{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:32:34Z","timestamp":1760239954074,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032079008","type":"print"},{"value":"9783032079015","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:00:00Z","timestamp":1760227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:00:00Z","timestamp":1760227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-07901-5_14","type":"book-chapter","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:29:44Z","timestamp":1760185784000},"page":"274-293","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RIPOST: Two-Phase Private Decomposition for\u00a0Multidimensional Data"],"prefix":"10.1007","author":[{"given":"Ala Eddine","family":"Laouir","sequence":"first","affiliation":[]},{"given":"Abdessamad","family":"Imine","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,12]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Abowd, J.M.: The us census bureau adopts differential privacy. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2867\u20132867 (2018)","DOI":"10.1145\/3219819.3226070"},{"issue":"6","key":"14_CR2","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1109\/TKDE.2018.2855136","volume":"31","author":"G Acs","year":"2018","unstructured":"Acs, G., Melis, L., Castelluccia, C., De Cristofaro, E.: Differentially private mixture of generative neural networks. IEEE Trans. Knowl. Data Eng. 31(6), 1109\u20131121 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Cormode, G., Procopiuc, C., Srivastava, D., Shen, E., Yu, T.: Differentially private spatial decompositions. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 20\u201331. IEEE (2012)","DOI":"10.1109\/ICDE.2012.16"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Ding, B., Winslett, M., Han, J., Li, Z.: Differentially private data cubes: optimizing noise sources and consistency. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 217\u2013228 (2011)","DOI":"10.1145\/1989323.1989347"},{"key":"14_CR5","unstructured":"Dwork, C.: Differential privacy. In: Automata, Languages and Programming: 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part II 33, pp. 1\u201312. Springer (2006)"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Dwork, C., Roth, A., et\u00a0al.: The algorithmic foundations of differential privacy. Found. Trends\u00ae Theor. Comput. Sci. 9(3\u20134), 211\u2013407 (2014)","DOI":"10.1561\/0400000042"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Ebadi, H., Antignac, T., Sands, D.: Sampling and partitioning for differential privacy. In: 2016 14th Annual Conference on Privacy, Security and Trust (PST), pp. 664\u2013673. IEEE (2016)","DOI":"10.1109\/PST.2016.7906954"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Fan, J., Liu, T., Li, G., Chen, J., Shen, Y., Du, X.: Relational data synthesis using generative adversarial networks: a design space exploration. arXiv preprint arXiv:2008.12763 (2020)","DOI":"10.14778\/3407790.3407802"},{"key":"14_CR9","unstructured":"Harder, F., Adamczewski, K., Park, M.: DP-MERF: differentially private mean embeddings with random features for practical privacy-preserving data generation. In: International Conference on Artificial Intelligence And Statistics, pp. 1819\u20131827. PMLR (2021)"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Inan, A., Kantarcioglu, M., Ghinita, G., Bertino, E.: Private record matching using differential privacy. In: Proceedings of the 13th International Conference on Extending Database Technology, pp. 123\u2013134 (2010)","DOI":"10.1145\/1739041.1739059"},{"key":"14_CR11","unstructured":"Jordon, J., Yoon, J., Van Der\u00a0Schaar, M.: Pate-gan: generating synthetic data with differential privacy guarantees. In: International conference on learning representations (2019)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Kato, F., Takahashi, T., Takagi, S., Cao, Y., Liew, S.P., Yoshikawa, M.: Hdpview: differentially private materialized view for exploring high dimensional relational data. arXiv preprint arXiv:2203.06791 (2022)","DOI":"10.14778\/3538598.3538601"},{"issue":"11","key":"14_CR13","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.14778\/3342263.3342274","volume":"12","author":"I Kotsogiannis","year":"2019","unstructured":"Kotsogiannis, I., Tao, Y., He, X., Fanaeepour, M., Machanavajjhala, A., Hay, M., Miklau, G.: Privatesql: a differentially private SQL query engine. Proc. VLDB Endowment 12(11), 1371\u20131384 (2019)","journal-title":"Proc. VLDB Endowment"},{"key":"14_CR14","unstructured":"Krea\u010di\u0107, E., Nouri, N., Potluru, V.K., Balch, T., Veloso, M.: Differentially private synthetic data using kd-trees. In: Uncertainty in Artificial Intelligence, pp. 1143\u20131153. PMLR (2023)"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Laouir, A.E., Imine, A.: