HashAlign: Hash-Based Alignment of Multiple Graphs

Heimann, Mark; Lee, Wei; Pan, Shengjie; Chen, Kuan‐Yu; Koutra, Danai

doi:10.1007/978-3-319-93040-4_57

Cited by 19 publications

(16 citation statements)

References 15 publications

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“…Many graph matching and alignment algorithms have been developed in research domains ranging from bioinformatics to multimedia information retrieval [11]. Such algorithms can be based on graph kernels [17], node and edge embeddings [14], or hashing [15]. In our approach, as discussed below, we use a Cosine locality sensitive hashing (LSH) based approach because it allows us to explicitly set a trade-off between the performance of the attack (as the number of feature vectors to be compared) versus the probability of comparing similar nodes [15].…”

Section: Node Matchingmentioning

confidence: 99%

“…Such algorithms can be based on graph kernels [17], node and edge embeddings [14], or hashing [15]. In our approach, as discussed below, we use a Cosine locality sensitive hashing (LSH) based approach because it allows us to explicitly set a trade-off between the performance of the attack (as the number of feature vectors to be compared) versus the probability of comparing similar nodes [15]. vectors for which we would need to calculate their similarities.…”

Section: Node Matchingmentioning

confidence: 99%

“…1, we generate one similarity graph from the encoded values and a second similarity graph from a set of plain-text values, where graph nodes correspond to the encoded or plain-text values, respectively, and graph edges correspond to the approximate similarities calculated between those values. We then apply graph matching techniques [1,11,15] to align nodes across these two graphs that are similar with regard to their local network structure, which allows us to match encoded values to plain-text values. This proposed attack can be applied on any PPRL encoding method that calculates similarities between encoded values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Graph Matching Attack on Privacy-Preserving Record Linkage

Vidanage

Christen

Ranbaduge

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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To facilitate advanced analytics, data science projects increasingly require records about individuals to be linked across databases. Generally no unique entity identifiers are available in the databases to be linked, and therefore quasi-identifiers such as names, addresses, and dates of birth are used to link records. The process of linking records without revealing any sensitive or confidential information about the entities represented by these records is known as privacy-preserving record linkage (PPRL). Various encoding and encryption based PPRL methods have been developed in the past two decades. Most existing PPRL methods calculate approximate similarities between records because errors and variations can occur in quasi-identifying attribute values. Even though being used in real-world linkage applications, certain PPRL methods, such as popular Bloom filter encoding, have shown to be vulnerable to cryptanalysis attacks. In this paper we present a novel attack on PPRL methods that exploits the approximate similarities calculated between encoded records. Our attack matches nodes in a similarity graph generated from an encoded database with a corresponding similarity graph generated from a plain-text database to re-identify sensitive values. Our attack is not limited to any specific PPRL method, and in an experimental evaluation we apply it on three PPRL encoding methods using three different databases. This evaluation shows that our attack can successfully re-identify sensitive values from these encodings with high accuracy where no previous attack on PPRL would have been successful. CCS CONCEPTS • Information systems → Entity resolution; • Security and privacy → Cryptanalysis and other attacks; Privacy-preserving protocols; Management and querying of encrypted data.

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Section: Node Matchingmentioning

confidence: 99%

Section: Node Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Graph Matching Attack on Privacy-Preserving Record Linkage

Vidanage

Christen

Ranbaduge

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…Global aligners, on the other hand, use optimization functions that reward matches and penalize mismatches over the entire graph. In [37,36] [18,42,30].…”

Section: Related Literaturementioning

confidence: 99%

Rigid Graph Alignment

Ravindra

Nassar

Gleich

et al. 2019

Complex Networks and Their Applications VIII

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Graph databases have been the subject of significant research and development in the database, data analytics, and applications' communities. Problems such as modularity, centrality, alignment, and clustering have been formalized and solved in various application contexts. In this paper, we focus on databases for applications in which graphs have a spatial basis, which we refer to as rigid graphs. Nodes in such graphs have preferred positions relative to their graph neighbors. Examples of such graphs include abstractions of large biomolecules (e.g., in drug databases), where edges corresponding to chemical bonds have preferred lengths, functional connectomes of the human brain (e.g., the HCP database [13]), where edges corresponding to co-firing regions of the brain have preferred anatomical distances, and mobile device/ sensor communication logs, where edges corresponding to point-to-point communications across devices have distance constraints. When analyzing such networks it is important to consider edge lengths; e.g., when identifying conserved patterns through graph alignment, it is important for conserved edges to have correlated lengths, in addition to topological similarity. Similar considerations exist for clustering (densely connected regions of short edges) and centrality (critical edges with large weights). Problem Formulation Problem definitionWe define the rigid graph alignment problem by first reviewing existing graph and structure alignment formulations, and use these to motivate our new problem.

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“…In contrast, our approach can be applied to attributed and unattributed graphs with virtually no change in formulation, and is unsupervised: it does not require prior alignment information to find high-quality matchings. Recent work [12] has used hand-engineered features, while our proposed approach leverages the power of latent feature representations.…”

Section: Related Workmentioning

confidence: 99%

Regal

Heimann

Shen

Safavi

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Self Cite

200

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Problems involving multiple networks are prevalent in many scientific and other domains. In particular, network alignment, or the task of identifying corresponding nodes in different networks, has applications across the social and natural sciences. Motivated by recent advancements in node representation learning for single-graph tasks, we propose REGAL (REpresentation learning-based Graph ALignment), a framework that leverages the power of automaticallylearned node representations to match nodes across different graphs. Within REGAL we devise xNetMF, an elegant and principled node embedding formulation that uniquely generalizes to multi-network problems. Our results demonstrate the utility and promise of unsupervised representation learning-based network alignment in terms of both speed and accuracy. REGAL runs up to 30× faster in the representation learning stage than comparable methods, outperforms existing network alignment methods by 20 to 30% accuracy on average, and scales to networks with millions of nodes each.

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HashAlign: Hash-Based Alignment of Multiple Graphs

Cited by 19 publications

References 15 publications

A Graph Matching Attack on Privacy-Preserving Record Linkage

A Graph Matching Attack on Privacy-Preserving Record Linkage

Rigid Graph Alignment

Regal

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