# Releasing Differentially Private Trajectories with Optimized Data Utility
## 6 Conclusions and Future Work  
Releasing individual trajectories with high utility is a challenging task. On the one hand, lots of existing data analysis tasks heavily rely on both the geographical utility and semantic utility of the released trajectories. On the other hand, semantic utility could be used as side channels to conduct de-anonymization attacks and location inference attacks. Existing location privacy protection studies merely focus on the geographical utility and neglect the preservation of location semantics. As a result, not only did numerous data analysis tasks suffer poor performance, but individual identity and sensitive locations could also be disclosed. To remedy this problem, we propose a utility-preserving and differentially private mechanism for publishing trajectories (UDPT) with two novel features. First, it enables simultaneous preservation of both geographical and semantical utility by solving an optimization problem. Second, it provides a formal and provable privacy guarantee to thwart location inference attacks and de-anonymization attacks. To our best knowledge, it is the first work that ensures differential privacy and preserves both types of data utility. Extensive experiments in real-world datasets demonstrate UDPT's outperformance against two state-of-the-art competitors in terms of both data utility and privacy. Our findings will promote the sharing of big trajectory data and improve the performance of the data analysis applications, e.g., semantic annotation and trajectory prediction, which heavily rely on trajectory data geographical utility as well as the semantic utility. In the meantime, the findings will also shed more light on the study of location privacy protection. In the future, we plan to extend our work from the following aspects. (i) The trade-off between geographical utility and semantic utility is an open problem. In this work, the data curators can express their preference for either type of utility by the weight parameter \(\alpha\) in Equation (10) and then manually intervene the trade-off. Inspired by the automated model tuning and hyperparameter optimization in machine learning, in the future, we plan to develop an automated parameter tuning mechanism for finding an optimal trade-off between the two types of data utility. Similarly, the tuning of differential privacy budget is also an open problem, which is worth an in-depth study in the future. (ii) Recently, more cases of privacy disclosure, e.g., the Facebook-Cambridge Analytica data scandal in 2018, have suggested that data curators, such as location-based social networks, should take measures to protect individual privacy ([[https://en.wikipedia.org/wiki/Facebook/e22%80%/93](https://en.wikipedia.org/wiki/Facebook/e22%80%/93) Cambridge_Analytica_data_scandal]([https://en.wikipedia.org/wiki/Facebook/e22%80%/93](https://en.wikipedia.org/wiki/Facebook/e22%80%/93) Cambridge_Analytica_data_scandal), accessed on 16 February 2022). A potential direction is to develop privacy tools for helping the curator to comply privacy protection regulation when releasing individual data. For example, [PERSON] et al. recently proposed a privacy tool for helping companies to comply with the general data protection regulation [36]. In the future, we plan to extend our work to a privacy-protecting trajectory data releasing middleware between the curators and the analyzers. Conceptualization, B.L. ; methodology, B.L. and H.Z. ; software, B.L. and H.Z. ; validation, M.X. ; formal analysis, B.L. ; investigation, B.L. and H.Z. ; resources, H.Z. and M.X. ; data curation, H.Z. and M.X. ; writing--original draft preparation, B.L. ; writing--review and editing, H.Z. and M.X. ; visualization, B.L. ; supervision, H.Z. and M.X. ; project administration, H.Z. ; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant number 61772215) and the Wuhan Science and Technology Bureau (grant number 2018010401011274).