{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T22:03:03Z","timestamp":1793052183588,"version":"4.5.0"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"8","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p>Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privacy concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios due to poor utility, dependence on external knowledge, high computational overhead, and vulnerability to attacks. To address these limitations, we introduce LDPTrace, a novel locally differentially private trajectory synthesis framework. Our framework takes into account three crucial patterns inferred from users' trajectories in the local setting, allowing us to synthesize trajectories that closely resemble real ones with minimal computational cost. Additionally, we present a new method for selecting a proper grid granularity without compromising privacy. Our extensive experiments using real-world as well as synthetic data, various utility metrics and attacks, demonstrate the efficacy and efficiency of LDPTrace.<\/jats:p>","DOI":"10.14778\/3594512.3594520","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T20:28:36Z","timestamp":1687465716000},"page":"1897-1909","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":71,"title":["LDPTrace: Locally Differentially Private Trajectory Synthesis"],"prefix":"10.14778","volume":"16","author":[{"given":"Yuntao","family":"Du","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujia","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhikun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziquan","family":"Fang","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baihua","family":"Zheng","sequence":"additional","affiliation":[{"name":"Singapore Management University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Gergely Acs and Claude Castelluccia. 2014. A Case Study: Privacy Preserving Release of Spatio-Temporal Density in Paris. In KDD. 1679--1688. Gergely Acs and Claude Castelluccia. 2014. A Case Study: Privacy Preserving Release of Spatio-Temporal Density in Paris. In KDD. 1679--1688."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508859.2516735"},{"key":"e_1_2_1_3_1","unstructured":"Raef Bassily Kobbi Nissim Uri Stemmer and Abhradeep Thakurta. 2017. Practical Locally Private Heavy Hitters. In NeurIPS. 2285--2293. Raef Bassily Kobbi Nissim Uri Stemmer and Abhradeep Thakurta. 2017. Practical Locally Private Heavy Hitters. In NeurIPS. 2285--2293."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Vincent Bindschaedler and Reza Shokri. 2016. Synthesizing Plausible Privacy-Preserving Location Traces. In S&P. 546--563. Vincent Bindschaedler and Reza Shokri. 2016. Synthesizing Plausible Privacy-Preserving Location Traces. 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Differentially private transit data publication: a case study on the montreal transportation system. In KDD. 213--221."},{"key":"e_1_2_1_10_1","first-page":"4","article-title":"Correlated Network Data Publication via Differential Privacy","volume":"23","author":"Chen Rui","year":"2014","unstructured":"Rui Chen , Benjamin C. Fung , Philip S. Yu , and Bipin C. Desai . 2014 . Correlated Network Data Publication via Differential Privacy . The VLDB Journal 23 , 4 (aug 2014), 653--676. Rui Chen, Benjamin C. Fung, Philip S. Yu, and Bipin C. Desai. 2014. Correlated Network Data Publication via Differential Privacy. The VLDB Journal 23, 4 (aug 2014), 653--676.","journal-title":"The VLDB Journal"},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Graham Cormode Somesh Jha Tejas Kulkarni Ninghui Li Divesh Srivastava and Tianhao Wang. 2018. Privacy at scale: Local differential privacy in practice. In SIGMOD. 1655--1658. 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Towards practical differential privacy for SQL queries. In PVLDB. 526--539.","DOI":"10.1145\/3187009.3177733"},{"key":"e_1_2_1_30_1","unstructured":"Haoran Li Li Xiong and Xiaoqian Jiang. 2014. Differentially private synthesization of multi-dimensional data using copula functions. In EDBT. 475. Haoran Li Li Xiong and Xiaoqian Jiang. 2014. Differentially private synthesization of multi-dimensional data using copula functions. In EDBT. 475."},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Ninghui Li Wahbeh Qardaji Dong Su and Jianneng Cao. 2012. PrivBasis: Frequent Itemset Mining with Differential Privacy. In PVLDB. 1340--1351. Ninghui Li Wahbeh Qardaji Dong Su and Jianneng Cao. 2012. PrivBasis: Frequent Itemset Mining with Differential Privacy. In PVLDB. 1340--1351.","DOI":"10.14778\/2350229.2350251"},{"key":"e_1_2_1_32_1","volume-title":"Membership Privacy: A Unifying Framework for Privacy Definitions. 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