Peter Christen

Linking Scottish vital event records using family groups

Publisher: Informa UK Limited

Date: 25-03-2019

DOI: 10.1080/01615440.2019.1571466

A Vulnerability Assessment Framework for Privacy-preserving Record Linkage

Publisher: Association for Computing Machinery (ACM)

Date: 27-06-2023

DOI: 10.1145/3589641

Abstract: The linkage of records to identify common entities across multiple data sources has gained increasing interest over the last few decades. In the absence of unique entity identifiers, quasi-identifying attributes such as personal names and addresses are generally used to link records. Due to privacy concerns that arise when such sensitive information is used, privacy-preserving record linkage (PPRL) methods have been proposed to link records without revealing any sensitive or confidential information about these records. Popular PPRL methods such as Bloom filter encoding, however, are known to be susceptible to various privacy attacks. Therefore, a systematic analysis of the privacy risks associated with sensitive databases as well as PPRL methods used in linkage projects is of great importance. In this article we present a novel framework to assess the vulnerabilities of sensitive databases and existing PPRL encoding methods. We discuss five types of vulnerabilities: frequency, length, co-occurrence, similarity, and similarity neighborhood, of both plaintext and encoded values that an adversary can exploit in order to reidentify sensitive plaintext values from encoded data. In an experimental evaluation we assess the vulnerabilities of two databases using five existing PPRL encoding methods. This evaluation shows that our proposed framework can be used in real-world linkage applications to assess the vulnerabilities associated with sensitive databases to be linked, as well as with PPRL encoding methods.

An analysis of one-to-one matching algorithms for entity resolution

Publisher: Springer Science and Business Media LLC

Date: 18-04-2023

DOI: 10.1007/S00778-023-00791-3

Abstract: Entity resolution (ER) is the task of finding records that refer to the same real-world entities. A common scenario, which we refer to as Clean-Clean ER, is to resolve records across two clean sources (i.e., they are duplicate-free and contain one record per entity). Matching algorithms for Clean-Clean ER yield bipartite graphs, which are further processed by clustering algorithms to produce the end result. In this paper, we perform an extensive empirical evaluation of eight bipartite graph matching algorithms that take as input a bipartite similarity graph and provide as output a set of matched records. We consider a wide range of matching algorithms, including algorithms that have not previously been applied to ER, or have been evaluated only in other ER settings. We assess the relative performance of these algorithms with respect to accuracy and time efficiency over ten established real-world data sets, from which we generated over 700 different similarity graphs. Our results provide insights into the relative performance of these algorithms and guidelines for choosing the best one, depending on the data at hand.

A note on using the F-measure for evaluating record linkage algorithms

Publisher: Springer Science and Business Media LLC

Date: 19-04-2017

DOI: 10.1007/S11222-017-9746-6

Precise and Fast Cryptanalysis for Bloom Filter Based Privacy-Preserving Record Linkage

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 11-2019

DOI: 10.1109/TKDE.2018.2874004

A scalable privacy-preserving framework for temporal record linkage

Publisher: Springer Science and Business Media LLC

Date: 11-06-2020

DOI: 10.1007/S10115-019-01370-1

Unsupervised Identification of Abnormal Nodes and Edges in Graphs

Publisher: Association for Computing Machinery (ACM)

Date: 28-12-2022

DOI: 10.1145/3546912

Abstract: Much of today’s data are represented as graphs, ranging from social networks to bibliographic citations. Nodes in such graphs correspond to records that generally represent entities, while edges represent relationships between these entities. Both nodes and edges in a graph can have attributes that characterize the entities and their relationships. Relationships are either explicitly known (like friends in a social network), or they are inferred using link prediction (such as two babies are siblings because they have the same mother). Any graph representing real-world data likely contains nodes and edges that are abnormal, and identifying these can be important for outlier detection in applications ranging from crime and fraud detection to viral marketing. We propose a novel approach to the unsupervised detection of abnormal nodes and edges in graphs. We first characterize nodes and edges using a set of features, and then employ a one-class classifier to identify abnormal nodes and edges. We extract patterns of features from these abnormal nodes and edges, and apply clustering to identify groups of patterns with similar characteristics. We finally visualize these abnormal patterns to show co-occurrences of features and relationships between those features that mostly influence the abnormality of nodes and edges. We evaluate our approach on datasets from erse domains, including historical birth certificates, COVID patient records, e-mails, books, and movies. This evaluation demonstrates that our approach is well suited to identify both abnormal nodes and edges in graphs in an unsupervised way, and it can outperform several baseline anomaly detection techniques.

