# A Comparison of Blocking Methods for Record Linkage

arXiv: https://arxiv.org/abs/1407.3191 · HTML: https://mlsanity.com/paper/1407.3191/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2014.

## Built on by (most cited first, 7 of 7)

- [Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale](https://mlsanity.com/paper/2204.07309/) (2022)
- [Evaluating Blocking Biases in Entity Matching](https://mlsanity.com/paper/2409.16410/) (2024)
- [Multifile Partitioning for Record Linkage and Duplicate Detection](https://mlsanity.com/paper/2110.03839/) (2021)
- [Linking Representations with Multimodal Contrastive Learning](https://mlsanity.com/paper/2304.03464/) (2023)
- [A Primer on the Data Cleaning Pipeline](https://mlsanity.com/paper/2307.13219/) (2023)
- [Heterogeneous Entity Matching with Complex Attribute Associations using BERT and Neural Networks](https://mlsanity.com/paper/2309.11046/) (2023)
- [Leveraging Language Models for Automated Patient Record Linkage](https://mlsanity.com/paper/2504.15261/) (2025)

## Abstract (arXiv)

Record linkage seeks to merge databases and to remove duplicates when unique identifiers are not available. Most approaches use blocking techniques to reduce the computational complexity associated with record linkage. We review traditional blocking techniques, which typically partition the records according to a set of field attributes, and consider two variants of a method known as locality sensitive hashing, sometimes referred to as "private blocking." We compare these approaches in terms of their recall, reduction ratio, and computational complexity. We evaluate these methods using different synthetic datafiles and conclude with a discussion of privacy-related issues.