{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:13:24Z","timestamp":1760177604457,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed to prevent data re-identifcation and concerns us in the context of this work. Data modeling has also gained significant attention from the big data perspective. It is believed that the advancing distributed environments will provide users with several solutions for efficient spatio-temporal data management. GeoSpark will be utilized in the current work as it is a key solution that has been widely employed for spatial data. Specifically, it works on the top of Apache Spark, the main framework leveraged from the research community and organizations for big data transformation, processing and visualization. To this end, we focused on trajectory data representation so as to be applicable to the GeoSpark environment, and a GeoSpark-based approach is designed for the efficient management of real spatio-temporal data. Th next step is to gain deeper understanding of the data through the application of k nearest neighbor (k-NN) queries either using indexing methods or otherwise. The k-anonymity set computation, which is the main component for privacy preservation evaluation and the main issue of our previous works, is evaluated in the GeoSpark environment. More to the point, the focus here is on the time cost of k-anonymity set computation along with vulnerability measurement. The extracted results are presented into tables and figures for visual inspection.<\/jats:p>","DOI":"10.3390\/a13080182","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T10:16:49Z","timestamp":1595931409000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark"],"prefix":"10.3390","volume":"13","author":[{"given":"Elias","family":"Dritsas","sequence":"first","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 265 04 Patras, Greece"}]},{"given":"Andreas","family":"Kanavos","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 265 04 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7793-0407","authenticated-orcid":false,"given":"Maria","family":"Trigka","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 265 04 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9555-4775","authenticated-orcid":false,"given":"Gerasimos","family":"Vonitsanos","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 265 04 Patras, Greece"}]},{"given":"Spyros","family":"Sioutas","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 265 04 Patras, Greece"}]},{"given":"Athanasios","family":"Tsakalidis","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 265 04 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.3390\/a8041175","article-title":"A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments","volume":"8","author":"Jiang","year":"2015","journal-title":"Algorithms"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.cosrev.2015.05.002","article-title":"Understandable Big Data: A Survey","volume":"17","author":"Emani","year":"2015","journal-title":"Comput. Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.compenvurbsys.2016.10.010","article-title":"Utilizing Cloud Computing to Address Big Geospatial Data Challenges","volume":"61","author":"Yang","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.3390\/ijgi4042306","article-title":"Spatiotemporal Data Mining: A Computational Perspective","volume":"4","author":"Shekhar","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1007\/s00778-010-0185-7","article-title":"Efficient k-Nearest Neighbor Search on Moving Object Trajectories","volume":"19","author":"Behr","year":"2010","journal-title":"VLDB J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Huang, Y., Chen, Z., and Lee, C. (2009, January 2\u20134). Continuous K-Nearest Neighbor Query over Moving Objects in Road Networks. Proceedings of the Joint International Conferences on Advances in Data and Web Management (APWeb\/WAIM), Suzhou, China.","DOI":"10.1007\/978-3-642-00672-2_5"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/s11235-013-9795-x","article-title":"Continuous K-Nearest Neighbor Processing based on Speed and Direction of Moving Objects in a Road Network","volume":"55","author":"Fan","year":"2014","journal-title":"Telecommun. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zheng, B., Zheng, K., Xiao, X., Su, H., Yin, H., Zhou, X., and Li, G. (2016, January 16\u201320). Keyword-Aware Continuous kNN Query on Road Networks. Proceedings of the 32nd IEEE International Conference on Data Engineering (ICDE), Helsinki, Finland.","DOI":"10.1109\/ICDE.2016.7498297"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dritsas, E., Kanavos, A., Trigka, M., Sioutas, S., and Tsakalidis, A.K. (2019). Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases. Algorithms, 12.","DOI":"10.3390\/a12120266"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s10707-009-0081-8","article-title":"Efficient Evaluation of Continuous Spatio-temporal Queries on Moving Objects with Uncertain Velocity","volume":"14","author":"Huang","year":"2010","journal-title":"GeoInformatica"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.is.2011.08.002","article-title":"Vague Continuous K-Nearest Neighbor Queries over Moving Objects with Uncertain Velocity in Road Networks","volume":"37","author":"Fan","year":"2012","journal-title":"Inf. Syst."},{"key":"ref_12","first-page":"80","article-title":"Continuous Predictive Line Queries for On-the-Go Traffic Estimation","volume":"18","author":"Heendaliya","year":"2015","journal-title":"Trans. Large-Scale Data Knowl.-Cent. