The study investigates the privacy implications of human mobility data, revealing that even coarse datasets can expose individual identities. Using 15 months of data from 1.5 million individuals, the research finds that four spatio-temporal points are sufficient to uniquely identify 95% of users. This highlights the high uniqueness of human mobility traces, which can be re-identified using minimal external information. The uniqueness decreases as the spatial and temporal resolution of the data is reduced, following a power function with an exponent that scales linearly with the number of points. This implies that even low-resolution datasets may not provide adequate anonymity. Privacy has historically been protected by informal mechanisms, but modern technologies like smartphones and mobile networks have increased the uniqueness of individual traces, making privacy more vulnerable. Mobility data, which includes approximate locations and movement patterns, can reveal sensitive information such as religious practices, medical records, and personal activities. The study shows that with just a few spatio-temporal points, individuals can be re-identified, even in anonymized datasets. The research also demonstrates that the uniqueness of mobility traces can be modeled using a formula that depends on resolution and the number of available points. This formula provides mathematical bounds for privacy in mobility data. The study emphasizes the importance of understanding these privacy limits to design effective frameworks for protecting individual privacy. The findings suggest that current anonymization methods may not be sufficient to protect privacy in the era of widespread mobility data collection. The results have significant implications for the development of policies and technologies aimed at safeguarding personal privacy.The study investigates the privacy implications of human mobility data, revealing that even coarse datasets can expose individual identities. Using 15 months of data from 1.5 million individuals, the research finds that four spatio-temporal points are sufficient to uniquely identify 95% of users. This highlights the high uniqueness of human mobility traces, which can be re-identified using minimal external information. The uniqueness decreases as the spatial and temporal resolution of the data is reduced, following a power function with an exponent that scales linearly with the number of points. This implies that even low-resolution datasets may not provide adequate anonymity. Privacy has historically been protected by informal mechanisms, but modern technologies like smartphones and mobile networks have increased the uniqueness of individual traces, making privacy more vulnerable. Mobility data, which includes approximate locations and movement patterns, can reveal sensitive information such as religious practices, medical records, and personal activities. The study shows that with just a few spatio-temporal points, individuals can be re-identified, even in anonymized datasets. The research also demonstrates that the uniqueness of mobility traces can be modeled using a formula that depends on resolution and the number of available points. This formula provides mathematical bounds for privacy in mobility data. The study emphasizes the importance of understanding these privacy limits to design effective frameworks for protecting individual privacy. The findings suggest that current anonymization methods may not be sufficient to protect privacy in the era of widespread mobility data collection. The results have significant implications for the development of policies and technologies aimed at safeguarding personal privacy.