Privacy preserving record linkage in the presence of missing values

Yuan, Congying; Hong, Jun; Jurek, Anna; Liu, Weiru; O’Reilly, Dermot

doi:10.1016/j.is.2017.07.001

Cited by 11 publications

(7 citation statements)

References 33 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the similarity between two vertices is at least a minimum threshold s t an edge is created between the corresponding vertices (as shown in Figure 4 for s t = 0.75 and described in lines 14 and 15 in Algorithm 1). (5) The optimal one-to-one mapping (as defined in Section 2) is applied in every iteration i (with 1 ≤ i ≤ p − 1) after edges between the records from party P i+1 (singletons) and clusters of records from parties P 1 to P i have been added. This optimal mapping connects only two highly matching vertices, complying with the assumption of deduplication.…”

Section: Definition 33 (Average Similarity) the Average Similarity mentioning

confidence: 99%

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

Vatsalan

Christen

Rahm

2020

Data & Knowledge Engineering

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: Definition 33 (Average Similarity) the Average Similarity mentioning

confidence: 99%

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

Vatsalan

Christen

Rahm

2020

Data & Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…To rid this vulnerability of these anonymization models, in [15], the authors propose an anonymization model for filling in missing-values with using a lazy decision-tree imputation algorithm for data that is horizontally partitioned between two groups. In [7], an anonymization model for missing-value datasets is also proposed. To achieve privacy preservation constraints, this model imputes the similarity measure between every missing-value and the value of the corresponding field in any of the possible matching tuples from another dataset.…”

Section: Introductionmentioning

confidence: 99%

“…In brief, the anonymization models are proposed by [7,15], they can address privacy violation issues in missingvalue datasets. In [15], the lazy decision-tree algorithm is applied.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Achieving Privacy Preservation Constraints in Missing-Value Datasets

2020

View full text Add to dashboard Cite

Privacy violation issues must be taken into consideration when datasets are released for public use. To address these issues, there are various anonymization models to be proposed, e.g., k-anonymity, l-diversity, and t-closeness. However, these anonymization models generally propose to address privacy violation issues in datasets which are assumed that all attributes of them must be completed. Thus, these anonymization models could be insufficient to address privacy violation issues in such a dataset which is allowed to collect missing-values, e.g., rating datasets and trajectory datasets. Therefore, a new appropriate privacy preservation model for missing-value datasets is proposed by this work. With the proposed model, aside from privacy preservation, the data utility is also maintained as much as possible. Moreover, a suitable data utility metric for missing-value datasets is also presented by this work. Furthermore, the proposed model is shown that it is an NP-Complete problem by reduction from the X3C problem.

show abstract

“…A huge quantity of data from diverse sources is collected and processed, the IoT process may have an important impact on the privacy of users' [82]. In addition, focusing the extending trend on gathering extra personalized and individual data in IoT, there exists a lot of exertions concerning the impact on privacy of the individuals' from an authorized perspective [83]. The data managing or processing of IoT is significantly impacted by position information and consecutively affects its position privacy [84].…”

Section: Introductionmentioning

confidence: 99%

Route Map of Privacy Preservation to IOT

Kathavate¹,

Amudhavel²

2018

IJET

View full text Add to dashboard Cite

Data anonymization is the main feature of privacy preservation, and it assists in eradicating the privacy hazard in data preparation in various applications including IoT. Pseudonymity and Anonymization are two significant security factors that were adopted when sensitive data are shared. In medical field, data is usually distributed horizontally with diverse regions carrying a similar set of characteristics for various anonymization techniques.

show abstract

Privacy preserving record linkage in the presence of missing values

Cited by 11 publications

References 33 publications

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

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

Achieving Privacy Preservation Constraints in Missing-Value Datasets

Route Map of Privacy Preservation to IOT

Contact Info

Product

Resources

About