Slim-view: sampling and private publishing of multidimensional databases. In: Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy, pp. 391\u2013402 (2024)","DOI":"10.1145\/3626232.3653275"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Li, C., Hay, M., Miklau, G., Wang, Y.: A data-and workload-aware algorithm for range queries under differential privacy. arXiv preprint arXiv:1410.0265 (2014)","DOI":"10.14778\/2732269.2732271"},{"key":"14_CR17","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s00778-015-0398-x","volume":"24","author":"C Li","year":"2015","unstructured":"Li, C., Miklau, G., Hay, M., McGregor, A., Rastogi, V.: The matrix mechanism: optimizing linear counting queries under differential privacy. VLDB J. 24, 757\u2013781 (2015)","journal-title":"VLDB J."},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"McKenna, R., Miklau, G., Hay, M., Machanavajjhala, A.: Optimizing error of high-dimensional statistical queries under differential privacy. arXiv preprint arXiv:1808.03537 (2018)","DOI":"10.14778\/3231751.3231769"},{"issue":"4","key":"14_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3569087","volume":"9","author":"S Shaham","year":"2023","unstructured":"Shaham, S., Ghinita, G., Ahuja, R., Krumm, J., Shahabi, C.: HTF: homogeneous tree framework for differentially private release of large geospatial datasets with self-tuning structure height. ACM Trans. Spatial Algor. Syst. 9(4), 1\u201330 (2023)","journal-title":"ACM Trans. Spatial Algor. Syst."},{"key":"14_CR20","unstructured":"Srivastava, G.C.C.P.D., Shen, E., Yu, T.: Differentially private spatial decompositions"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Takagi, S., Takahashi, T., Cao, Y., Yoshikawa, M.: P3gm: private high-dimensional data release via privacy preserving phased generative model. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 169\u2013180. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00022"},{"key":"14_CR22","unstructured":"Team, A.: learning-with-privacy-at-scale. https:\/\/docs-assets.developer.apple.com\/ml-research\/papers\/learning-with-privacy-at-scale.pdf"},{"issue":"2","key":"14_CR23","first-page":"230","volume":"2020","author":"RJ Wilson","year":"2020","unstructured":"Wilson, R.J., Zhang, C.Y., Lam, W., Desfontaines, D., Simmons-Marengo, D., Gipson, B.: Differentially private SQL with bounded user contribution. Proc. Priv. Enhanc. Technol. 2020(2), 230\u2013250 (2020)","journal-title":"Proc. Priv. Enhanc. Technol."},{"issue":"12","key":"14_CR24","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1109\/TIFS.2017.2737966","volume":"12","author":"C Xu","year":"2017","unstructured":"Xu, C., Ren, J., Zhang, Y., Qin, Z., Ren, K.: DPPro: differentially private high-dimensional data release via random projection. IEEE Trans. Inf. Forensics Secur. 12(12), 3081\u20133093 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"14_CR25","unstructured":"Zeidler, E., Hackbusch, W., Schwarz, H., Hunt, B.: Oxford Users\u2019 Guide to Mathematics. OUP Oxford (2004)"},{"issue":"4","key":"14_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3134428","volume":"42","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Cormode, G., Procopiuc, C.M., Srivastava, D., Xiao, X.: Privbayes: private data release via Bayesian networks. ACM Trans. Database Syst. (TODS) 42(4), 1\u201341 (2017)","journal-title":"ACM Trans. Database Syst. (TODS)"},{"key":"14_CR27","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 (2016)","DOI":"10.1145\/2882903.2882928"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, R., Xu, J., Meng, X., Xie, Y.: Towards accurate histogram publication under differential privacy. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 587\u2013595. SIAM (2014)","DOI":"10.1137\/1.9781611973440.68"},{"key":"14_CR29","unstructured":"Zhang, Z., et al.: Privsyn: differentially private data synthesis (2021)"}],"container-title":["Lecture Notes in Computer Science","Computer Security \u2013 ESORICS 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07901-5_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:29:50Z","timestamp":1760185790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07901-5_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,12]]},"ISBN":["9783032079008","9783032079015"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07901-5_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,12]]},"assertion":[{"value":"12 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESORICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Symposium on Research in Computer Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toulouse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esorics2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.esorics2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}