scholarly journals Location-sharing protocol for privacy protection in mobile online social networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ou Ruan ◽  
Lixiao Zhang ◽  
Yuanyuan Zhang
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Smart Cities ◽  
Location Based Services ◽  
Broadcast Encryption ◽  
Location Sharing ◽  
Identity Privacy ◽  
And Performance ◽  
Network Server

AbstractLocation-based services are becoming more and more popular in mobile online social networks (mOSNs) for smart cities, but users’ privacy also has aroused widespread concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location-sharing protocol, which solves two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users’ identity privacy, and prevent location server from inferring users’ activity tracks by updating dummy identities in time. The details of security and performance analysis with related protocols show that our protocol enjoys two advantages: (1) it’s more efficient than related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals; however, most previous protocols only meet some security goals.

scholarly journals Location-sharing Protocol for Privacy Protection in Mobile Online Social Networks

2021 ◽  
Author(s):  
Ou Ruan ◽  
Lixiao Zhang ◽  
Yuanyuan Zhang
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Smart Cities ◽  
Location Based Services ◽  
Broadcast Encryption ◽  
Location Sharing ◽  
Identity Privacy ◽  
And Performance ◽  
Network Server

Abstract Location-based services are becoming more and more popular in mobile online social networks(mOSNs) for smart cities, but users' privacy also has aroused wide concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location sharing protocol, which solves these two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users' identity privacy, and prevent locationserver from inferring users' activity tracks by updating dummy identities in time. The details of security and performance analysis with the related protocols show that our protocol enjoys two advantages: (1) it's more efficient than the related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals, however, most previous protocols only meet some security goals.

scholarly journals Location-Sharing Protocol for Privacy Protection in Mobile Online Social Networks

2020 ◽  
Author(s):  
Ou Ruan ◽  
Lixiao Zhang ◽  
Yuanyuan Zhang
Keyword(s):  
Private Information ◽  
Online Social Networks ◽  
Smart Cities ◽  
Location Based Services ◽  
Broadcast Encryption ◽  
Security Risks ◽  
Location Sharing ◽  
Identity Privacy ◽  
And Performance ◽  
Network Server

Abstract Location-based services are becoming more and more popular in mobile online so-cial networks(mOSNs) for smart cities, but users' privacy also has aroused wide concern, such as locations, friend sets and other private information. At present, many location sharing protocols in mOSNs have been proposed, but these pro-tocols are inecient and ignore some security risks. In this paper, we propose a new location sharing protocol, which solves these two issues by using symmetric encryption and asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one loca-tion server; connecting social network server and location server directly instead of through celluar towers; avoiding broadcast encryption. We introduce dummy iden-tities to protect users' identity privacy, and prevent location server from inferring users' activity tracks by updating dummy identities in time. The details of security and performance analysis with the related protocols show that our protocol enjoys two advantages: (1) it's more ecient than the related protocols, which greatly re-duces the computation and communication costs; (2) it satises all security goals, however, most previous protocols only meet some security goals.

scholarly journals A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks

2020 ◽  
Vol 10 (23) ◽  
pp. 8402
Author(s):  
Guangcan Yang ◽  
Shoushan Luo ◽  
Yang Xin ◽  
Hongliang Zhu ◽  
Jingkai Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Security Analysis ◽  
Control Policy ◽  
Privacy Preserving ◽  
Handheld Devices ◽  
Location Sharing ◽  
Trade Offs ◽  
And Performance

With the advent of intelligent handheld devices, location sharing becomes one of the most popular services in mobile online social networks (mOSNs). In location-sharing services, users can enjoy a better social experience by updating their real-time location information. However, the leakage of private information may hinder the further development of location-sharing services. Although many solutions have been proposed to protect users’ privacy, the privacy-utility trade-offs must be considered. Therefore, we propose a new scheme called search efficient privacy-preserving location-sharing (SELS) system. In our scheme, we create a new approach named associated grids to improve the efficiency of location-sharing systems while maintaining users’ privacy. In addition, by setting the user-defined access control policy proposed in our scheme, users’ flexible privacy-preserving requirements can be satisfied. Detailed complexity and security analysis show that the proposed scheme is a practical and efficient privacy-preserving solution. Extensive simulations are performed to validate the effectiveness and performance of our scheme.

