Social Network De-anonymization

—Social network data is widely shared, transferred and published for research purposes and business interests, but it has raised much concern on users' privacy. Even though users' identity information is always removed, attackers can still de-anonymize users with the help of auxiliary information. To protect against de-anonymization attack, various privacy protection techniques for social networks have been proposed. However, most existing approaches assume specific and restrict network structure as background knowledge and ignore semantic level prior belief of attackers, which are not always realistic in practice and do not apply to arbitrary privacy scenarios. Moreover, the privacy inference attack in the presence of semantic background knowledge is barely investigated. To address these shortcomings, in this work, we introduce knowledge graphs to explicitly express arbitrary prior belief of the attacker for any individual user. The processes of de-anonymization and privacy inference are accordingly formulated based on knowledge graphs. Our experiment on data of real social networks shows that knowledge graphs can power de-anonymization and inference attacks, and thus increase the risk of privacy disclosure. This suggests the validity of knowledge graphs as a general effective model of attackers' background knowledge for social network attack and privacy preservation. Index Terms—Social network data publishing, attack and privacy preservation, knowledge graph.

Encyclopedia of Social Network Analysis and Mining, 2014

Adversarial model Formal description of the unique characteristics of a particular adversary Adversary Somebody who attempts to reveal sensitive, private information Attribute disclosure A privacy breach wherein some descriptive attribute of somebody is revealed Identity disclosure A privacy breach in which a presumably anonymous person is in fact identifiable Target The particular social network member against whom an adversary is trying to breach privacy

Procedia Computer Science, 2018

Anonymization techniques are widely adopted to protect users' privacy during social data publishing and sharing. In this paper, we conduct a comprehensive analysis on the typical structure-based social network de-anonymization algorithms. We aim to understand the de-anonymization approaches and disclose the impacts on their application performance caused by different factors. We design the analysis framework and define three experiment environments to evaluate a few factors' impacts on the target algorithms. Based on our analysis architecture, we simulate two typical de-anonymization algorithms and evaluate their performance under different pre-configured environments.

2012

The problem of de-anonymizing social networks is to identify the same users between two anonymized social networks [7] (Figure 1). Network de-anonymization task is of multifold significance, with user profile enrichment as one of its most promising applications. After the deanonymization and alignment, we can aggregate and enrich user profile information from different online networking services and make the bundled profiles available for end-users as well as thirdparty applications. In our project, we aim to develop effective algorithms for de-anonymizing real-world social networks. Specifically, we focus on two tasks: one is to align the networks of Flickr1 and Instagram2 and the other is to align Flickr and Twitter 3. Our work is motivated by the two parts of information that network data is composed of: network structure and node attributes. Preliminary tests have shown that de-anonymizing algorithm based merely on node attributes, e.g. user names, is computationally efficient b...

2020

Privacy is a concern of social network users. Social networks are a source of valuable data for scientific or commercial analysis. Therefore, anonymizing social network data before releasing it becomes an important issue. The nodes in the network represent the individuals and the links among them denote their relationships. Nevertheless, publishing a social graph directly by simply removing the names of people who contributed to this graph raises important privacy issues. In particular, some inference attacks on the published graph can lead to deanonymizing certain nodes, learning the existence of a social relation between two nodes or even using the structure of the graph itself to deduce the value of certain sensitive attributes. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the challenges in privacy preserving publishing of social network data comparing to the ...

EURASIP Journal on Wireless Communications and Networking, 2018

The rapid development of wellness smart devices and apps, such as Fitbit Coach and FitnessGenes, has triggered a wave of interaction on social networks. People communicate with and follow each other based on their wellness activities. Though such IoT devices and data provide a good motivation, they also expose users to threats due to the privacy leakage of social networks. Anonymization techniques are widely adopted to protect users' privacy during social data publishing and sharing. However, de-anonymization techniques are actively studied to identify weaknesses in current social network data-publishing mechanisms. In this paper, we conduct a comprehensive analysis on the typical structure-based social network de-anonymization algorithms. We aim to understand the de-anonymization approaches and disclose the impacts on their application performance caused by different factors, e.g., topology properties and anonymization methods adopted to sanitize original data. We design the analysis framework and define three experiment environments to evaluate a few factors' impacts on the target algorithms. Based on our analysis architecture, we simulate three typical de-anonymization algorithms and evaluate their performance under different pre-configured environments.

IEEE Transactions on Computational Social Systems, 2019

The tradeoff between anonymity and utility in the context of the anonymization of graph data sets is well acknowledged; for better privacy, some of the graph structural properties must be lost. What is not well understood, however, is what forces shape this tradeoff. Specifically, for the data practitioner who wants to publish an anonymized graph data set, it is unclear what graph structural properties can be preserved and what are the anonymity costs associated with preserving them. This article proposes a framework that examines the interplay between graph properties and the vulnerability to deanonymization attacks. We demonstrate its applicability via extensive experiments on thousands of graphs with controlled properties generated from real data sets. In addition, we show empirically that there are structural properties that affect graph vulnerability to reidentification attacks independent of degree distribution.

2008

This paper studies anonymity in a setting where individuals who communicate with each other over an anonymous channel are also members of a social network. In this setting the social network graph is known to the attacker. We propose a Bayesian method to combine multiple available sources of information and obtain an overall measure of anonymity. We study the effects of network size and find that in this case anonymity degrades when the network grows. We also consider adversaries with incomplete or erroneous information; characterize their knowledge of the social network by its quantity, quality and depth; and discuss the implications of these properties for anonymity.

Data Science and Engineering, 2019

Ever since the social networks became the focus of a great number of researches, the privacy risks of published network data have also raised considerable concerns. To evaluate users' privacy risks, researchers have developed methods to deanonymize the networks and identify the same person in the different networks. However, the existing solutions either require high-quality seed mappings for cold start, or exhibit low accuracy without fully exploiting the structural information, and entail high computation expense. In this paper, we propose a fast and effective seedless network de-anonymization approach simply relying on structural information, named RoleMatch. RoleMatch equips with a new pairwise node similarity measure and an efficient node matching algorithm. Through testing RoleMatch with both real and synthesized social networks, which are anonymized by several popular anonymization algorithms, we demonstrate that the RoleMatch receives superior performance compared with existing de-anonymization algorithms.

2008

Abstract We identify privacy risks associated with releasing network data sets and provide an algorithm that mitigates those risks. A network consists of entities connected by links representing relations such as friendship, communication, or shared activity. Maintaining privacy when publishing networked data is uniquely challenging because an individual's network context can be used to identify them even if other identifying information is removed.