Abstract
Contact tracing is one of the most effective ways of disease control during a pandemic. A typical method for contact tracing is to examine the spatio-temporal companion between the trajectories of patients and others. However, human trajectory data collected by mobile devices cannot be directly shared due to privacy. To utilize personal trajectory data in contact tracing, this paper presents a federated trajectory search engine called Fetra, which can efficiently process top-k search over a data federation composed of numerous mobile devices without uploading raw trajectories. To achieve this, we first propose a lightweight similarity measure LCTS based on spatio-temporal companion time to evaluate the similarity between trajectories. We then build a federated grid index named FGI via location anonymization. Given a query, a pruning strategy over FGI is applied to prune the candidate mobile devices dynamically. In addition, we propose a local optimization strategy to accelerate similarity computations in mobile devices. Extensive experiments on real-world dataset verify the effectiveness of LCTS and the efficiency of Fetra.
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References
Foursquare dataset (2014). https://www.kaggle.com/datasets/chetanism/foursquare-nyc-and-tokyo-checkin-dataset
Spatio-temporal companion (2021). https://www.bloomberg.com/news/articles/2021-11-08/people-you-don-t-know-and-can-t-see-are-close-contacts-in-china
Amap (2022). https://www.amap.com/
GPS accuracy (2022). https://www.gps.gov/systems/gps/performance/accuracy/
Chen, C., Ding, Y., Wang, Z., Zhao, J., Guo, B., Zhang, D.: VTracer: when online vehicle trajectory compression meets mobile edge computing. IEEE Syst. J. 14(2), 1635–1646 (2020)
Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD, pp. 491–502 (2005)
Chow, C.Y., Mokbel, M.F., Aref, W.G.: Casper*: query processing for location services without compromising privacy. TODS 20, 1–45 (2010)
Christodoulou, G., Bouros, P., Mamoulis, N.: Hint: a hierarchical index for intervals in main memory, pp. 1257–1270, June 2022
Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: VLDB, pp. 426–435 (1997)
Ding, X., Chen, L., Gao, Y., Jensen, C.S., Bao, H.: UlTraMan: a unified platform for big trajectory data management and analytics. PVLDB 11(7), 787–799 (2018)
Hu, Y., et al.: Salon: a universal stay point-based location analysis platform. In: SIGSPATIAL, pp. 407–410, November 2021
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
Kato, F., Cao, Y., Yoshikawa, M.: PCT-TEE: trajectory-based private contact tracing system with trusted execution environment. ACM Trans. Spatial Algorithms Syst. 8(2), 1–35 (2022)
Lemire, D., Boytsov, L.: Decoding billions of integers per second through vectorization. Softw. Practice Exp. 45(1), 1–29 (2015)
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: SIGSPATIAL, pp. 1–10 (2008)
Li, R., et al.: TrajMesa: a distributed NoSQL-based trajectory data management system. IEEE Trans. Knowl. Data Eng. 14(8), 1013–1027 (2021)
Nutanong, S., Jacox, E.H., Samet, H.: An incremental Hausdorff distance calculation algorithm. PVLDB 4(8), 506–517 (2011)
Pérez-Torres, R., Torres-Huitzil, C., Galeana-Zapién, H.: Full on-device stay points detection in smartphones for location-based mobile applications. Sensors 16(10), 1693 (2016)
Schmuck, M., Bazant, M.Z.: Computing the discrete Fréchet distance in subquadratic time. SIAM J. Comput. 75(3), 1369–1401 (2015)
Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. PVLDB 10(11), 1178–1189 (2017)
Shang, Z., Li, G., Bao, Z.: DITA: distributed in-memory trajectory analytics. In: SIGMOD, pp. 725–740 (2018)
Shi, Y., Tong, Y., Zeng, Y., Zhou, Z., Ding, B., Chen, L.: Efficient approximate range aggregation over large-scale spatial data federation. IEEE Trans. Knowl. Data Eng. 35(1), 418–430 (2021)
Su, H., Liu, S., Zheng, B., Zhou, X., Zheng, K.: A survey of trajectory distance measures and performance evaluation. VLDB J. 29, 3–32 (2020)
Tong, Y., et al.: Hu-Fu: efficient and secure spatial queries over data federation. PVLDB 15(6), 1159–1172 (2022)
Vlachos, M., Gunopulos, D., Kollios, G.: Robust similarity measures for mobile object trajectories. In: ICDE, pp. 721–726 (2002)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: ICDE, pp. 673–684 (2002)
Wang, H., Li, Y., Gao, C., Wang, G., Tao, X., Jin, D.: Anonymization and de-anonymization of mobility trajectories: dissecting the gaps between theory and practice. IEEE Trans. Mob. Comput. 20(3), 796–815 (2021)
Wang, S., Bao, Z., Culpepper, J.S., Sellis, T., Qin, X.: Fast large-scale trajectory clustering. PVLDB 13(1), 29–42 (2019)
Wang, S., Bao, Z., Culpepper, J.S., Xie, Z., Liu, Q., Qin, X.: Torch: a search engine for trajectory data. In: SIGIR, pp. 535–544 (2018)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: WWW, pp. 791–800 (2009)
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Wu, C., Peng, Z. (2024). Federated Trajectory Search via a Lightweight Similarity Computation Framework. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_32
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DOI: https://doi.org/10.1007/978-981-97-2390-4_32
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