Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization

Torra, Vicenç; Salas, Julián

doi:10.1007/978-3-030-31500-9_8

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…No decrease can be obtained for two of the functions. Moreover, since the flow between users of Bitcoin can be poured as a graph, inspired by the studies combining differential privacy and graphs [31][32][33][34], we examine the applicability of differential privacy mechanisms for graphs to Bitcoin, as well. The graph perturbation decreases the fraction of the cases violating differential privacy from 1 2 ⁄ to 1 4 ⁄ for three functions, and from 1 4 ⁄ to 1 8 ⁄ for one of the functions.…”

Section: B Contributionmentioning

confidence: 99%

Investigation and Application of Differential Privacy in Bitcoin

Khalilov

Lévi

2022

IEEE Access

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Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy. Generally used methods for improvement include mixing protocols, ring signatures, zero-knowledge proofs, homomorphic commitments, and off-chain storage systems. To the best of our knowledge, in the literature, there is no study examining Bitcoin in terms of differential privacy, which is a privacy notion coming up with some mechanisms that enable running useful statistical queries without identifying any personal information. In this paper, we provide a theoretical examination of differential privacy in Bitcoin. Our motivation arises from the idea that the Bitcoin public blockchain structure can benefit from differential privacy mechanisms for improved privacy, both making anonymization and privacy breaches by direct queries impossible, and preserving the checkability of the integrity of the blockchain. We first examine the current Bitcoin implementation for four query functions using the differential privacy formulation. Then, we present the feasibility of the utilization of two differential privacy mechanisms in Bitcoin; the noise addition to the transaction amounts and the user graph perturbation. We show that these mechanisms decrease the fraction of the cases violating differential privacy, therefore they can be used for improving anonymity and privacy in Bitcoin. Moreover, we showcase the noise addition to transaction amounts by using IBM Differential Privacy Library. We compare four differential privacy mechanisms for varying privacy parameter values and determine the feasible mechanisms and the parameters.

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Section: B Contributionmentioning

confidence: 99%

Investigation and Application of Differential Privacy in Bitcoin

Khalilov

Lévi

2022

IEEE Access

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“…After this step, we need to represent the frequent patterns and users as a bipartite graph. In [6], a formalization of graph perturbation was proposed as noise graph addition. It was shown in [7] that it provides ε-edge-differential privacy.…”

Section: Noise Graph Additionmentioning

confidence: 99%

“…It was shown in [7] that it provides ε-edge-differential privacy. We adapted the basic definitions and notations from [6,7] for our use-case.…”

Section: Noise Graph Additionmentioning

confidence: 99%

“…Then, the method represents the relation of individuals and their behavior through a bipartite graph of users and frequent patterns. After that, the sanitization mechanism protects the bipartite graph by adding noise graphs [6] to obtain differential privacy guarantees [7]. Finally, the sequential patterns with differential privacy are obtained from the sanitized bipartite graph.…”

Section: Introductionmentioning

confidence: 99%

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A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns

Núñez-del-Prado

Maehara²,

Salas

et al. 2022

Applied Sciences

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Currently, individuals leave a digital trace of their activities when they use their smartphones, social media, mobile apps, credit card payments, Internet surfing profile, etc. These digital activities hide intrinsic usage patterns, which can be extracted using sequential pattern algorithms. Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases. These sequences represent individuals’ common behavior and could contain sensitive information. Thus, sequential patterns should be sanitized to preserve individuals’ privacy. Hence, many algorithms have been proposed to accomplish this task. However, these techniques add noise to the candidate support before they are validated as, frequently, and thus, they cannot be applied without having access to all the users’ sequences data. In this paper, we propose a differential privacy graph-based technique for publishing frequent sequential patterns. It is applied at the post-processing stage; hence it may be used to protect frequent sequential patterns after they have been extracted, without the need to access all the users’ sequences. To validate our proposal, we performed a detailed assessment of its utility as a pattern mining algorithm and calculated the impact of the sanitization mechanism on a recommender system. We further evaluated its information loss disclosure risk and performed a comparison with the DP-FSM algorithm.

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“…However this is a different scenario from that considered in this paper, where we make the assumption that structure and identities are not available to the attacker and are instead the information to be protected. Scenarios where the attacker has access to both the original structure as well as the node identities could instead be handled adding structural noise [36] to the inter-group connections of ε-regular pairs (see Section 3).…”

Section: Introductionmentioning

confidence: 99%

k-Anonymity on Graphs Using the Szemerédi Regularity Lemma

Minello

Rossi

Torsello

2021

IEEE Trans. Netw. Sci. Eng.

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Graph anonymization aims at reducing the ability of an attacker to identify the nodes of a graph by obfuscating its structural information. In k-anonymity, this means making each node indistinguishable from at least other k − 1 nodes. Simply stripping the nodes of a graph of their identifying label is insufficient, as with enough structural knowledge an attacker can still recover the nodes identities. We propose an algorithm to enforce k-anonymity based on the Szemerédi regularity lemma. Given a graph, we start by computing a regular partition of its nodes. The Szemerédi regularity lemma ensures that such a partition exists and that the edges between the sets of nodes behave quasi-randomly. With this partition to hand, we anonymize the graph by randomizing the edges within each set, obtaining a graph that is structurally similar to the original one yet the nodes within each set are structurally indistinguishable. Unlike other k-anonymization methods, our approach does not consider a single type of attack, but instead it aims to prevent any structure-based de-anonymization attempt. We test our framework on a wide range of real-world networks and we compare it against another simple yet widely used k-anonymization technique demonstrating the effectiveness of our approach.

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Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization

Cited by 9 publications

References 37 publications

Investigation and Application of Differential Privacy in Bitcoin

Investigation and Application of Differential Privacy in Bitcoin

A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns

k-Anonymity on Graphs Using the Szemerédi Regularity Lemma

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