{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:16:52Z","timestamp":1742955412398,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030926342"},{"type":"electronic","value":"9783030926359"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92635-9_23","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T13:02:45Z","timestamp":1641042165000},"page":"389-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["$$\\mathbb {PSG}$$: Local Privacy Preserving Synthetic Social Graph Generation"],"prefix":"10.1007","author":[{"given":"Hongyu","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yantao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"issue":"5","key":"23_CR1","doi-asserted-by":"publisher","first-page":"056109","DOI":"10.1103\/PhysRevE.85.056109","volume":"85","author":"C Seshadhri","year":"2012","unstructured":"Seshadhri, C., Kolda, T.G., Pinar, A.: Community structure and scale-free collections of erd\u0151s-r\u00e9nyi graphs. Phys. Rev. E 85(5), 056109 (2012)","journal-title":"Phys. Rev. E"},{"key":"23_CR2","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/978-3-030-00916-8_53","volume-title":"Collaborative Computing: Networking, Applications and Worksharing","author":"L Chen","year":"2018","unstructured":"Chen, L., et al.: A privacy settings prediction model for textual posts on social networks. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds.) CollaborateCom 2017. LNICST, vol. 252, pp. 578\u2013588. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00916-8_53"},{"issue":"05","key":"23_CR3","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1142\/S0218488502001648","volume":"10","author":"L Sweeney","year":"2002","unstructured":"Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(05), 557\u2013570 (2002)","journal-title":"Int. J. Uncertain. Fuzziness Knowl. Based Syst."},{"issue":"1","key":"23_CR4","doi-asserted-by":"publisher","first-page":"3-es","DOI":"10.1145\/1217299.1217302","volume":"1","author":"A Machanavajjhala","year":"2007","unstructured":"Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 3-es (2007)","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"23_CR5","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":"23_CR6","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Bao, Z., Jin, H.: Privacy-preserving triangle counting in large graphs. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1283\u20131292 (2018)","DOI":"10.1145\/3269206.3271736"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Sun, H., et al.: Analyzing subgraph statistics from extended local views with decentralized differential privacy. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 703\u2013717 (2019)","DOI":"10.1145\/3319535.3354253"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Erd\u00f6s, P., R\u00e9nyi, A.: On the evolution of random graphs. In: The Structure and Dynamics of Networks, pp. 38\u201382. Princeton University Press (2011)","DOI":"10.1515\/9781400841356.38"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Aiello, W., Chung, F., Lu, L.: A random graph model for massive graphs. In: Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing, pp. 171\u2013180 (2000)","DOI":"10.1145\/335305.335326"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yu, T., Yang, Y., Khalil, I., Xiao, X., Ren, K.: Generating synthetic decentralized social graphs with local differential privacy. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 425\u2013438 (2017)","DOI":"10.1145\/3133956.3134086"},{"key":"23_CR11","doi-asserted-by":"publisher","first-page":"164304","DOI":"10.1109\/ACCESS.2019.2952452","volume":"7","author":"H Zhu","year":"2019","unstructured":"Zhu, H., Zuo, X., Xie, M.: DP-FT: a differential privacy graph generation with field theory for social network data release. IEEE Access 7, 164304\u2013164319 (2019)","journal-title":"IEEE Access"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Zhu, T., Li, G., Zhou, W., Yu, P.S.: Preliminary of differential privacy. In: Differential Privacy and Applications. Advances in Information Security, vol. 69, pp. 7\u201316. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-62004-6","DOI":"10.1007\/978-3-319-62004-6"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Day, W.Y., Li, N., Lyu, M.: Publishing graph degree distribution with node differential privacy. In: Proceedings of the 2016 International Conference on Management of Data, pp. 123\u2013138 (2016)","DOI":"10.1145\/2882903.2926745"},{"key":"23_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-642-33627-0_21","volume-title":"Privacy in Statistical Databases","author":"V Karwa","year":"2012","unstructured":"Karwa, V., Slavkovi\u0107, A.B.: Differentially private graphical degree sequences and synthetic graphs. In: Domingo-Ferrer, J., Tinnirello, I. (eds.) PSD 2012. LNCS, vol. 7556, pp. 273\u2013285. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33627-0_21"},{"key":"23_CR15","unstructured":"Wang, T., Blocki, J., Li, N., Jha, S.: Locally differentially private protocols for frequency estimation. In: 26th USENIX Security Symposium (USENIX Security 2017), pp. 729\u2013745 (2017)"},{"key":"23_CR16","unstructured":"Leskovec, J., Krevl, A.: Snap datasets: Stanford large network dataset collection (2014)"},{"issue":"2","key":"23_CR17","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/BF02289146","volume":"14","author":"RD Luce","year":"1949","unstructured":"Luce, R.D., Perry, A.D.: A method of matrix analysis of group structure. Psychometrika 14(2), 95\u2013116 (1949). https:\/\/doi.org\/10.1007\/BF02289146","journal-title":"Psychometrika"},{"key":"23_CR18","volume-title":"Becoming Metric-Wise. A Bibliometric Guide for Researchers","author":"R Rousseau","year":"2018","unstructured":"Rousseau, R., Egghe, L., Guns, R.: Becoming Metric-Wise. A Bibliometric Guide for Researchers. Chandos-Elsevier, Kidlington (2018)"},{"issue":"336","key":"23_CR19","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846\u2013850 (1971)","journal-title":"J. Am. Stat. Assoc."},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Pinar, A., Seshadhri, C., Kolda, T.G.: The similarity between stochastic Kronecker and Chung-Lu graph models. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 1071\u20131082. SIAM (2012)","DOI":"10.1137\/1.9781611972825.92"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92635-9_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T13:06:02Z","timestamp":1641042362000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92635-9_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030926342","9783030926359"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92635-9_23","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2021","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":"colcom2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/collaboratecom.eai-conferences.org\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"206","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}