{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:34:33Z","timestamp":1743028473812,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030373511"},{"type":"electronic","value":"9783030373528"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-37352-8_31","type":"book-chapter","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T20:03:00Z","timestamp":1577995380000},"page":"343-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Privacy Preserving Cross-Datasets Collaborative Outlier Detection"],"prefix":"10.1007","author":[{"given":"Zhaohui","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingqi","family":"Pei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuefeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lichuan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1145\/772862.772867","volume":"4","author":"J Vaidya","year":"2002","unstructured":"Vaidya, J., Clifton, C., Kantarcioglu, M.: Tools for privacy preserving distributed data mining. ACM SIGKDD Explor. Newsl. 4, 28\u201334 (2002)","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"31_CR2","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1109\/TKDE.2004.32","volume":"16","author":"C Clifton","year":"2004","unstructured":"Clifton, C., Kantarcioglu, M.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans. Knowl. Data Eng. 16, 1026\u20131037 (2004)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"doi-asserted-by":"crossref","unstructured":"Clifton, C., Vaidya, J.: Privacy preserving association rule mining in vertically partitioned data. In: 8th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (KDD02), pp. 639\u2013644 (2002)","key":"31_CR3","DOI":"10.1145\/775047.775142"},{"doi-asserted-by":"crossref","unstructured":"Wright, R., Jagannathan, G.: Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD05), pp 593\u2013599 (2005)","key":"31_CR4","DOI":"10.1145\/1081870.1081942"},{"unstructured":"Ghosh, J., Merugu, S.: Privacy-preserving distributed clustering using generative models. In: 3rd IEEE International Conference on Data Mining (ICDM03), pp. 211\u2013218 (2003)","key":"31_CR5"},{"doi-asserted-by":"crossref","unstructured":"Clifton, C., Vaidya, J.: Privacy-preserving k-means clustering over vertically partitioned data. In: 9th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), pp 206\u2013215 (2003)","key":"31_CR6","DOI":"10.1145\/956750.956776"},{"doi-asserted-by":"crossref","unstructured":"Srikant, R., Agrawal, R.: Privacy-preserving data mining. In: ACMSIGMOD International Conference on Management of Data (SIGMOD 2000), pp. 439\u2013450 (2000)","key":"31_CR7","DOI":"10.1145\/335191.335438"},{"doi-asserted-by":"crossref","unstructured":"Pinkas, B., Lindell, Y.: Privacy preserving data mining. In: 20th Annual International Cryptology Conference (CRYPTO 2000), pp. 36\u201354 (2000)","key":"31_CR8","DOI":"10.1007\/3-540-44598-6_3"},{"doi-asserted-by":"crossref","unstructured":"Clifton, C., Vaidya, J.: Privacy preserving naive Bayes classifier for vertically partitioned data. In: SIAM International Conference on Data Mining (SDM 2004), pp. 522\u2013526 (2004)","key":"31_CR9","DOI":"10.1137\/1.9781611972740.59"},{"unstructured":"Wang, S., Zhao, W., Zhang, N.: A new scheme on privacy-preserving data classification. In: 11th ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2005), pp. 374\u2013383 (2005)","key":"31_CR10"},{"key":"31_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6396-2","volume-title":"Outlier Analysis","author":"CC Aggarwal","year":"2013","unstructured":"Aggarwal, C.C.: Outlier Analysis. Springer-Verlag, New York (2013). \nhttps:\/\/doi.org\/10.1007\/978-1-4614-6396-2"},{"doi-asserted-by":"crossref","unstructured":"Rastogi, R., Shim, K., Ramaswame, S.: Efficient algorithms formining outliers from large data sets. In: ACMSIGMOD International Conference on Management of Data (SIGMOD 2000), pp. 427\u2013438 (2000)","key":"31_CR12","DOI":"10.1145\/335191.335437"},{"unstructured":"Atallah, M., Du, W.: Privacy-preserving cooperative statistical analysis. In: 17th Annual Computer Security Applications Conference (ACSAC 2001), pp. 102\u2013110 (2001)","key":"31_CR13"},{"key":"31_CR14","first-page":"39","volume":"42","author":"P Syverson","year":"1999","unstructured":"Syverson, P., Goldschlag, D., Reed, M.: Onion routing. Commun. ACM 42, 39\u201341 (1999)","journal-title":"Commun. ACM"},{"unstructured":"Vaidya, J., Clifton, C.: Privacy-preserving outlier detection. In: 4th IEEE International Conference on Data Mining, pp. 233\u2013240 (2004)","key":"31_CR15"},{"issue":"03","key":"31_CR16","doi-asserted-by":"publisher","first-page":"1850005","DOI":"10.1142\/S0218843018500053","volume":"27","author":"H Asif","year":"2018","unstructured":"Asif, H., Talukdar, T., Vaidya, J.: Differentially private outlier detection in a collaborative environment. Int. J. Coop. Inf. Syst. 27(03), 1850005 (2018)","journal-title":"Int. J. Coop. Inf. Syst."