{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:25:51Z","timestamp":1781367951481,"version":"3.54.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030871000","type":"print"},{"value":"9783030871017","type":"electronic"}],"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\/978-3-030-87101-7_4","type":"book-chapter","created":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T23:40:49Z","timestamp":1632094849000},"page":"31-41","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Privacy Preserving Machine Learning for\u00a0Malicious URL Detection"],"prefix":"10.1007","author":[{"given":"Imtiyazuddin","family":"Shaik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitesh","family":"Emmadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harshal","family":"Tupsamudre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harika","family":"Narumanchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajan Mindigal Alasingara","family":"Bhattachar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,20]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Verizon Business Ready: 2019 data breach investigation report by verizon (2018)","DOI":"10.1016\/S1361-3723(19)30060-0"},{"key":"4_CR2","unstructured":"Wombat Security: State of phish 2019 (2018)"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Gerbet, T., Kumar, A., Lauradoux, C.: A privacy analysis of google and yandex safe browsing. In: 2016 46th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 347\u2013358. IEEE (2016)","DOI":"10.1109\/DSN.2016.39"},{"key":"4_CR4","unstructured":"Sheng, S., Wardman, B., Warner, G., Cranor, L.F., Hong, J., Zhang, C.: An empirical analysis of phishing blacklists. In: Sixth Conference on Email and Anti-Spam (CEAS), California, USA (2009)"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Le, A., Markopoulou, A., Faloutsos, M.: Phishdef: URL names say it all. In: 2011 Proceedings IEEE INFOCOM, pp. 191\u2013195. IEEE (2011)","DOI":"10.1109\/INFCOM.2011.5934995"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Ma, J., Saul, L.K., Savage, S., Voelker, G.M.: Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1245\u20131254. ACM (2009)","DOI":"10.1145\/1557019.1557153"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hong, J.I., Cranor, L.F.: Cantina: a content-based approach to detecting phishing web sites. In: Proceedings of the 16th International Conference on World Wide Web, pp. 639\u2013648. ACM (2007)","DOI":"10.1145\/1242572.1242659"},{"key":"4_CR8","unstructured":"Apple\u2019s tencent privacy controversy is more complicated than it looks (2019). https:\/\/www.theverge.com\/2019\/10\/14\/20913680\/apple-tencent-privacy-controversy-safe-browsing-blacklist-explainer"},{"key":"4_CR9","unstructured":"Lopatka, M., Bird, S., Segall, S.: Replication: why we still can\u2019t browse in peace: on the uniqueness and reidentifiability of web browsing histories. In: USENIX (2020)"},{"key":"4_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/11681878_14","volume-title":"Theory of Cryptography","author":"C Dwork","year":"2006","unstructured":"Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265\u2013284. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11681878_14"},{"key":"4_CR11","unstructured":"How safe is apple\u2019s safe browsing? (2019). https:\/\/blog.cryptographyengineering.com\/2019\/10\/13\/dear-apple-safe-browsing-might-not-be-that-safe\/"},{"key":"4_CR12","unstructured":"Gentry, C., Boneh, D.: A fully homomorphic encryption scheme, vol. 20. Stanford University Stanford (2009)"},{"key":"4_CR13","unstructured":"Saxe, J., Berlin, K.: expose: a character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys. arXiv preprint arXiv:1702.08568 (2017)"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Y.-L., et al.: Poster: a PU learning based system for potential malicious URL detection. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 2599\u20132601. ACM (2017)","DOI":"10.1145\/3133956.3138825"},{"key":"4_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-030-20951-3_21","volume-title":"Cyber Security Cryptography and Machine Learning","author":"H Tupsamudre","year":"2019","unstructured":"Tupsamudre, H., Singh, A.K., Lodha, S.: Everything is in the name \u2013 A URL based approach for phishing detection. In: Dolev, S., Hendler, D., Lodha, S., Yung, M. (eds.) CSCML 2019. LNCS, vol. 11527, pp. 231\u2013248. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20951-3_21"},{"issue":"3","key":"4_CR16","first-page":"30","volume":"2","author":"J Ma","year":"2011","unstructured":"Ma, J., Saul, L.K., Savage, S., Voelker, G.M.: Learning to detect malicious URLs. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 30 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"4_CR17","unstructured":"Le, H., Pham, Q., Sahoo, D., Hoi, S.C.H.: URLNet: learning a URL representation with deep learning for malicious URL detection. arXiv preprint arXiv:1802.03162 (2018)"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Chou, E.J., Gururajan, A., Laine, K., Goel, N.K., Bertiger, A., Stokes, J.W.: Privacy-preserving phishing web page classification via fully homomorphic encryption. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2792\u20132796 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053729"},{"issue":"18","key":"4_CR19","doi-asserted-by":"publisher","first-page":"6266","DOI":"10.1002\/sec.1674","volume":"9","author":"G Varshney","year":"2016","unstructured":"Varshney, G., Misra, M., Atrey, P.K.: A survey and classification of web phishing detection schemes. Secur. Commun. Networks 9(18), 6266\u20136284 (2016)","journal-title":"Secur. Commun. Networks"},{"key":"4_CR20","unstructured":"Lauter, K.E.: Private AI: machine learning on encrypted data. Cryptology ePrint Archive, Report 2021\/324 (2021). https:\/\/eprint.iacr.org\/2021\/324"},{"key":"4_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/978-3-319-70694-8_15","volume-title":"Advances in Cryptology \u2013 ASIACRYPT 2017","author":"JH Cheon","year":"2017","unstructured":"Cheon, J.H., Kim, A., Kim, M., Song, Y.: Homomorphic encryption for arithmetic of approximate numbers. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 409\u2013437. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70694-8_15"},{"key":"4_CR22","unstructured":"HEEAN library (2017). https:\/\/github.com\/kimandrik\/HEAAN"},{"issue":"1","key":"4_CR23","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s10623-012-9720-4","volume":"71","author":"NP Smart","year":"2014","unstructured":"Smart, N.P., Vercauteren, F.: Fully homomorphic SIMD operations. Des. Codes Cryptography 71(1), 57\u201381 (2014)","journal-title":"Des. Codes Cryptography"},{"key":"4_CR24","first-page":"35","volume":"2017","author":"H Chabanne","year":"2017","unstructured":"Chabanne, H., de Wargny, A., Milgram, J., Morel, C., Prouff, E.: Privacy-preserving classification on deep neural network. IACR Cryptol. ePrint Arch. 2017, 35 (2017)","journal-title":"IACR Cryptol. ePrint Arch."},{"issue":"2","key":"4_CR25","doi-asserted-by":"publisher","first-page":"e19","DOI":"10.2196\/medinform.8805","volume":"6","author":"M Kim","year":"2018","unstructured":"Kim, M., Song, Y., Wang, S., Xia, Y., Jiang, X.: Secure logistic regression based on homomorphic encryption: design and evaluation. JMIR Med. Inform. 6(2), e19 (2018)","journal-title":"JMIR Med. Inform."}],"container-title":["Communications in Computer and Information Science","Database and Expert Systems Applications - DEXA 2021 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87101-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T22:03:23Z","timestamp":1649887403000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87101-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871000","9783030871017"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87101-7_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"20 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","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":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dexa.org\/dexa2021","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"149","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":"37","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":"31","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":"25% - 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":"4","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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"DEXA 2021 Workshops: 50 papers submitted, 23 papers accepted","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}