K-anonymous path privacy on social graphs

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.

International Journal of Software Engineering and Knowledge Engineering, 2013

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.

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.

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.

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.

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.

Technical Report, TR–IIIA–2010–04, IIIA-CSIC, 2010

In this document we review the state of the art on graph privacy with special emphasis on applications to online social networks, and we review how six different operators modify local topologies, when activity data is included. We consider an aspect which has not been greatly covered in the specialized literature on graph privacy: adding, deleting and disaggregation of nodes. We also cover the following key considerations: (i) choice of six different operators to modify the graph; (ii) simulated annealing to find the optimum graph using a fitness function based on information loss and disclosure risk; (iii) Use of heuristics to choose graph elements (nodes, edges) to be modified, as a probability weighted by the distribution of an elements statistical characteristics (degree, clustering coefficient and path length) in the original graph; (iv) re-linking of nodes: heuristic which finds the topology whose statistical characteristics are closest to those of the original neighborhood; (v) in the case of the aggregation of two nodes, we choose adjacent nodes rather than isomorphic topologies, in order to maintain the overall structure of the graph; (vi) incorporation of network activity as a weight on the topology characteristics; (vii) a statistically knowledgeable attacker who is able to search for regions of the graph based on statistical characteristics and map those onto a given node and its immediate neighborhood.

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.