Record linkage is the challenging task of deciding which records, coming from disparate data sources, refer to the same entity. Established back in 1946 by Halbert L. Dunn [1], the area of record linkage has received tremendous attention over the years due to its numerous real-world applications, and has led to a plethora of technologies, methods, metrics, and systems. A major direction in record linkage regards methods for linking records in a privacy-preserving manner, where sensitive and personally identifiable information in the records is not leaked as part of the linkage process. In this article, we provide an overview of the large body of research literature in privacy-preserving record linkage, discuss the different generations of techniques that have been proposed, their advantages and limitations, and present a taxonomy as well as an extensive survey on the latest generation of methods. We conclude this work with a roadmap to the new generation of analytics-driven techniques that aims to address some of the major challenges in the field.

Healthcare enterprises are starting to adopt cloud computing due to its numerous advantages over traditional infrastructures. This has become a necessity because of the increased volume, velocity and variety of healthcare data, and the need to facilitate data correlation and large-scale analysis. Cloud computing infrastructures have the power to offer continuous acquisition of data from multiple heterogeneous sources, efficient data integration, and big data analysis. At the same time, security, availability, and disaster recovery are critical factors aiding towards the adoption of cloud computing. However, the migration of healthcare workloads to cloud is not straightforward due to the vagueness in healthcare data standards, heterogeneity and sensitive nature of healthcare data, and many regulations that govern its usage. This paper highlights the need for providing healthcare data acquisition using cloud infrastructures and presents the challenges, requirements, use-cases, and best practices for building a state-of-the-art healthcare data ingestion service on cloud.

In privacy-preserving record linkage, a number of data custodians encode their records and submit them to a trusted third-party who is responsible to identify those records that refer to the same real-world entity. In this paper, we propose FEDERAL, a novel record linkage framework that implements methods for anonymizing both string and numerical data values, which are typically present in data records. These methods rely on a strong theoretical foundation for rigorously specifying the dimensionality of the anonymization space, into which the original values are embedded, to provide accuracy and privacy guarantees under various models of privacy attacks. Key component of the applied embedding process is the threshold that is required by the distance computations, which we prove that can be formally specified to guarantee accurate results. We evaluate our framework using three real-world data sets with varying characteristics. Our experimental findings show that FEDERAL offers a complete and effective solution for accurately identifying matching anonymized record pairs (with recall rates constantly above 93%) in large-scale privacy-preserving record linkage tasks.