Deanonymizing mobility traces: using social network as a side-channel

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Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, methodology): “Social and behavioral dependence have also been used to perform de-anonymization attacks on released datasets [22], [32], [33], [37].”
Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “online social networks) are used to re-identify users [73–75].”
On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge (2015, methodology): “…our quantification can be applied to real world social networks to evaluate their perfect or partial de-anonymizability, i.e., our quantification can quantitatively demonstrate the vulnerability of real world social networks to existing structure based de-anonymization attacks (e.g., [1][2][3]).”
Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs (2020, background): “That kind of location trace is often sufficient to deanonymize personal identities [23].”
Structural Data De-anonymization: Quantification, Practice, and Implications (2014, methodology): “However, the attacks presented in [3] are only suitable for small datasets due to the computational infeasibility of finding a proper landmark mappings for large datasets.”