Scalable Privacy-Preserving Linking of Multiple Databases Using Counting Bloom Filters

Vatsalan, Dinusha; Christen, Peter; Rahm, Erhard

doi:10.1109/icdmw.2016.0130

Cited by 20 publications

(25 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, our MP-PPRL protocol has a worst-case quadratic computation complexity and a linear communication complexity in the number of records n and databases p, which are both significantly lower than the exponential complexities of earlier MP-PPRL protocols [23,24,28]. Please note that extending existing PPRL techniques (that can link two databases with quadratic complexity) to multi-database linkage requires the additional step of clustering once the pairwise similarities have been calculated.…”

Section: Complexity Analysismentioning

confidence: 94%

“…We describe our protocol using the Bloom filter (BF) encoding technique, which is widely used in both research and practical applications of PPRL [8,33,34]. We also provide an improved solution for privacy-preservation in the multiparty context using counting Bloom filter (CBF) encoding [24].…”

Section: Preliminariesmentioning

confidence: 99%

“…In order to overcome inference attacks by the LU on the BFs, the counting Bloom filter (CBF)-based approach (described in Section 3.3) can be applied where the LU sequentially gets a CBF from the relevant set of parties for each cluster of records. Using the CBF the LU can calculate cluster similarity as equivalent to calculating cluster similarity using individual BFs [24]. CBFs significantly reduce the risk of inference attack compared to BFs [24].…”

Section: Privacy Analysismentioning

confidence: 99%

“…The main drawbacks of these small number of existing MP-PPRL approaches are that they either (1) only consider the blocking step to reduce the matching space [22] but not how the matching is done, (2) only support exact matching, which classifies record sets as matches if their masked QIDs are exactly the same [4], (3) are applicable to QIDs of categorical data only (however, linkage using QIDs of string data, such as names and addresses, is required in many real applications [8,23]), or (4) they do not support subset matching where records that match across subsets of databases also need to be identified in addition to records that match across all databases. The pri-mary challenge of MP-PPRL is the complexity of linkage, which generally is exponential with the number of databases to be linked and their sizes [24]. This challenge multiplies when matching records from any possible subset across databases need to be identified.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Incremental clustering techniques for multi-party Privacy-Preserving Record Linkage

Vatsalan

Christen

Rahm

2020

Data & Knowledge Engineering

Self Cite

View full text Add to dashboard Cite

Privacy-Preserving Record Linkage (PPRL) supports the integration of sensitive information from multiple datasets, in particular the privacy-preserving matching of records referring to the same entity. PPRL has gained much attention in many application areas, with the most prominent ones in the healthcare domain. PPRL techniques tackle this problem by conducting linkage on masked (encoded) values. Employing PPRL on records from multiple (more than two) parties/sources (multi-party PPRL, MP-PPRL) is an increasingly important but challenging problem that so far has not been sufficiently solved. Existing MP-PPRL approaches are limited to finding only those entities that are present in all parties thereby missing entities that match only in a subset of parties. Furthermore, previous MP-PPRL approaches face substantial scalability limitations due to the need of a large number of comparisons between masked records. We thus propose and evaluate new MP-PPRL approaches that find matches in any subset of parties and still scale to many parties. Our approaches maintain all matches within clusters, where these clusters are incrementally extended or refined by considering records from one party after the other. An empirical evaluation using multiple real datasets ranging from 3 to 26 parties each containing up to 5 million records validates that our protocols are efficient, and significantly outperform existing MP-PPRL approaches in terms of linkage quality and scalability.

show abstract

Section: Complexity Analysismentioning

confidence: 94%

Section: Preliminariesmentioning

confidence: 99%

Section: Privacy Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incremental clustering techniques for multi-party Privacy-Preserving Record Linkage

Vatsalan

Christen

Rahm

2020

Data & Knowledge Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Existing works in that area include [51], [52], [53]. However, it may be unsecure to rely on Bloom filters for privacy, as some works highlighted [54] or addressed [55], [56].…”

Section: Introductionmentioning

confidence: 99%

Accurate Filtering of Privacy-Sensitive Information in Raw Genomic Data

Decouchant

Fernandes

Voelp

et al. 2018

Preprint

View full text Add to dashboard Cite

Sequencing thousands of human genomes has enabled breakthroughs in many areas, among them precision medicine, the study of rare diseases, and forensics. However, mass collection of such sensitive data entails enormous risks if not protected to the highest standards. In this article, we follow the position and argue that post-alignment privacy is not enough and that data should be automatically protected as early as possible in the genomics workflow, ideally immediately after the data is produced. We show that a previous approach for filtering short reads cannot extend to long reads and present a novel filtering approach that classifies raw genomic data (i.e., whose location and content is not yet determined) into privacy-sensitive (i.e., more affected by a successful privacy attack) and non-privacy-sensitive information. Such a classification allows the fine-grained and automated adjustment of protective measures to mitigate the possible consequences of exposure, in particular when relying on public clouds. We present the first filter that can be indistinctly applied to reads of any length, i.e., making it usable with any recent or future sequencing technologies. The filter is accurate, in the sense that it detects all known sensitive nucleotides except those located in highly variable regions (less than 10 nucleotides remain undetected per genome instead of 100,000 in previous works). It has far less false positives than previously known methods (10% instead of 60%) and can detect sensitive nucleotides despite sequencing errors (86% detected instead of 56% with 2% of mutations). Finally, practical experiments demonstrate high performance, both in terms of throughput and memory consumption.

show abstract

Private record linkage with linkage maps

Patel

Dewri

2022

Security and Privacy

View full text Add to dashboard Cite

Private record linkage is an actively pursued research area to facilitate the linkage of database records under the constraints of regulations that do not allow linkage agents to learn sensitive identities of record owners. Recent works have shown that linkage using commutative ciphers, which were discarded earlier for efficiency concerns, can be made feasible by leveraging precomputations, data parallelism, and probabilistic key reuse approaches. In this work, we propose further optimizations that can be performed to improve the runtime efficiency of such an approach. We transition from modular exponentiation ciphers to elliptic curve operations to improve precomputation time, eliminate memory intensive comparisons of encrypted values, and introduce data structures to detect negative comparisons. We benchmark the proposed approach using real world demographics data, and provide an extensive study of the parametric aspects of the approach. We also supplement our execution time results with an assessment of the residual privacy risk left by the approach. The approach can perform a linkage of two datasets with 105 records each in 20 minutes in a commodity laptop. This is achieved by eliminating the need to compare more than 70% of the record pairs. By design, the linkage accuracy is also retained at the same level as a nonprivate record linkage procedure.

show abstract

Scalable Privacy-Preserving Linking of Multiple Databases Using Counting Bloom Filters

Cited by 20 publications

References 35 publications

Incremental clustering techniques for multi-party Privacy-Preserving Record Linkage

Incremental clustering techniques for multi-party Privacy-Preserving Record Linkage

Accurate Filtering of Privacy-Sensitive Information in Raw Genomic Data

Private record linkage with linkage maps

Contact Info

Product

Resources

About