Understanding structure-based social network de-anonymization techniques via empirical analysis

Mao, Jian; Tian, Wenqian; Jiang, Jingbo; He, Zhaoyuan; Zhou, Zhihong; Liu, Jianwei

doi:10.1186/s13638-018-1291-2

Cited by 12 publications

(7 citation statements)

References 33 publications

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“…Ghazinour et al 50 have shown that users' identities can be exposed easily by tracking session cookies. By proposing a new technique of de-anonymization attack, Mao et al 51 demonstrate that by analyzing the group membership data, an attacker can identify a user easily in the OSN.…”

Section: Traditional Attacksmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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Section: Traditional Attacksmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

View full text Add to dashboard Cite

show abstract

“…When storing sensitive data like personal health records to cloud servers, the security and privacy of these data are still challenges in the fog computing paradigm [10][11][12]. To solve this problem, applying access control mechanism is an essential method to protect the sensitive data from unauthorized users.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Fine-Grained Searchable Encryption Scheme in Fog-Based Healthcare IoT Networks

Jing

2019

Wireless Communications and Mobile Computing

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For a smart healthcare system, a cloud based paradigm with numerous user terminals is to support and improve more reliable, convenient, and intelligent services. Considering the resource limitation of terminals and communication overhead in cloud paradigm, we propose a hybrid IoT-Fog-Cloud framework. In this framework, we deploy a geo-distributed fog layer at the edge of networks. The fogs can provide the local storage, sufficient processing power, and appropriate network functions. For the fog-based healthcare system, data confidentiality, access control, and secure searching over ciphertext are the key issues in sensitive data. Furthermore, how to adjust the storage and computing requirements to meet the limited resource is also a great challenge for data management. To address these, we design a lightweight keyword searchable encryption scheme with fine-grained access control for our proposed healthcare related IoT-Fog-Cloud framework. Through our design, the users can achieve a fast and efficient service by delegating a majority part of the workloads and storage requirements to fogs and the cloud without extra privacy leakage. We prove our scheme satisfies the security requirements and demonstrate the excellent efficiency through experimental evaluation.

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“…Moreover, inspired by graph matching and network alignment, structure prediction based de-anonymization methods are defined to match the accounts across networks for user identity identification [23]- [26]. More information about structure prediction based graph de-anonymization methods can be found in the survey paper [27]. Therefore, in the necessity of privacy protection and preventing prediction-based inference attacks, the study of structural predictability optimization, i.e., regulating the inherent difficulty of structure prediction in networks independent of specific algorithms, is urgently need.…”

Section: Introductionmentioning

confidence: 99%

Structural Predictability Optimization Against Inference Attacks in Data Publishing

Ming

Xian

et al. 2019

IEEE Access

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Graphs have been proved to be a useful mathematical representation for a broad variety of real-world complex systems, and the structure prediction on graphs refers to estimating the potential relationship between the objects from the observed structures, being fundamental in many data analysis applications, such as network alignment, network reconstruction, and link prediction. Accordingly, in data publishing, it is necessary to regulate the structural predictability of graphs against inference attack to protect the sensitive information of the data generators. In contrast to the existing works about graph structure perturbation for node ranking, information diffusion, and so on, the structural predictability optimization problem, i.e., reducing the accuracy of sensitive relationships inference in graphs, has not been extensively studied. This paper presents an active learning algorithm that selects the most representative links to be perturbed, thus regulating the structural predictability of graphs, that is, removing as few as possible links to undermine the regularity level of graphs, which forms the foundation of inference attack methods. Specifically, with the assumption that the substructure with higher regularity level contains more regular equivalence components and has more equivalent paths supplied for the random walk processes, random walk-based link importance measuring algorithm is proposed to identify the representative links. The structural regularity metric, measuring the structural predictability of graphs, is also introduced to guide the link perturbation for structural predictability optimization. The extensive experiments on artificial and real-world data sets demonstrate the effectiveness of the proposed structural predictability optimization method. Specifically, the method can learn the role of links accurately in term of graph organization, and the performance of structure inference on graphs can be deteriorated effectively by representative link-based perturbation.

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Understanding structure-based social network de-anonymization techniques via empirical analysis

Cited by 12 publications

References 33 publications

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

A Lightweight Fine-Grained Searchable Encryption Scheme in Fog-Based Healthcare IoT Networks

Structural Predictability Optimization Against Inference Attacks in Data Publishing

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