An Evaluation Framework for Privacy-Preserving Record Linkage

Publisher: Journal of Privacy and Confidentiality

Date: 06-2014

DOI: 10.29012/JPC.V6I1.636

Abstract: Privacy-preserving record linkage (PPRL) addresses the problem of identifying matching records from different databases that correspond to the same real-world entities using quasi-identifying attributes (in the absence of unique entity identifiers), while preserving privacy of these entities. Privacy is being preserved by not revealing any information that could be used to infer the actual values about the records that are not reconciled to the same entity (non-matches), and any confidential or sensitive information (that is not agreed upon by the data custodians) about the records that were reconciled to the same entity (matches) during or after the linkage process. The PPRL process often involves three main challenges, which are scalability to large databases, high linkage quality in the presence of data quality errors, and sufficient privacy guarantees. While many solutions have been developed for the PPRL problem over the past two decades, an evaluation and comparison framework of PPRL solutions with standard numerical measures defined for all three properties (scalability, linkage quality, and privacy) of PPRL has so far not been presented in the literature. We propose a general framework with normalized measures to practically evaluate and compare PPRL solutions in the face of linkage attack methods that are based on an external global dataset. We conducted experiments of several existing PPRL solutions on real-world databases using our proposed evaluation framework, and the results show that our framework provides an extensive and comparative evaluation of PPRL solutions in terms of the three properties.

DLforum – A multidisciplinary online discussion forum for data linkage researchers and practitioners

Publisher: Swansea University

Date: 20-02-2018

DOI: 10.23889/IJPDS.V3I1.420

Abstract: Data linkage, the process of identifying records that refer to the same entities across databases, is a crucial component of Population Data Science. Data linkage has a history going back over fifty years with many different methods and techniques being developed in various disciplines including computer science, statistics, and health informatics. Data linkage researchers and practitioners are commonly only familiar with methods and techniques that have been developed or are used in their own discipline, and they often only follow research that is being published at venues in their own discipline. There is currently no single online resource that allows data linkage researchers and practitioners across different disciplines to exchange ideas, post questions, or advertise new publications, software, open positions, or upcoming conferences and workshops. This leads to a communication gap in the multi-disciplinary field of data linkage. We aim to address this gap with the DLforum, a public online discussion forum for data linkage. DLforum contains several discussion areas, including publication announcements, resources (software and data sets), information about upcoming conferences and workshops, job opportunities, and general questions related to data linkage. The forum includes a moderation process where all registered users can post content and reply to posts by other users. We anticipate that the number of users registered and the amount of content posted in the forum will show that such an online forum is of value to data linkage researchers and practitioners from different disciplines to effectively communicate and exchange their knowledge, and thus form an online community of practice. In this paper we describe the methods of developing the DLforum, its structure and content, and our plan on how to evaluate the forum. The DLforum is freely available at: dmm.anu.edu.au/DLforum/

A Review of the F-Measure: Its History, Properties, Criticism, and Alternatives

Publisher: Association for Computing Machinery (ACM)

