On the privacy protection of indoor location dataset using anonymization

•Serious privacy concerns when publishing indoor location datasets.•Preserving users’ privacy in indoor location datasets using anonymization.•Indoor location privacy via k-anonymity, ℓ-diversity, t-closeness, (α, k)-anonymity and δ- presence.•Resisting against at least five major attacks for indoor location fingerprints.