication protocol that has low latency and high throughput in wide-area networks. For the function layer of OSNs, we investigate how to prevent the functioning of OSNs from being disturbed by adversaries, and we propose SybilDefender, a centralized sybil defense scheme that can effectively detect sybil nodes by analyzing social network topologies. Finally, we study how to protect user privacy on OSNs, and we propose two schemes. MobiShare is a privacy-preserving location-sharing scheme designed for location-based OSNs (LBSNs), which supports sharing locations between both friends and strangers. LBSNSim is a trace-driven LBSN model that can generate synthetic LBSN datasets used in place of real datasets. Combining our work contributes to improving security and privacy in OSNs

“Sharing” the Mykonian summer: The case of AirBnB

The aim of this paper is to illustrate an overview of the current AirBnB market in the Greek island of Mykonos and its place within the entire hospitality real estate market of the island. Global economy has changed its form and functions several times during the years. Sharing economy or collaborative consumption is a new way of economic activity, which basically refers to goods and services sharing. The change from owning to sharing, the wide use of online social networks and electronic markets and the expansion of mobile devices and electronic services have all contributed to the augmentation of sharing economy. The impact of sharing economy on real estate began on 2008, when AirBnB was founded in San Francisco. Nowadays, sharing economy in real estate is very popular not only in USA but internationally too and AirBnB has become one of the most famous short-term lease platforms. Mykonos which is one of the most touristic and popular Greek islands enjoys its own economy. Despite the economic crisis, Mykonos has not been affected in terms of real estate values, hotel room values and holiday expenses. In such an economy, AirBnB has found its place all over the island. This paper aims at mapping the AirBnB phenomenon in Mykonos through the use of GIS, proving that sharing economy is not just targeting markets of lower income or budget and highlighting any geographical patterns in the location of AirBnB properties. Moreover, an analysis on the factors that affect the rental value/day provides an additional insight. It is clearly proven that AirBnB covers any additional need for tourist accommodation successfully even in the high demanding market of Mykonos with over 300 sharing residential facilities, which are described as a shared space, a private room or even an entire house. The paper provides interesting results on the location of each sharing property, the site and property attributes, amenities and services and rental rules, which are all investigated in order to present the Mykonian AirBnB