Date: 06-10-2023

DOI: 10.1145/3606367

Taxonomy of Attacks on Privacy-Preserving Record Linkage

Publisher: Journal of Privacy and Confidentiality

Date: 29-07-2022

DOI: 10.29012/JPC.764

Abstract: Record linkage is the process of identifying records that corresponds to the same real-world entities across different databases. Due to the absence of unique entity identifiers, record linkage is often based on quasi-identifying values of entities (in iduals) such as their names and addresses. However, regulatory ethical and legal obligations can limit the use of such personal information in the linkage process in order to protect the privacy and confidentiality of entities. Privacy-preserving record linkage (PPRL) aims to develop techniques that enable the linkage of records without revealing any sensitive or confidential information about the entities that are represented by these records. Over the past two decades various PPRL techniques have been proposed to securely link records between different databases by encrypting and/or encoding sensitive values. However, some PPRL techniques, such as popular Bloom filter encoding, have shown to be susceptible to privacy attacks. These attacks exploit the weaknesses of PPRL techniques by trying to reidentify encrypted and/or encoded sensitive values. In this paper we propose a taxonomy for analysing such attacks on PPRL where we categorise attacks across twelve dimensions, including different types of adversaries, different attack types, assumed knowledge of the adversary, the vulnerabilities of encoded and/or encrypted values exploited by an attack, and assessing the success of attacks. Our taxonomy can be used by data custodians to analyse the privacy risks associated with different PPRL techniques in terms of existing as well as potential future attacks on PPRL.

Privacy Attack on Multiple Dynamic Match-key based Privacy-Preserving Record Linkage

Publisher: Swansea University

Date: 11-08-2020

DOI: 10.23889/IJPDS.V5I1.1345

Abstract: Introduction Over the last decade, the demand for linking records about people across databases has increased in various domains. Privacy challenges associated with linking sensitive information led to the development of privacy-preserving record linkage techniques. The multiple dynamic match-key encoding approach recently proposed by Randall et al. (IJPDS, 2019) is such a technique aimed at providing sufficient privacy for linkage applications while obtaining high linkage quality. However, the use of this encoding in large databases can reveal frequency information that can allow the re-identification of encoded values. Objectives We propose a frequency-based attack to evaluate the privacy guarantees of multiple dynamic match-key encoding. We then present two improvements to this match-key encoding approach to prevent such a privacy attack. Methods The proposed attack analyses the frequency distributions of in idual match-keys in order to identify the attributes used for each match-key, where we assume the adversary has access to a plain-text database with similar characteristics as the encoded database. We employ a set of statistical correlation tests to compare the frequency distributions of match-key values between the encoded and plain-text databases. Once the attribute combinations used for match-keys are discovered, we then re-identify encoded sensitive values by utilising a frequency alignment method. Next, we propose two modifications to the match-key encoding one to alter the original frequency distributions and another to make the frequency distributions uniform. Both will help to prevent frequency-based attacks. Results We evaluate our privacy attack using two large real-world databases. The results show that in certain situations the attack can successfully re-identify a set of sensitive values encoded using the multiple dynamic match-key encoding approach. On the databases used in our experiments, the attack is able to re-identify plain-text values with a precision and recall of both up to 98%. Furthermore, we show that our proposed improvements are able to make this attack harder to perform with only a small reduction in linkage quality. Conclusions Our proposed privacy attack demonstrates the weaknesses of multiple match-key encoding that should be taken into consideration when linking databases that contain sensitive personal information. Our proposed modifications ensure that the multiple dynamic match-key encoding approach can be used securely while retaining high linkage quality.

F*: an interpretable transformation of the F-measure

Publisher: Springer Science and Business Media LLC

Date: 03-2021

DOI: 10.1007/S10994-021-05964-1

Abstract: The F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning the appropriateness of combining two aspects of performance as conceptually distinct as precision and recall, and also questioning whether the harmonic mean is the best way to combine them. To ease this concern, we describe a simple transformation of the F-measure, which we call $$F^*$$ F ∗ (F-star), which has an immediate practical interpretation.

Australian National University

Location: Australia

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Universität Basel

Location: Switzerland

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Discovery Projects - Grant ID: DP160101934

Start Date: 04-2016

End Date: 12-2020

Amount: $327,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP130101801

Start Date: 03-2013

End Date: 08-2018

Amount: $350,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0453463

Start Date: 2004

End Date: 12-2007

Amount: $100,668.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP100200079

Start Date: 05-2011

End Date: 02-2015

Amount: $240,000.00

Funder: Australian Research Council