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s11280-018-0539-4","article-title":"Privacy Preserving K-Nearest Neighbor Classification over Encrypted Database in Outsourced Cloud Environments","volume":"22","author":"Wu","year":"2019","journal-title":"World Wide Web"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jpdc.2019.07.013","article-title":"Privacy-Preserving K-Nearest Neighbor Query with Authentication on Road Networks","volume":"134","author":"Yang","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_15","unstructured":"Hagedorn, S., G\u00f6tze, P., and Sattler, K. (2017, January 6\u201310). The STARK Framework for Spatio-Temporal Data Analytics on Spark. Proceedings of the 17th Conference on Database Systems for Business, Technology, and Web (BTW), Stuttgart, Germany."},{"key":"ref_16","unstructured":"Hagedorn, S., and R\u00e4th, T. (2017, January 21\u201324). Efficient Spatio-Temporal Event Processing with STARK. Proceedings of the 20th International Conference on Extending Database Technology (EDBT), Venice, Italy."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Jin, C., Mao, J., Yang, X., and Zhou, A. (2017, January 7\u20139). TrajSpark: A Scalable and Efficient In-Memory Management System for Big Trajectory Data. Proceedings of the 1st (APWeb-WAIM) International Joint Conference on Web and Big Data, Beijing, China.","DOI":"10.1007\/978-3-319-63579-8_2"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alarabi, L. (2018, January 6\u20139). Summit: A Scalable System for Massive Trajectory Data Management. Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL), Seattle, WA, USA.","DOI":"10.1145\/3274895.3282795"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Giannousis, K., Bereta, K., Karalis, N., and Koubarakis, M. (2018, January 10\u201313). Distributed Execution of Spatial SQL Queries. Proceedings of the IEEE International Conference on Big Data, Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8621908"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Patrou, M., Alam, M.M., Memarzia, P., Ray, S., Bhavsar, V.C., Kent, K.B., and Dueck, G.W. (2018, January 4\u20136). DISTIL: A Distributed In-Memory Data Processing System for Location-Based Services. Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA.","DOI":"10.1145\/3274895.3274961"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, C., Yu, X., and Liu, Y. (2014, January 14\u201317). Continuous KNN Join Processing for Real-Time Recommendation. Proceedings of the IEEE International Conference on Data Mining (ICDM), Shenzhen, China.","DOI":"10.1109\/ICDM.2014.20"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"582","DOI":"10.3390\/a7040582","article-title":"Processing KNN Queries in Grid-Based Sensor Networks","volume":"7","author":"Huang","year":"2014","journal-title":"Algorithms"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dong, T., Lulu, Y., Shang, Y., Ye, Y., and Zhang, L. (2019). Direction-Aware Continuous Moving K-Nearest-Neighbor Query in Road Networks. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8090379"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jnca.2015.01.004","article-title":"A Fast Privacy-Preserving Framework for Continuous Location-based Queries in Road Networks","volume":"53","author":"Wang","year":"2015","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_25","first-page":"6182769:1","article-title":"A Privacy-Preserving Location-Based System for Continuous Spatial Queries","volume":"2016","author":"Song","year":"2016","journal-title":"Mob. Inf. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.ins.2019.05.054","article-title":"A Trajectory Privacy-Preserving Scheme Based on Dual-K Mechanism for Continuous Location-Based Services","volume":"527","author":"Zhang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.14778\/2824032.2824057","article-title":"Spatial Partitioning Techniques in Spatial Hadoop","volume":"8","author":"Eldawy","year":"2015","journal-title":"Proc. VLDB Endow."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/TKDE.2014.2364046","article-title":"Scalable Distributed Processing of K Nearest Neighbor Queries over Moving Objects","volume":"27","author":"Yu","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_29","first-page":"723","article-title":"Improving Distance-Join Query processing with Voronoi-Diagram based partitioning in SpatialHadoop","volume":"111","author":"Corral","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dritsas, E., Trigka, M., Gerolymatos, P., and Sioutas, S. (2018). Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases. Algorithms, 11.","DOI":"10.3390\/a11120207"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10462-016-9477-7","article-title":"A Review of Moving Object Trajectory Clustering Algorithms","volume":"47","author":"Yuan","year":"2017","journal-title":"Artif. Intell. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Huang, Z., Chen, Y., Wan, L., and Peng, X. (2017). GeoSpark SQL: An Effective Framework Enabling Spatial Queries on Spark. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6090285"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10707-018-0330-9","article-title":"Spatial Data Management in Apache Spark: The GeoSpark Perspective and Beyond","volume":"23","author":"Yu","year":"2019","journal-title":"GeoInformatica"},{"key":"ref_34","first-page":"139","article-title":"kdANN+: A Rapid AkNN Classifier for Big Data","volume":"24","author":"Nodarakis","year":"2016","journal-title":"Trans. Large-Scale Data Knowl.-Cent. Syst."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/8\/182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:52:12Z","timestamp":1760176332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/8\/182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,28]]},"references-count":34,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["a13080182"],"URL":"https:\/\/doi.org\/10.3390\/a13080182","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2020,7,28]]}}}