Data and Structural k-Anonymity in Social Networks

2012

Privacy preserving analysis of a social network aims at a better understanding of the network and its behavior, while at the same time protecting the privacy of its individuals. We propose an anonymization method for weighted graphs, i.e., for social networks where the strengths of links are important. This is in contrast with many previous studies which only consider unweighted graphs. Weights can be essential for social network analysis, but they pose new challenges to privacy preserving network analysis. In this paper, we mainly consider prevention of identity disclosure, but we also touch on edge and edge weight disclosure in weighted graphs. We propose a method that provides k-anonymity of nodes against attacks where the adversary has information about the structure of the network, including its edge weights. The method is efficient, and it has been evaluated in terms of privacy and utility on real word datasets.

—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.

With an abundance of social network data being released, the need to protect sensitive information within these networks has become an important concern of data publishers. In this paper we focus on the popular notion of k-anonymization as applied to node degrees in a social network. Given such a network N , the problem we study is to transform N to N ′ , such that the degree of each node in N ′ is attained by at least k − 1 other nodes in N ′ . Apart from previous work, we permit modifications to the node set, rather than the edge set, and this offers unique advantages with respect to the utility of the released anonymized network. We study both vertex-labeled and unlabeled graphs, since instances of each occur in real-world social networks. Under the constraint of minimum node additions, we show that on vertex-labeled graphs, the problem is NP-complete. For unlabeled graphs, we give an efficient (near-linear) algorithm and show that it gives solutions that are optimal modulo k, a guarantee that is novel in the literature. Additionally, we demonstrate empirically that commonly-studied structural properties of the network, such as clustering coefficient, are quite minorly distorted by the anonymization procedure.

"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."

In this era of 20 th century, online social networks such as Facebook and Twitter play a very important role in everyone's life. Social network data, regarding any individual organization, can be published online at any time, in which there is a risk of information leakage of anyone's personal data. Hence, preserving the privacy of individual organizations and companies is needed before data are published online. Therefore, this research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exist for tabular data but we cannot apply directly to the structured complex graph data, which consist of vertices represented as individuals and edges represented as some kind of connection or relationship between the nodes. Various techniques such as K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes, and techniques such as edge perturbation and edge randomization are available to provide privacy to edges in social network graphs. Development of new techniques by integration into the exiting techniques such as K-anonymity, edge perturbation, edge randomization, and L-diversity to provide more privacy to relational data and social network data is ongoing in the best possible manner.

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2012

Social networks such as Facebook, LinkedIn, or Twitter have nowadays a global reach that surpassed all previous expectations. Many social networks gather confidential information of their users, and as a result, the privacy in social networks has become a topic of general interest. To defend against privacy violations, several social network anonymization models were introduced. In this paper, we empirically study how well several structural properties of a social network are preserved through an anonymization process. We first anonymize several real and synthetic social networks using the kanonymous cluster social network model, and then we compare how well structural properties such as diameter, centrality measures, clustering coefficients, and topological indices are preserved between the original networks and their anonymized versions. Our experiments show that there are correlations between the structural properties' values obtained from the original network and from the corresponding anonymized networks. Preserving such graph properties through anonymization might be extremely important / essential for subsequent graph-mining of the anonymized networks.

IAEME PUBLICATION, 2013

Developing privacy preserving mechanisms for data sharing across network for research purposes and business decisions has become one of the issues of the days research interest. L.Sweeney et.al., (2002) [26] developed the concept of k-anonymity, a model for protecting privacy which poses the condition that a database to be k-anonymous, then each record is indistinguishable from at least k-1 other records with respect to their quasi-identifiers. Despite the k-anonymity model, an intruder may gain access the sensitive information if a set of nodes share similar attributes. In this paper we systematically analyze the pure structure anonymization mechanisms and models proposed in the literature. Also we make a detailed study on k-degree-l-diversity anonymity model, which takes into consideration the structural information and sensitive labels of individuals as well. Also the study the algorithmic impact of adding noise nodes to original graph and the rigorous analyses on the theoretical limitations of the appended noise nodes and its impact.

Journal of Computer and Knowledge Engineering, 2023

Nowadays, with the development of social networks, the risk of disclosure of users' information has also increased, which has caused serious concerns among users. Accordingly, privacy preserving on social networks is a significant issue that has attracted much attention. Although there are various methods for preserving privacy on social networks, most of the existing methods are based on the universal approach that considers the same level of preservation for all users and only some of them consider individual personalized privacy requirements, which is very limited, and those are based on users' willing to share friends list and sensitive information with other users. This study focuses on a new scheme of personalized privacy preserving based on k-anonymity which can anonymize the social network graph based on the personalized privacy requirements of each individual. We develop a Modified Degree Privacy Level Sequence (MDPLS) Algorithm and execute experiments on two datasets. The results of the experiments show that in this new method of social network graph anonymization, when we consider the personalized privacy requirements, the costs of the anonymity process are reduced and data utility is improved in comparison with the situation where we only consider one level of privacy for all users, i.e., universal approach.

The popularity of social network sites has increased extremely during the previous years. Social network sites provide an intimacy interactive platform on the Internet for exchanging information among users. Users may disclose their ideas, comments, pictures or videos, secrets about their business or other private information that may be used by inappropriate user to threaten users' future decisions or positions. Thus, the goal of this paper is to explain how users' data can be anonymized to mitigate privacy concerns through information dissemination. The results depicts that although anonymization of data cannot protect the privacy of data completely, it can reduce the possibility of re-identification.

2007

Abstract 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 (eg, email correspondence) and sharing the data in full can result in unacceptable disclosures.