Social Network Privacy for Attribute Disclosure Attacks

—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 strengthen 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 privacy preservation. Index Terms—Social network data publishing, attack and privacy preservation, knowledge graph.

Advances in technology have made it possible to collect data about individuals and the connections between them, such as email correspondence and friendships. Agencies and researchers who have collected such social network data often have a compelling interest in allowing others to analyze the data. However, in many cases the data describes relationships that are private (e.g., email correspondence) and sharing the data in full can result in unacceptable disclosures. In this paper, we present a framework for assessing the privacy risk of sharing anonymized network data. This includes a model of adversary knowledge, for which we consider several variants and make connections to known graph theoretical results. On several real-world social networks, we show that simple anonymization techniques are inadequate, resulting in substantial breaches of privacy for even modestly informed adversaries. We propose a novel anonymization technique based on perturbing the network and demonstrate empirically that it leads to substantial reduction of the privacy threat. We also analyze the effect that anonymizing the network has on the utility of the data for social network analysis.

With more and more people joining different online social networking (OSN) services every day, the archives of the OSN service providers are increasing drastically. This great amount of personal information is then shared by the service providers with different third parties, which raises a serious concern in preserving privacy of the individuals. For the last few years many work have been done to innovate new techniques, called anonyization techniques, to protect privacy in social network data publishing. In this paper we briefly discuss and categorize vertex and label anonymization techniques which prevent disclosure of individual identities and sensitive information about those identities. We also categorize attributes, attacks and privacy breaches in online social networks.

Privacy is considered as one of the major issues while publishing or sharing social network data for social science research. In the recent period, researchers have developed secure privacy models similar to kanonymity to prevent node re-identification through structural information. However, after the development of this models even then privacy models are enforced, an attacker or hacker may still be able assumption one's private information if they share the same sensitive label's in a group of nodes largely (i.e., attributes). In other words, the structural anaonymaization methods are not well protecting the label-node relationship. Furthermore, existing approaches, which rely on edge editing or node clustering, may significantly alter key graph properties. In this paper, we define a k-degree-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals. We further propose a novel anonymization methodology based on adding noise nodes. We develop a new algorithm by adding noise nodes into the original graph with the consideration of introducing the least distortion to graph properties. Most importantly, we provide a rigorous analysis of the theoretical bounds on the number of noise nodes added and their impacts on an important graph property. We conduct extensive experiments to evaluate the effectiveness of the proposed technique.

2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC), 2014

With the popularity of cloud computing, many companies would outsource their social network data to a cloud service provider, where privacy leaks have become a more and more serious problem. However, most of the previous studies have ignored an important fact, i.e., in real social networks, users possess various attributes and have the flexibility to decide which attributes of their profiles are sensitive attributes by themselves. These sensitive attributes of the users should be protected from being revealed when outsourcing a social network to a cloud service provider. In this paper, we consider the problem of resisting privacy attacks with neighborhood information of both network structure and labels of one-hop neighbors as background knowledge. To tackle this problem, we propose a Global Similarity-based Group Anonymization (GSGA) method to generate a anonymized social network while maintaining as much utility as possible. We also extensively evaluate our approach on both real data set and synthetic data sets. Evaluation results show that the social network anonymized by our approach can still be used to answer aggregation queries with high accuracy.

International Journal of Data Science, 2019

In order to improve their business solutions the data holders often release the social network data and its structure to the third party. This data undergo node and attribute anonymisation before its release. This however does not prevent the users from inference attacks which an un-trusted third party or an adversary would carry out at their end by analysing the structure of the graph. Therefore, there is an utmost necessity to not only anonymise the nodes and their attributes but also to anonymise the edge sets in the released social network graph. Anonymising involves perturbing the actual data which results in utility loss. Ensuring utility and preserving privacy are inversely proportional to each other and is a challenging task. In this work we have proposed, implemented and verified an efficient utility based privacy preserving solution to prevent the third party inference attacks for an online social network graph.

Procedia Computer Science, 2016

Online Social Networks (OSNs) have become a mainstream cultural phenomenon for millions of Internet users. More importantly, OSNs expose now information from multiple social spheres e.g. personal information or professional activity. We identify two stakeholders in online social networks: the OSN users and the OSN itself. On one hand, OSN users share an astonishing amount of information ranging from personal to professional. On the other hand, OSN services handle users' information and manage all users' activities in the network, being responsible for the correct functioning of its services and maintaining a profitable business model. Indirectly, this translates into ensuring that their users continue to happily use their services without becoming victims of malicious actions. We thus classify online social networks privacy and security issues into two categories of attacks on users and OSN. In this paper we propose a utility based association rule hiding algorithm for privacy preserving user profiles data against attacks from OSN users or even OSN applications. Experimental has been conducted on samples of real datasets. Experimental has been showed less attribute modification in the released user's profiles datasets.

Lecture Notes in Computer Science, 2009

The advent of social network sites in the last years seems to be a trend that will likely continue. What naive technology users may not realize is that the information they provide online is stored and may be used for various purposes. Researchers have pointed out for some time the privacy implications of massive data gathering, and effort has been made to protect the data from unauthorized disclosure. However, the data privacy research has mostly targeted traditional data models such as microdata. Recently, social network data has begun to be analyzed from a specific privacy perspective, one that considers, besides the attribute values that characterize the individual entities in the networks, their relationships with other entities. Our main contributions in this paper are a greedy algorithm for anonymizing a social network and a measure that quantifies the information loss in the anonymization process due to edge generalization.

2012

Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social net-work unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as we...

"With an abundance of social network data being released, the need to protect sensitive information within these networks has become an impor- tant concern of data publishers. To achieve this objective, various notions of k-anonymization have been proposed for social network graphs. In this paper we focus on the complexity of optimization problems that arise from trying to anonymize graphs, establishing that optimally k-anonymizing the label se- quences of edge-labeled graphs is intractable. We show how this result implies intractability for other notions of k-anonymization in literature. We also consider the case of bipartite social network graphs which arise from the representation of distinct entities, such as movies and viewers, pa- tients and drugs, or products and customers. Within this setting we demon- strate that, although k-anonymizing edge-labeled graphs is intractable for k ≥ 3, polynomial time algorithms exist for arbitrary bipartite graphs when k = 2 and for unlabeled bipartite graphs irrespective of the value of k. Finally, in this paper we extend the study of attribute disclosure within the context of social networks by defining t-closeness, a measure of how effec- tively an adversary can determine sensitive information about members of a k-anonymous social network."