{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:46:50Z","timestamp":1776336410136,"version":"3.51.2"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,30]],"date-time":"2018-10-30T00:00:00Z","timestamp":1540857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1D1A1B03028097"],"award-info":[{"award-number":["NRF-2017R1D1A1B03028097"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mobile Crowdsensing (MCS) is a paradigm for collecting large-scale sensor data by leveraging mobile devices equipped with small and low-powered sensors. MCS has recently received considerable attention from diverse fields, because it can reduce the cost incurred in the process of collecting a large amount of sensor data. However, in the task assignment process in MCS, to allocate the requested tasks efficiently, the workers need to send their specific location to the requester, which can raise serious location privacy issues. In this paper, we focus on the methods for publishing differentially a private spatial histogram to guarantee the location privacy of the workers. The private spatial histogram is a sanitized spatial index where each node represents the sub-regions and contains the noisy counts of the objects in each sub-region. With the sanitized spatial histograms, it is possible to estimate approximately the number of workers in the arbitrary area, while preserving their location privacy. However, the existing methods have given little concern to the domain size of the input dataset, leading to the low estimation accuracy. This paper proposes a partitioning technique SAGA (Skew-Aware Grid pArtitioning) based on the hotspots, which is more appropriate to adjust the domain size of the dataset. Further, to optimize the overall errors, we lay a uniform grid in each hotspot. Experimental results on four real-world datasets show that our method provides an enhanced query accuracy compared to the existing methods.<\/jats:p>","DOI":"10.3390\/s18113696","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T03:50:56Z","timestamp":1540957856000},"page":"3696","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Differentially Private and Skew-Aware Spatial Decompositions for Mobile Crowdsensing"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3812-3802","authenticated-orcid":false,"given":"Jong Seon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yon Dohn","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sangmyung University, Seoul 03016, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MCOM.2011.6069707","article-title":"Mobile crowdsensing: current state and future challenges","volume":"49","author":"Ganti","year":"2011","journal-title":"IEEE Commun. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alvear, O., Calafate, C.T., Cano, J.C., and Manzoni, P. (2018). Crowdsensing in Smart Cities: Overview, Platforms, and Environment Sensing Issues. Sensors, 18.","DOI":"10.3390\/s18020460"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1126\/science.1256297","article-title":"Unique in the shopping mall: On the reidentifiability of credit card metadata","volume":"347","author":"Radaelli","year":"2015","journal-title":"Science"},{"key":"ref_4","unstructured":"Dwork, C. (2006, January 10\u201314). Differential privacy. Proceedings of the International Colloquium on Automata, Languages and Programming (ICALP), Venice, Italy."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1142\/S0218488502001648","article-title":"k-anonymity: A model for protecting privacy","volume":"10","author":"Sweeney","year":"2002","journal-title":"Int. J. Uncertain. Fuzziness Knowl.-Based Syst."},{"key":"ref_6","first-page":"1","article-title":"l-diversity: Privacy beyond k-anonymity","volume":"1","author":"Machanavajjhala","year":"2007","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, N., Li, T., and Venkatasubramanian, S. (2007, January 15\u201320). t-closeness: Privacy beyond k-anonymity and l-diversity. Proceedings of the IEEE International Conference on Data Engineering (ICDE), Istanbul, Turkey.","DOI":"10.1109\/ICDE.2007.367856"},{"key":"ref_8","unstructured":"Kifer, D. (July, January 29). Attacks on privacy and deFinetti\u2019s theorem. Proceedings of the ACM International Conference on Management of Data (SIGMOD), Providence, RI, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cormode, G., Procopiuc, C., Srivastava, D., Shen, E., and Yu, T. (2012, January 1\u20135). Differentially private spatial decompositions. Proceedings of the IEEE International Conference on Data Engineering (ICDE), Arlington, VA, USA.","DOI":"10.1109\/ICDE.2012.16"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Xiong, L., and Yuan, C. (2010, January 17). Differentially private data release through multidimensional partitioning. Proceedings of the Workshop on Secure Data Management (SDM), Singapore.","DOI":"10.1007\/978-3-642-15546-8_11"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qardaji, W., Yang, W., and Li, N. (2013, January 8\u201312). Differentially private grids for geospatial data. Proceedings of the IEEE International Conference on Data Engineering (ICDE), Brisbane, QLD, Australia.","DOI":"10.1109\/ICDE.2013.6544872"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"To, H., Fan, L., and Shahabi, C. (2015, January 3\u20136). Differentially private h-tree. Proceedings of the ACM SIGSPATIAL Workshop on Privacy in Geographic Information Collection and Analysis, GeoPrivacy 2015, Seattle, WA, USA.","DOI":"10.1145\/2830834.2830837"},{"key":"ref_13","unstructured":"Zhang, J., Xiao, X., and Xie, X. (July, January 26). Privtree: A differentially private algorithm for hierarchical decompositions. Proceedings of the ACM International Conference on Management of Data (SIGMOD), San Francisco, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhu, R., Liu, S., and Cai, Z. (2018). Node Location Privacy Protection Based on Differentially Private Grids in Industrial Wireless Sensor Networks. Sensors, 18.","DOI":"10.3390\/s18020410"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MCOM.2014.6871668","article-title":"4W1H in mobile crowd sensing","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Commun. Mag."