RcDT: Privacy Preservation Based on R-constrained Dummy Trajectory in Mobile Social Networks

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.

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Privacy preservation in mobile social networks

10.32469/10355/69982 ◽

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.

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PIF: A Personalized Fine-Grained Spam Filtering Scheme With Privacy Preservation in Mobile Social Networks

IEEE Transactions on Computational Social Systems ◽

10.1109/tcss.2016.2519819 ◽

2015 ◽

Vol 2 (3) ◽

pp. 41-52 ◽

Cited By ~ 20

Author(s):

Kuan Zhang ◽

Xiaohui Liang ◽

Rongxing Lu ◽

Xuemin Shen

Keyword(s):

Social Networks ◽

Privacy Preservation ◽

Spam Filtering ◽

Mobile Social Networks ◽

Fine Grained

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Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation

Journal of ISMAC - June 2019 ◽

10.36548/jismac.2021.3.006 ◽

2021 ◽

Vol 3 (3) ◽

pp. 250-262

Author(s):

Jennifer S. Raj

Keyword(s):

Social Networks ◽

Access Control ◽

Privacy Preservation ◽

Keyword Search ◽

Power Saving ◽

Privacy Preserving ◽

Mobile Social Networks ◽

User Privacy ◽

Attribute Based Encryption ◽

Ciphertext Policy

Several subscribing and content sharing services are largely personalized with the growing use of mobile social media technology. The end user privacy in terms of social relationships, interests and identities as well as shared content confidentiality are some of the privacy concerns in such services. The content is provided with fine-grained access control with the help of attribute-based encryption (ABE) in existing work. Decryption of privacy preserving content suffers high consumption of energy and data leakage to unauthorized people is faced when mobile social networks share privacy preserving data. In the mobile social networks, a secure proxy decryption model with enhanced publishing and subscribing scheme is presented in this paper as a solution to the aforementioned issues. The user credentials and data confidentiality are protected by access control techniques that work on privacy preserving in a self-contained manner. Keyword search based public-key encryption with ciphertext policy attribute-based encryption is used in this model. At the end users, ciphertext decryption is performed to reduce the energy consumption by the secure proxy decryption scheme. The effectiveness and efficiency of the privacy preservation model is observed from the experimental results.

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

Scientific Programming ◽

10.1155/2020/7832875 ◽

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.

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An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks

Applied Sciences ◽

10.3390/app9020316 ◽

2019 ◽

Vol 9 (2) ◽

pp. 316 ◽

Cited By ~ 3

Author(s):

Guangcan Yang ◽

Shoushan Luo ◽

Hongliang Zhu ◽

Yang Xin ◽

Mingzhen Li ◽

...

Keyword(s):

Social Networks ◽

Privacy Preservation ◽

Rapid Development ◽

Experimental Studies ◽

Global Perspective ◽

Handheld Devices ◽

Mobile Social Networks ◽

Daily Lives ◽

Service Category ◽

The Relationship

With the rapid development of smart handheld devices, wireless communication, and positioning technologies, location-based service (LBS) has been gaining tremendous popularity in mobile social networks (MSN). Users’ daily lives are facilitated by the applications of LBS, but users’ privacy leaking hinders the further development of LBS. In order to solve this problem, techniques such as k-anonymity and l-diversity have been widely adopted. However, most papers that combine with k-anonymity and l-diversity focus on the security of users’ privacy with little consideration of service efficiency. In this paper, we firstly treat the relationship between k-anonymity and l-diversity in the clustering process from a dynamic and global perspective. Then a service category table based algorithm (SCTB) is designed to identify and calculate l-diversity securely and efficiently, which promotes the cooperative efficiency of users in LBS query, especially when the preference privacy that users request in the clustering process have similarities. Finally, theoretical performance analysis and extensive experimental studies are performed to validate the effectiveness of our SCTB algorithm.

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