A Novel Graph-modiﬁcation Technique for User Privacy-preserving on Social Networks

Erfani, Seyedeh Hamideh; Mortazavi, Reza

doi:10.26636/jtit.2019.134319

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…Recent studies on non-randomization techniques prove that they accomplish a significant level of anonymization while maintaining the original structure of a graph [23]. Some famous non-randomization techniques are listed below in Table 1: • k-anonymization: k-anonymization method modifies the original graph by inserting/deleting links or vertices to obtain certain requirement [24]. If an adversary has background knowledge of degrees and relationship details about its victim, it tries to re-identify the victim from the anonymized graph.…”

Section: Graph Modification Techniquesmentioning

confidence: 99%

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

et al. 2021

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With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than 0.8%, also added dummy links are 60% less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data.

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Section: Graph Modification Techniquesmentioning

confidence: 99%

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

et al. 2021

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“…Nowadays, there is a considerable demand for real-world datasets in various data mining tasks. However, the privacy of involved entities usually agitates data owners about the usage of such information [1,2]. Privacy preserving data publishing is the task that addresses the problem.…”

Section: Introduction †mentioning

confidence: 99%

Repeated Record Ordering for Constrained Size Clustering

Mortazavi

2020

IJE

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One of the main techniques used in data mining is data clustering, which has many applications in computer science, biology and social sciences. Constrained clustering is a type of clustering in which side information provided by the user is incorporated into current clustering algorithms. One of the well researched constrained clustering algorithms is called microaggregation. In a microaggregation technique, the algorithm divides the dataset into groups containing at least members, where is a userdefined parameter. The main application of microaggregation is in Statistical Disclosure Control (SDC) for privacy preserving data publishing. A microaggregation algorithm is qualified based on the sum of within-group squared error,. Unfortunately, it has been proven that the optimal microaggregation problem is NP-Hard in general, but the special univariate case can be solved optimally in polynomial time. Many heuristics exist for the general case of the problem that are founded on the univariate case. These techniques order multivariate records in a sequence. This paper proposes a novel method for record ordering. Starting from a conventional clustering algorithm, the proposed method repeatedly puts multivariate records into a sequence and then clusters them again. The process is repeated until no improvement is achieved. Extensive experiments have been conducted in this research to confirm the effectiveness of the proposed method for different parameters and datasets.

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“…In fact, VLC occurs when the target victim is linked to a speci c vertex in the published graph. Clearly, to solve VLC, we can insert some additional edges among vertices [4] or insert additional vertices in the graph [5]. For example, by adding an additional edge (edge [6][7][8], the graph in Figure 2 presents that the vertex 7 (Ada) is not unique.…”

Section: Introductionmentioning

confidence: 99%

“…In this mode, LLC occurs when the sensitive values on the edge of a target victim are estimated, like the recent example. Clearly, according to Figure 3, to solve LLC on edge, we can insert a few additional edges, like edge 7-9, among some vertices or change the values of a few labels on some edges [5,6]. Visibly, this action hides the sensitive labels on edges of Ada among other edges.…”

Section: Introductionmentioning

confidence: 99%

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SNI: Supervised Anonymization Technique to Publish Social Networks Having Multiple Sensitive Labels

Rizi

Dehkordi

bakhsh³

2019

Security and Communication Networks

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In social networks, preserving privacy and preserving correlation among sensitive labels are a matter of trade-off. This paper presents a supervised anonymization technique, SNI (social network immunization), to publish social networks having multiple sensitive labels with correlation. SNI publishes all sensitive labels without distorting them. It publishes sensitive labels along with innovative labels named “partial sensitive labels” in an immune graph and multiple supplementary trees. These graph and trees, by itself or with the combination of other objects, supply correlation among sensitive labels for membership analysis. We present a framework along with an algorithm for extracting the immune graph and supplementary trees. These graph and trees minimize the membership error rate for membership analysis. The practical evaluation of the cancer code label of individuals also indicates the effectiveness of the SNI method.

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A Novel Graph-modiﬁcation Technique for User Privacy-preserving on Social Networks

Cited by 7 publications

References 36 publications

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

Repeated Record Ordering for Constrained Size Clustering

SNI: Supervised Anonymization Technique to Publish Social Networks Having Multiple Sensitive Labels

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