},{"key":"ref_16","first-page":"73","article-title":"Spatial task assignment for crowd sensing with cloaked locations","volume":"Volume 1","author":"Pournajaf","year":"2014","journal-title":"Proceedings of the IEEE International Conference on Mobile Data Management (MDM)"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, D., Yang, D., Lim, B.Y., and Ma, X. (2016, January 12\u201315). Differential Location Privacy for Sparse Mobile Crowdsensing. Proceedings of the IEEE Internation Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0169"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, L., Yang, D., Han, X., Wang, T., Zhang, D., and Ma, X. (2017, January 3\u20137). Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation. Proceedings of the International Conference on World Wide Web (WWW), Perth, Australia.","DOI":"10.1145\/3038912.3052696"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1145\/2935694.2935700","article-title":"Participant privacy in mobile crowd sensing task management: A survey of methods and challenges","volume":"44","author":"Pournajaf","year":"2016","journal-title":"ACM SIGMOD Rec."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Abul, O., Bonchi, F., and Nanni, M. (2008, January 7\u201312). Never walk alone: Uncertainty for anonymity in moving objects databases. Proceedings of the IEEE International Conference on Data Engineering (ICDE), Cancun, Mexico.","DOI":"10.1109\/ICDE.2008.4497446"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, D., Fang, X., and Xue, G. (2013, January 14\u201319). Truthful incentive mechanisms for k-anonymity location privacy. Proceedings of the IEEE Conference on Computer Communications (INFOCOM), Turin, Italy.","DOI":"10.1109\/INFCOM.2013.6567111"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, K., Guo, L., Li, X., and Fang, Y. (2013, January 14\u201319). A game-theoretic approach for achieving k-anonymity in location based services. Proceedings of the IEEE INFOCOM, Turin, Italy.","DOI":"10.1109\/INFCOM.2013.6567110"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006, January 4\u20137). Calibrating noise to sensitivity in private data analysis. Proceedings of the Conference on Theory of Cryptography Conference (TCC), New York, NY, USA.","DOI":"10.1007\/11681878_14"},{"key":"ref_24","unstructured":"Dwork, C. (2008, January 25\u201329). Differential privacy: A survey of results. Proceedings of the International Conference on Theory and Applications of Models of Computation (TAMC), Xi\u2019an, China."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, R., Fung, B., and Desai, B.C. (arXiv, 2011). Differentially private trajectory data publication, arXiv.","DOI":"10.1145\/2339530.2339564"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, R., Acs, G., and Castelluccia, C. (2012, January 16\u201318). Differentially private sequential data publication via variable-length n-grams. Proceedings of the ACM Conference on Computer and Communications Security (CCS), Raleigh, NC, USA.","DOI":"10.1145\/2382196.2382263"},{"key":"ref_27","unstructured":"Hay, M., Machanavajjhala, A., Miklau, G., Chen, Y., and Zhang, D. (1, January 26). Principled evaluation of differentially private algorithms using dpbench. Proceedings of the ACM International Conference on Management of Data (SIGMOD), San Francisco, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"McSherry, F., and Talwar, K. (2007, January 20\u201323). Mechanism design via differential privacy. Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS), Providence, RI, USA.","DOI":"10.1109\/FOCS.2007.66"},{"key":"ref_29","unstructured":"McSherry, F. (July, January 29). Privacy integrated queries: an extensible platform for privacy-preserving data analysis. Proceedings of the ACM International Conference on Management of Data (SIGMOD), Providence, RI, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Roh, Y.J., Kim, J.H., Chung, Y.D., Son, J.H., and Kim, M.H. (2010, January 6\u201310). Hierarchically organized skew-tolerant histograms for geographic data objects. Proceedings of the ACM International Conference on Management of Data (SIGMOD), Indianapolis, IN, USA.","DOI":"10.1145\/1807167.1807236"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xiao, Y., and Xiong, L. (2015, January 12). Protecting locations with differential privacy under temporal correlations. Proceedings of the ACM Conference on Computer and Communications Security (CCS), Denver, CO, USA.","DOI":"10.1145\/2810103.2813640"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/18\/11\/3696\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:27:03Z","timestamp":1760196423000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/18\/11\/3696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,30]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113696"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/s18113696","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,30]]}}}