},{"doi-asserted-by":"crossref","unstructured":"Nikolaenko, V., Ioannidis, S., Weinsberg, U., Joye, M., Taft, N., Boneh, D.: Privacy-preserving matrix factorization. In: ACM SIGSAC Conference on Computer Communications Security, pp. 801\u2013812 (2013)","key":"31_CR17","DOI":"10.1145\/2508859.2516751"},{"doi-asserted-by":"crossref","unstructured":"Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: IEEE Symposium on Security and Privacy, pp. 334\u2013348 (2013)","key":"31_CR18","DOI":"10.1109\/SP.2013.30"},{"key":"31_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/978-3-642-01307-2_84","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"K Zhang","year":"2009","unstructured":"Zhang, K., Hutter, M., Jin, H.: A new local distance-based outlier detection approach for scattered real-world data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 813\u2013822. Springer, Heidelberg (2009). \nhttps:\/\/doi.org\/10.1007\/978-3-642-01307-2_84"},{"key":"31_CR20","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s00145-008-9036-8","volume":"22","author":"Y Lindell","year":"2009","unstructured":"Lindell, Y., Pinkas, B.: A proof of security of Yaos protocol for two-party computation. J. Cryptology 22, 161\u2013188 (2009)","journal-title":"J. Cryptology"},{"key":"31_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-3-540-45146-4_9","volume-title":"Advances in Cryptology - CRYPTO 2003","author":"Y Ishai","year":"2003","unstructured":"Ishai, Y., Kilian, J., Nissim, K., Petrank, E.: Extending oblivious transfers efficiently. In: Boneh, D. (ed.) CRYPTO 2003. LNCS, vol. 2729, pp. 145\u2013161. Springer, Heidelberg (2003). \nhttps:\/\/doi.org\/10.1007\/978-3-540-45146-4_9"},{"doi-asserted-by":"crossref","unstructured":"Schneider, T., Asharov, G., Lindell, Y., Zohner, M.: More efficient oblivious transfer and extensions for faster secure computation. In: ACM CCS 2013, pp. 535\u2013548 (2013)","key":"31_CR22","DOI":"10.1145\/2508859.2516738"},{"key":"31_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/978-3-642-32009-5_40","volume-title":"Advances in Cryptology \u2013 CRYPTO 2012","author":"JB Nielsen","year":"2012","unstructured":"Nielsen, J.B., Nordholt, P.S., Orlandi, C., Burra, S.S.: A new approach to practical active-secure two-party computation. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 681\u2013700. Springer, Heidelberg (2012). \nhttps:\/\/doi.org\/10.1007\/978-3-642-32009-5_40"},{"doi-asserted-by":"crossref","unstructured":"Demmler, D., Schneider, T., Zohner, M.: ABY-a framework for efficient mixed-protocol secure two-party computation. In: NDSS (2015)","key":"31_CR24","DOI":"10.14722\/ndss.2015.23113"},{"unstructured":"Nisan, N., Pinkas, B., Sella, Y., Malkhi, D., et al. : Fairplay a secure two-party computation system. In: 13th Conference on USENIX Security Symposium, vol. 13, pp. 20\u201320 (2004)","key":"31_CR25"},{"key":"31_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/3-540-46766-1_34","volume-title":"Advances in Cryptology \u2014 CRYPTO 1991","author":"D Beaver","year":"1992","unstructured":"Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 420\u2013432. Springer, Heidelberg (1992). \nhttps:\/\/doi.org\/10.1007\/3-540-46766-1_34"},{"unstructured":"Kamara, S., Mohassel, P., Raykova, M.: Outsourcing multiparty computation. IACR Cryptology ePrint Archive 272 (2011)","key":"31_CR27"},{"doi-asserted-by":"crossref","unstructured":"Canetti, R.: Universally composable security: a new paradigm for cryptographic protocols. In: 42nd IEEE Symposium on Foundations of Computer Science (FOCS 2001), pp. 136\u2013145 (2001)","key":"31_CR28","DOI":"10.1109\/SFCS.2001.959888"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Mohassel, P.: SecureML: a system for scalable privacy-preserving machine learning. In: IEEE Symposium on Security and Privacy (SP), pp. 19\u201338 (2017)","key":"31_CR29","DOI":"10.1109\/SP.2017.12"},{"unstructured":"GMP library. \nhttps:\/\/gmplib.org","key":"31_CR30"},{"unstructured":"NTL library. \nhttp:\/\/www.shoup.net\/ntl","key":"31_CR31"}],"container-title":["Lecture Notes in Computer Science","Cyberspace Safety and Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-37352-8_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T00:37:56Z","timestamp":1578011876000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-37352-8_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030373511","9783030373528"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-37352-8_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Cyberspace Safety and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"css2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/css2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"235","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":"61","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":"40","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":"26% - 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":"2.2","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":"4.5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}