Anonymizing Shortest Paths on Social Network Graphs

Objectives: This paper focuses on a privacy preservation technique which is applied on graphs to preserve sensitive information present as shortest paths. Methods/Statistical Analysis: Provide privacy in Social Network Analysis by protecting the sensitive edge weights with the help of preserving the nearest shortest path lengths as well as shortest paths so that individual confidential information can be protected from multiple type of attacks. This research work provides more privacy then greedy perturbation technique in social network analysis. Findings: Privacy preservation has a tradeoff between the utility of data and preservation of sensitive information. This is achieved by modification of shortest path length in graphs and also maintains the structure of the graph. This procedure enhances the privacy of sensitive information with minimal concerns to utility. Application/Improvements: The privacy preservation technique of edge weight perturbation is applied to social graphs in a small user group to preserve sensitive information when data is shared within the group members. The edge weight perturbation algorithm can be improved by combing the algorithm with the preservation techniques for the user nodes.

IEEE Transactions on Knowledge and Data Engineering, 2000

The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing social networks as a weighted network, edge weight anonymization is also gaining importance. We present Anónimos, a Linear Programming based technique for anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning tree, and maximizing information spread. As a proof of concept, we apply Anónimos to the shortest paths problem and its extensions, prove the correctness, analyze complexity, and experimentally evaluate it using real social network data sets. Our experiments demonstrate that Anónimos anonymizes the weights, improves k-anonymity of the weights, and also scrambles the relative ordering of the edges sorted by weights, thereby providing robust and effective anonymization of the sensitive edge-weights. Additionally, we demonstrate the composability of different models generated using Anónimos, a property that allows a single anonymized graph to preserve multiple linear properties.

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.

Journal of Telecommunications and Information Technology, 2019

The growing popularity of social networks and the increasing need for publishing related data mean that protection of privacy becomes an important and challenging problem in social networks. This paper describes the (k, l k, l k, l)-anonymity model used for social network graph anonymization. The method is based on edge addition and is utility-aware, i.e. it is designed to generate a graph that is similar to the original one. Different strategies are evaluated to this end and the results are compared based on common utility metrics. The outputs confirm that the naïve idea of adding some random or even minimum number of possible edges does not always produce useful anonymized social network graphs, thus creating some interesting alternatives for graph anonymization techniques.

IEEE Transactions on Knowledge and Data Engineering, 2012

The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing social networks as a weighted network, edge weight anonymization is also gaining importance. We present Anónimos, a Linear Programming based technique for anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning tree, and maximizing information spread. As a proof of concept, we apply Anónimos to the shortest paths problem and its extensions, prove the correctness, analyze complexity, and experimentally evaluate it using real social network data sets. Our experiments demonstrate that Anónimos anonymizes the weights, improves k-anonymity of the weights, and also scrambles the relative ordering of the edges sorted by weights, thereby providing robust and effective anonymization of the sensitive edge-weights. Additionally, we demonstrate the composability of different models generated using Anónimos, a property that allows a single anonymized graph to preserve multiple linear properties.

2011 International Conference on Advances in Social Networks Analysis and Mining, 2011

The growing need to address privacy concerns when social network data is released for mining purposes has recently led to considerable interest in various techniques for graph anonymization. In this paper, we study the following problem: Given a social network modeled as an edge-labeled graph G, we aim to make a pre-specified subset of vertices of G k-label sequence anonymous with the minimum number of edge additions. Here, the label sequence of a vertex is the sequence of labels of edges incident to it.

Social Network Analysis and Mining, 2020

Social networks, which have become so popular today, allow their users to share information. The main challenge the users are facing is the security preservation of their information and privacy. Therefore, structural anonymity techniques were introduced that would hide the identity of users. One of the drawbacks of these techniques, which are based on graph modification, is the lack of attention about the structural semantics of graphs. This paper focuses on the popular notion of a privacy protection method called k-degree anonymization and tries to reduce utility loss on the graph. The new k-degree anonymization method, genetic k-degree edge modification, has two steps. The first step includes partitioning of vertices and community detection in the graph. The result of these two determines the needed increase in edges for every vertex in each society to achieve k-degree anonymization. The second step is graph modification using the genetic algorithm by adding some edges between vertices in each community. Average Path Length (APL), Average Clustering Coefficient, and Transitivity (T) are employed to evaluate the method. The proposed algorithm has been tested on four datasets, and the results have shown the average relative performance demonstrates more stability than the other four well-known algorithms. Also, APL criterion in our algorithm is better preserved than all other algorithms; furthermore, Transitivity parameters are the best result in most cases.

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.

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.

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 ...