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

2021 ◽  
pp. 1-12
Author(s):  
Gokay Saldamli ◽  
Richard Chow ◽  
Hongxia Jin
Keyword(s):  
Service Providers ◽  
Smart Cities ◽  
User Interaction ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
Fine Grained ◽  
Location Sharing ◽  
Social Network Structure

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

Managing the virtual boundaries: Online social networks, disclosure, and privacy behaviors

2016 ◽  
Vol 20 (1) ◽  
pp. 50-67 ◽  
Author(s):  
Mary Helen Millham ◽  
David Atkin
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Privacy Management ◽  
Communication Privacy Management ◽  
Communication Privacy Management Theory ◽  
Online Setting ◽  
Communication Privacy ◽  
Privacy Behaviors

Online social networks are designed to encourage disclosure while also having the ability to disrupt existing privacy boundaries. This study assesses those individuals who are the most active online: “Digital Natives.” The specific focus includes participants’ privacy beliefs; how valuable they believe their personal, private information to be; and what risks they perceive in terms of disclosing this information in a fairly anonymous online setting. A model incorporating these concepts was tested in the context of communication privacy management theory. Study findings suggest that attitudinal measures were stronger predictors of privacy behaviors than were social locators. In particular, support was found for a model positing that if an individual placed a higher premium on their personal, private information, they would then be less inclined to disclose such information while visiting online social networking sites.

scholarly journals Responsible Research on Social Networks

2018 ◽  
pp. 558-576
Author(s):  
Jon Crowcroft ◽  
Hamed Haddadi ◽  
Tristan Henderson
Keyword(s):  
Social Networks ◽  
Interpersonal Communication ◽  
Private Information ◽  
Online Social Networks ◽  
Personal Data ◽  
Legal Requirements ◽  
Pros And Cons ◽  
Conducting Research ◽  
Responsible Research ◽  
Responsible Manner

Researchers have found online social networks a goldmine for research into various aspects of social behavior and interpersonal communication. For example, observing social interaction between individuals and their engagement in conversations, or performing sentiment analysis on these communications, is often carried out for research in a number of disciplines such as health, sociology, or politics. Such studies introduce many challenges for conducting research in a responsible manner. Data may be repurposed or cross-correlated in ways that participants may not have anticipated or desired, private information may be collected, or legal requirements may not be met. This chapter explores some of the challenges and dilemmas faced by industry, academia, regulators, privacy advocates, and ultimately the individuals using these services. It discusses the pros and cons of the collection, analysis, and archiving of personal data for digital research. The chapter concludes by discussing theoretical and practical approaches that target these dilemmas.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2019 ◽  
pp. 188-212
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

scholarly journals A Trustworthy SIoT Aware Mechanism as an Enabler for Citizen Services in Smart Cities

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 918 ◽  
Author(s):  
Ateeq Ur Rehman ◽  
Rizwan Ali Naqvi ◽  
Abdul Rehman ◽  
Anand Paul ◽  
Muhammad Tariq Sadiq ◽  
...  
Keyword(s):  
Social Networks ◽  
Information And Communication Technologies ◽  
Online Social Networks ◽  
Information Technologies ◽  
Smart Cities ◽  
Trust Model ◽  
Human Interaction ◽  
Opinion Leaders ◽  
Local Cluster ◽  
Information And Communication

In the recent era, new information technologies have a significant impact on social networks. Initial integration of information and communication technologies (ICT) into city operations has promoted information city, ease of communication and principles of smart communities. Subsequently, the idea of the Internet of Things (IoT) with the specific focus of social IoT (SIoT) has contributed towards the smart cities (SC), which support the city operations with minimal human interaction. The user-generated data obtained by SIoT can be exploited to produce new useful information for creating citizen-centered smart services for SC. The aim of this research is twofold. Firstly, we used the concept of local and global trust to provide new services in SC based on popular online social networks (OSN) data used by the citizens. Secondly, the sustainability of the three different OSN is assessed. This paper investigates the social network domain with regard to the SC. Although in SC, OSN are increasing day by day, there is still an unresolved issue of trust among their users and also OSN are not much sustainable. In this research, we are analyzing the sustainability of different OSN for the SC. We employ datasets of three different social networks for our analyses. A local trust model is used to identify the central user within the local cluster while the global trust-based framework is used to identify the opinion leaders. Our analysis based on the datasets of Facebook, Twitter, and Slashdot unveil that filtration of these central-local users and opinion leaders result in the dispersion and significant reduction in a network. A novel model is being developed that outlines the relationship between local and global trust for the protection of OSN users in SC. Furthermore, the proposed mechanism uses the data posted by citizens on OSN to propose new services by mitigating the effect of untrusted users.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2021 ◽  
pp. 2110-2134
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

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