Linkage Deanonymization Risks, Data-Matching and Privacy: A Case Study

Data anonymization is a widely used solution for preserving users' privacy in data publishing. It is achieved through Privacy Preserving Data Publishing (PPDP), which provides a set of models, tools, and methods to defend against the privacy threats. However, data anonymization has been considered insufficient to protect data privacy for years. New de-anonymization techniques increase the risk of breaching privacy protection through data anonymization. Previous researchers write papers on de-anonymization techniques. In most cases, the dataset used for simulation had a limited range and small scale. In this paper, we aim to evaluate the potential of large-scale datasets being vulnerable to linkage attacks with external social media network data. The data set resource comes from the April 2023 Labour Force Survey (Statistics Canada, 2023). The team estimates the probability of successful linkage attacks of a dataset drawn from the entire population with published attributes that could be linked with publicly available data with the same individually specific attributes. The result shows that the estimated deanonymization risks for each of Newfoundland and Labrador, Prince Edward Island, and Manitoba are 12.5%, 12.5% and 1.9% respectively. It can be summarized that de-anonymization risk is inversely proportional to the underlying population size (or number of entries). It is intuitive in that the more diverse the population, the less risk of deanonymization through linkage with selected attributes. Therefore, we conclude that a more extensive scaling and more comprehensive entries dataset decreases the probability of successful linkage de-anonymization attacks.