{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:55:58Z","timestamp":1743029758988,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789813349216"},{"type":"electronic","value":"9789813349223"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"vor","delay-in-days":384,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, with the development of mobile terminals, geographic location has attracted the attention of many researchers because of its convenience in collection and its ability to reflect user profile. To protect user privacy, researchers have adopted local differential privacy in data collection process. However, most existing methods assume that location has already been discretized, which we found, if not done carefully, may introduces huge noise, lowering collected result utility. Thus in this paper, we design a differentially private location division module that could automatically discretize locations according to access density of each region. However, as the size of discretized regions may be large, if directly applying existing local differential privacy based attribute method, the overall utility of collected results may be completely destroyed. Thus, we further improve the optimized binary local hash method, based on personalized differential privacy, to collect user visit frequency of each discretized region. This solution improve the accuracy of the collected results while satisfying the privacy of the user\u2019s geographic location. Through experiments on synthetic and real data sets, this paper proves that the proposed method achieves higher accuracy than the best known method under the same privacy budget.<\/jats:p>","DOI":"10.1007\/978-981-33-4922-3_13","type":"book-chapter","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T11:21:04Z","timestamp":1610968864000},"page":"175-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perosonalized Differentially Private Location Collection Method with Adaptive GPS Discretization"],"prefix":"10.1007","author":[{"given":"Huichuan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yong","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Jiale","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhihong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jianfeng","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Xiaoyan","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,19]]},"reference":[{"key":"13_CR1","unstructured":"Fawaz, K., Feng, H., Shin, K.G.: Anatomization and protection of mobile apps\u2019 location privacy threats. In: 24th USENIX Security Symposium, pp. 753\u2013768. USENIX (2015)"},{"key":"13_CR2","unstructured":"Chen, R., Fung, B., Desai, B.C.: Differentially private trajectory data publication. arXiv preprint arXiv:1112.2020 (2011)"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Chen, R., Acs, G., Castelluccia, C.: Differentially private sequential data publication via variable-length n-grams. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 638\u2013649. ACM (2012)","DOI":"10.1145\/2382196.2382263"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xiao, X., Xie, X.: PrivTree: a differentially private algorithm for hierarchical decompositions. In: Proceedings of the 2016 International Conference on Management of Data, pp. 155\u2013170. ACM (2016)","DOI":"10.1145\/2882903.2882928"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"He, X., Cormode, G., Machanavajjhala, A., Procopiuc, C.M., Srivastava, D.: DPT: differentially private trajectory synthesis using hierarchical reference systems. In: Proceedings of the VLDB Endowment, pp. 1154\u20131165. Springer (2015)","DOI":"10.14778\/2809974.2809978"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Gursoy, M.E., Liu, L., Truex, S., Yu, L., Wei, W.: Utility-aware synthesis of differentially private and attack-resilient location traces. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 196\u2013211. ACM (2018)","DOI":"10.1145\/3243734.3243741"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Erlingsson, \u00da., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1054\u20131067. ACM (2014)","DOI":"10.1145\/2660267.2660348"},{"issue":"3","key":"13_CR8","first-page":"41","volume":"2016","author":"G Fanti","year":"2016","unstructured":"Fanti, G., Pihur, V., Erlingsson, \u00da.: Building a rappor with the unknown: privacy-preserving learning of associations and data dictionaries. Proc. Priv. Enhanc. Technol. 2016(3), 41\u201361 (2016)","journal-title":"Proc. Priv. Enhanc. Technol."},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Bassily, R., Smith, A.: Local, private, efficient protocols for succinct histograms. In: Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing, pp. 127\u2013135. ACM (2015)","DOI":"10.1145\/2746539.2746632"},{"key":"13_CR10","unstructured":"Wang, T., Blocki, J., Li, N., Jha, S.: Locally differentially private protocols for frequency estimation. In: 26th USENIX Security Symposium, pp. 729\u2013745. USENIX (2017)"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Wang, N., et al.: PrivTrie: effective frequent term discovery under local differential privacy. In: 2018 IEEE 34th International Conference on Data Engineering, pp. 821\u2013832. IEEE (2018)","DOI":"10.1109\/ICDE.2018.00079"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Chen, R., Li, H., Qin, A.K., Kasiviswanathan, S.P., Jin, H.: Private spatial data aggregation in the local setting. In: 2016 IEEE 32nd International Conference on Data Engineering, pp. 289\u2013300. IEEE (2016)","DOI":"10.1109\/ICDE.2016.7498248"},{"issue":"309","key":"13_CR13","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1080\/01621459.1965.10480775","volume":"60","author":"SL Warner","year":"1965","unstructured":"Warner, S.L.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63\u201369 (1965)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"13_CR14","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1145\/356924.356930","volume":"16","author":"H Samet","year":"1984","unstructured":"Samet, H.: The quadtree and related hierarchical data structures. ACM Comput. Surv. 16(2), 187\u2013260 (1984)","journal-title":"ACM Comput. Surv."},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Ho, S.S., Ruan, S.: Differential privacy for location pattern mining. In: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp. 17\u201324. ACM (2011)","DOI":"10.1145\/2071880.2071884"},{"key":"13_CR16","unstructured":"Brinkhoff, T.: Generating network-based moving objects. In: Proceedings of the 12th International Conference on Scientific and Statistical Database Management, pp. 253\u2013255. IEEE (2000)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Agarwal, P.K., Fox, K., Munagala, K., Nath, A., Pan, J., Taylor, E.: Subtrajectory clustering: models and algorithms. In: Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 75\u201387, May 2018","DOI":"10.1145\/3196959.3196972"},{"issue":"9","key":"13_CR18","doi-asserted-by":"publisher","first-page":"948","DOI":"10.14778\/3329772.3329773","volume":"12","author":"F Orakzai","year":"2019","unstructured":"Orakzai, F., Calders, T., Pedersen, T.B.: k\/2-hop: fast mining of convoy patterns with effective pruning. Proc. VLDB Endow. 12(9), 948\u2013960 (2019)","journal-title":"Proc. VLDB Endow."}],"container-title":["Communications in Computer and Information Science","Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-33-4922-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T11:46:45Z","timestamp":1610970405000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-33-4922-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789813349216","9789813349223"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-33-4922-3_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CNCERT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China Cyber Security Annual Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncert2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/conf.cert.org.cn","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}