In this chapter, we propose SEmantic-aware Information-Theoretic Privacy (SEITP), a novel mechanism for online location trajectory sharing that protects both data and semantic privacy while maintaining high data utility. To achieve this, we define two privacy metrics, one for data privacy leakage and one for semantic privacy leakage, along with a semantic-aware utility metric. With those metrics, the shortcoming of failing to guarantee the data utility is avoided naturally through structuring a multi-objective optimization problem. We also provide theoretical proofs of SEITP’s privacy protection capabilities. Experimental evaluations using a real-world private vehicle trajectory dataset further demonstrate that SEITP outperforms existing mechanisms.