Alpha-anonymization techniques for privacy preservation in social networks

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Saptarshi Chakraborty ◽  
B. K. Tripathy
Keyword(s):  
Social Networks ◽  
Privacy Preservation

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

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

Privacy in Online Social Networks

2021 ◽  
pp. 2078-2109
Author(s):  
Ramanpreet Kaur ◽  
Tomaž Klobučar ◽  
Dušan Gabrijelčič
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Mitigation Strategies ◽  
Research Issues ◽  
Privacy Requirements ◽  
Privacy Threats

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.

Towards privacy preservation for “check-in” services in location-based social networks

2019 ◽  
Vol 481 ◽  
pp. 616-634 ◽  
Author(s):  
Gang Sun ◽  
Liangjun Song ◽  
Dan Liao ◽  
Hongfang Yu ◽  
Victor Chang
Keyword(s):  
Social Networks ◽  
Privacy Preservation ◽  
Location Based Social Networks

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.

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