Identification Attack

Identification attacks, also known as re-identification attacks, focus on compromising the anonymity of individuals within datasets by linking anonymized or pseudonymized records to their true identities. Current research emphasizes the vulnerability of various anonymization techniques, including re-pseudonymization and the use of generative models like GANs, to sophisticated attacks leveraging deep learning architectures such as transformers, CNN-LSTMs, and Siamese neural networks. These attacks highlight significant privacy risks in diverse applications, from smart meter data and healthcare records to behavioral clickstream data, underscoring the need for robust privacy-preserving data sharing and synthetic data generation methods.