scholarly journals A User-Defined Location-Sharing Scheme with Efficiency and Privacy in Mobile Social Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Tao Peng ◽  
Jierong Liu ◽  
Guojun Wang ◽  
Qin Liu ◽  
Jianer Chen ◽  
...  
Keyword(s):  
Social Networks ◽  
Privacy Preservation ◽  
Location Privacy ◽  
Coarse Grained ◽  
Smart Devices ◽  
Mobile Users ◽  
Mobile Social Networks ◽  
Location Sharing ◽  
Sharing Scheme ◽  
The Individual

The popularity of the modern smart devices and mobile social networks (MSNs) brings mobile users better experiences and services by taking advantage of location-aware capabilities. Location sharing, as an important function of MSNs, has attracted attention with growing popularity. While the users get great benefits and conveniences from MSNs, they also have high concerns about the privacy of location. However, in the existing solution, the privacy of users can hardly be guaranteed without the assumption of full trust in the service provider (SP), and few previous research studies have discussed the individual requirement of mobile users in MSNs. In this paper, we propose a user-defined location-sharing scheme (ULSS) to achieve enhanced privacy preservation under different contexts. We present a coarse-grained proximity detection method and a lightweight order-preserving encryption- (OPE-) based method to provide the users with flexible privacy preservation at different privacy levels. The proposed scheme preserves user’s location privacy with respect to SP, friends, and other adversaries, getting rid of the introduction of fully trusted party (TTP). Extensive experiments were conducted to verify the effectiveness and efficiency of our proposed scheme.

scholarly journals Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3994
Author(s):  
Yuxi Li ◽  
Fucai Zhou ◽  
Yue Ge ◽  
Zifeng Xu
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Location Privacy ◽  
Nearest Neighbors ◽  
Search Pattern ◽  
Broadcast Encryption ◽  
Mobile Social Networks ◽  
K Nearest Neighbors ◽  
Fine Grained ◽  
Server Architecture

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.

Morality-Driven Data Forwarding With Privacy Preservation in Mobile Social Networks

2012 ◽  
Vol 61 (7) ◽  
pp. 3209-3222 ◽  
Author(s):  
Xiaohui Liang ◽  
Xu Li ◽  
Tom H. Luan ◽  
Rongxing Lu ◽  
Xiaodong Lin ◽  
...  
Keyword(s):  
Social Networks ◽  
Privacy Preservation ◽  
Mobile Social Networks ◽  
Data Forwarding

scholarly journals Private Profile Matching for Mobile Social Networks Based on Fuzzy Extractors

10.29007/st23 ◽  
2018 ◽  
Author(s):  
Jaweher Zouari ◽  
Mohamed Hamdi ◽  
Tai-Hoon Kim
Keyword(s):  
Social Networks ◽  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Mobile Social Networks ◽  
Fuzzy Extractor ◽  
Mobile Social Networking ◽  
Profile Matching ◽  
Novel Approach ◽  
Privacy Breaches ◽  
Fuzzy Extractors

Interacting with geographically proximate users who present similar interests and preferences is a key service offered by mobile social networks which leads to the creation of new connections that combine physical and social closeness. Usually these interactions are based on social profile matching where users publish their preferences and attributes to enable the search for a similar profile. Such public search would result in the leakage of sensitive or identifiable information to strangers who are not always potential friends. As a consequence this promising feature of mobile social networking may cause serious privacy breaches if not addressed properly. Most existent work relies on homomorphic encryption for privacy preservation during profile matching, while we propose in this paper a novel approach based on the fuzzy extractor which performs private matching of two sets and reveals them only if they overlap considerably. Our scheme achieves a desirable trade off between security and complexity.

scholarly journals Privacy preservation in mobile social networks

2019 ◽  
Author(s):  
◽  
Douglas Steiert
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Privacy Preservation ◽  
Location Based Services ◽  
Mobile Social Networks ◽  
Privacy Concerns ◽  
Average Person ◽  
Effectiveness And Efficiency ◽  
Privacy Breaches

In this day and age with the prevalence of smartphones, networking has evolved in an intricate and complex way. With the help of a technology-driven society, the term "social networking" was created and came to mean using media platforms such as Myspace, Facebook, and Twitter to connect and interact with friends, family, or even complete strangers. Websites are created and put online each day, with many of them possessing hidden threats that the average person does not think about. A key feature that was created for vast amount of utility was the use of location-based services, where many websites inform their users that the website will be using the users' locations to enhance the functionality. However, still far too many websites do not inform their users that they may be tracked, or to what degree. In a similar juxtaposed scenario, the evolution of these social networks has allowed countless people to share photos with others online. While this seems harmless at face-value, there may be times in which people share photos of friends or other non-consenting individuals who do not want that picture viewable to anyone at the photo owner's control. There exists a lack of privacy controls for users to precisely de fine how they wish websites to use their location information, and for how others may share images of them online. This dissertation introduces two models that help mitigate these privacy concerns for social network users. MoveWithMe is an Android and iOS application which creates decoys that move locations along with the user in a consistent and semantically secure way. REMIND is the second model that performs rich probability calculations to determine which friends in a social network may pose a risk for privacy breaches when sharing images. Both models have undergone extensive testing to demonstrate their effectiveness and efficiency.

scholarly journals Prophet: A Context-Aware Location Privacy-Preserving Scheme in Location Sharing Service

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jiaxing Qu ◽  
Guoyin Zhang ◽  
Zhou Fang
Keyword(s):  
Location Privacy ◽  
Behavior Patterns ◽  
Location Information ◽  
Context Aware ◽  
Mobile Social Networks ◽  
New Class ◽  
Privacy Threats ◽  
Temporal Correlations ◽  
Location Sharing ◽  
State Classification

Location sharing service has become an indispensable part in mobile social networks. However, location sharing may introduce a new class of privacy threats ranging from localizing an individual to profiling and identifying him based on the places he shared. Although users may avoid releasing geocontent in sensitive locations, it does not necessarily prevent the adversary from inferring users’ privacy through space-temporal correlations and historical information. In this paper, we design a Prophet framework, which provides an effective security scheme for users sharing their location information. First, we define fingerprint identification based on Markov chain and state classification to describe the users’ behavior patterns. Then, we propose a novel location anonymization mechanism, which adopts a ε-indistinguishability strategy to protect user’s sensitive location information published. Finally, experimental results are given to illustrate good performance and effectiveness of the proposed scheme.

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