Case Study: User Identification for Mobile Privacy

To facilitate the novel mobile data-driven applications and services as discussed in previous chapters, mobile big data with spatiotemporal information may need to be released to third parties or even to the public. However, direct data publishing may lead to a significant subscriber’s privacy leakage risk (Cheng et al. IEEE Netw 31(1):72–79, 2017), immediately resulting in data availability issues. To protect subscribers’ privacy, the common practice is to anonymize the dataset by replacing subscribers’ identifiers (e.g., name, social security number, etc.) with randomly generated strings.