On the computational complexities of three problems related to a privacy measure for large networks under active attack

Chatterjee, Tanima; DasGupta, Bhaskar; Mobasheri, Nasim; Srinivasan, Venkatkumar; Yero, Ismael G.

doi:10.1016/j.tcs.2018.12.013

Cited by 8 publications

(27 citation statements)

References 20 publications

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“…This paper does not introduce any new privacy model or measure, but simply investigates, both theoretically and empirically, computational problems for a model that is published in "Information Sciences, 328, 403-417, 2016" (reference [43]). There have been several other subsequent papers investigating this privacy measure, e.g., see [9,44,49,34]. Thus, researchers in network privacy are certainly interested in this model or related computational complexity questions.…”

Section: Some Remarks Regarding the Model And Our Contribution (To Avmentioning

confidence: 99%

“…The authors in [9] formalized and analyzed the computational complexities of several optimization problems motivated by the (k, )-anonymity of a network as described in [43]. The notations and terminologies from [9] relevant for this paper are as follows (see Fig 1 for an illustration) 3 :…”

Section: Basic Notations Relevant Background and Problem Formulationsmentioning

confidence: 99%

“…) . By using the terminologies mentioned above, the following three optimization problems were formalized and studied in [9]. We need to stress that one really needs to study the three different problems and consequently the three objectives (namely, k opt , L ≥k opt and L =k opt ) separately because they are motivated by different considerations as explained before and after the problem definitions and as stated in ( ), ( ) and (♠).…”

Section: For a Subset Of Nodesmentioning

confidence: 99%

“…Chatterjee et al in [9] (see also [49]) formalized and analyzed the computational complexities of several optimization problems motivated by the (k, )anonymity of a network as described in [43]. In this article, we consider three of these optimization problems from [9], namely Problems 1-3 as defined in Section 2. A high-level itemized overview of the contribution of this article is as follows (see Section 3 for precise technical statements and details of all contributions):…”

Section: Introductionmentioning

confidence: 99%

“…In Section 3.2, we first describe briefly efficient implementations of the high-level algorithms of Chatterjee et al [9] for Problems 1-3 (namely Algorithms I and II in Section 3.2.1). We then tabulate and discuss the results of applying these implemented algorithms for the following type of network data: eight real social networks listed in Table 3 in Section 3.4.2, the classical undirected Erdös-Rényi random networks G(n, p) for four suitable combinations of n and p, and the scale-free random networks G(n, q) generated by the Barábasi-Albert preferential-attachment model for four suitable combinations of n and q.…”

Section: Introductionmentioning

confidence: 99%

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On analyzing and evaluating privacy measures for social networks under active attack

DasGupta

Mobasheri

Yero

2019

Information Sciences

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Widespread usage of complex interconnected social networks such as Facebook, Twitter and LinkedIn in modern internet era has also unfortunately opened the door for privacy violation of users of such networks by malicious entities. In this article we investigate, both theoretically and empirically, privacy violation measures of large networks under active attacks that was recently introduced in (Information Sciences, 328, 403-417, 2016). Our theoretical result indicates that the network manager responsible for prevention of privacy violation must be very careful in designing the network if its topology does not contain a cycle. Our empirical results shed light on privacy violation properties of eight real social networks as well as a large number of synthetic networks generated by both the classical Erdös-Rényi model and the scale-free random networks generated by the Barábasi-Albert preferential-attachment model.

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Section: Some Remarks Regarding the Model And Our Contribution (To Avmentioning

confidence: 99%

Section: Basic Notations Relevant Background and Problem Formulationsmentioning

confidence: 99%

Section: For a Subset Of Nodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On analyzing and evaluating privacy measures for social networks under active attack

DasGupta

Mobasheri

Yero

2019

Information Sciences

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Conditional adjacency anonymity in social graphs under active attacks

2018

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Social network data is typically made available in a graph format, where users and their relations are represented by vertices and edges, respectively. In doing so, social graphs need to be anonymised to resist various privacy attacks. Among these, the so-called active attacks, where an adversary has the ability to enrol sybil accounts in the social network, have proven difficult to counteract. In this article, we provide an anonymisation technique that successfully thwarts active attacks while causing low structural perturbation. We achieve this goal by introducing (k, G,)-adjacency anonymity: a privacy property based on (k,)-anonymity that alleviates the computational burden suffered by anonymisation algorithms based on (k,)-anonymity and relaxes some of its assumptions on the adversary capabilities. We show that the proposed method is efficient and establish tight bounds on the number of modifications that it performs on the original graph. Experimental results on real-life and randomly generated graphs show that when compared to methods based on (k,)-anonymity, the new method continues to provide protection from equally capable active attackers while introducing a much smaller number of changes in the graph structure.

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On the $$(k,\ell )$$-anonymity of networks via their k-metric antidimension

Fernández,

Kuziak,

Munoz-Marquez

et al. 2023

Sci Rep

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This work focuses on the $$(k,\ell )$$ ( k , ℓ ) -anonymity of some networks as a measure of their privacy against active attacks. Two different types of networks are considered. The first one consists of graphs with a predetermined structure, namely cylinders, toruses, and 2-dimensional Hamming graphs, whereas the second one is formed by randomly generated graphs. In order to evaluate the $$(k,\ell )$$ ( k , ℓ ) -anonymity of the considered graphs, we have computed their k-metric antidimension. To this end, we have taken a combinatorial approach for the graphs with a predetermined structure, whereas for randomly generated graphs we have developed an integer programming formulation and computationally tested its implementation. The results of the combinatorial approach, as well as those from the implementations indicate that, according to the $$(k,\ell )$$ ( k , ℓ ) -anonymity measure, only the 2-dimensional Hamming graphs and some general random dense graphs are achieving some higher privacy properties.

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On the computational complexities of three problems related to a privacy measure for large networks under active attack

Cited by 8 publications

References 20 publications

On analyzing and evaluating privacy measures for social networks under active attack

On analyzing and evaluating privacy measures for social networks under active attack

Conditional adjacency anonymity in social graphs under active attacks

On the $$(k,\ell )$$-anonymity of networks via their k-metric antidimension

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