{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:35:03Z","timestamp":1742985303699,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819958337"},{"type":"electronic","value":"9789819958344"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-5834-4_25","type":"book-chapter","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T06:02:34Z","timestamp":1693807354000},"page":"307-321","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lightweight and\u00a0Efficient Privacy-Preserving Multimodal Representation Inference via\u00a0Fully Homomorphic Encryption"],"prefix":"10.1007","author":[{"given":"Zhaojue","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2085-4359","authenticated-orcid":false,"given":"Yingpeng","family":"Sang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinru","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"25_CR1","unstructured":"Albrecht, M.R., et al.: Homomorphic encryption standard. IACR Cryptol. ePrint Arch., 939 (2019). https:\/\/eprint.iacr.org\/2019\/939"},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"226544","DOI":"10.1109\/ACCESS.2020.3045465","volume":"8","author":"AA Badawi","year":"2020","unstructured":"Badawi, A.A., Hoang, L., Mun, C.F., Laine, K., Aung, K.M.M.: PrivFT: private and fast text classification with homomorphic encryption. IEEE Access 8, 226544\u2013226556 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3045465","journal-title":"IEEE Access"},{"issue":"3","key":"25_CR3","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1109\/TETC.2020.3014636","volume":"9","author":"AA Badawi","year":"2021","unstructured":"Badawi, A.A., et al.: Towards the AlexNet moment for homomorphic encryption: HCNN, the first homomorphic CNN on encrypted data with GPUs. IEEE Trans. Emerg. Top. Comput. 9(3), 1330\u20131343 (2021). https:\/\/doi.org\/10.1109\/TETC.2020.3014636","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"issue":"2","key":"25_CR4","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltrusaitis","year":"2019","unstructured":"Baltrusaitis, T., Ahuja, C., Morency, L.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423\u2013443 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2018.2798607","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR5","unstructured":"Benaissa, A., Retiat, B., Cebere, B., Belfedhal, A.E.: TenSEAL: a library for encrypted tensor operations using homomorphic encryption. CoRR abs\/2104.03152 (2021). https:\/\/arxiv.org\/abs\/2104.03152"},{"issue":"8","key":"25_CR6","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A.C., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013). https:\/\/doi.org\/10.1109\/TPAMI.2013.50","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"25_CR7","doi-asserted-by":"publisher","first-page":"13:1","DOI":"10.1145\/2633600","volume":"6","author":"Z Brakerski","year":"2014","unstructured":"Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrapping. ACM Trans. Comput. Theor. 6(3), 13:1-13:36 (2014). https:\/\/doi.org\/10.1145\/2633600","journal-title":"ACM Trans. Comput. Theor."},{"key":"25_CR8","unstructured":"Brutzkus, A., Gilad-Bachrach, R., Elisha, O.: Low latency privacy preserving inference. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9\u201315 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol. 97, pp. 812\u2013821. PMLR (2019). http:\/\/proceedings.mlr.press\/v97\/brutzkus19a.html"},{"issue":"1","key":"25_CR9","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.1080\/09540091.2022.2111406","volume":"34","author":"C Cai","year":"2022","unstructured":"Cai, C., Sang, Y., Tian, H.: A multimodal differential privacy framework based on fusion representation learning. Connect. Sci. 34(1), 2219\u20132239 (2022). https:\/\/doi.org\/10.1080\/09540091.2022.2111406","journal-title":"Connect. Sci."},{"key":"25_CR10","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":"25_CR11","doi-asserted-by":"publisher","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: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, Barcelona, Spain, 4\u20138 May 2020, pp. 2792\u20132796. IEEE (2020). https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053729","DOI":"10.1109\/ICASSP40776.2020.9053729"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Deldjoo, Y., Schedl, M., Hidasi, B., Wei, Y., He, X.: Multimedia recommender systems: algorithms and challenges, 3rd edn. In: Recommender Systems Handbook (2020)","DOI":"10.1007\/978-1-0716-2197-4_25"},{"key":"25_CR13","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Volume 1 (Long and Short Papers), Minneapolis, MN, USA, 2\u20137 June 2019, pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"25_CR14","unstructured":"Fan, J., Vercauteren, F.: Somewhat practical fully homomorphic encryption. IACR Cryptol. ePrint Arch., 144 (2012). http:\/\/eprint.iacr.org\/2012\/144"},{"key":"25_CR15","doi-asserted-by":"publisher","unstructured":"Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Mitzenmacher, M. (ed.) Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009, Bethesda, MD, USA, 31 May\u20132 June 2009, pp. 169\u2013178. ACM (2009). https:\/\/doi.org\/10.1145\/1536414.1536440","DOI":"10.1145\/1536414.1536440"},{"key":"25_CR16","unstructured":"Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K.E., Naehrig, M., Wernsing, J.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: Balcan, M., Weinberger, K.Q. (eds.) Proceedings of the 33nd International Conference on Machine Learning, JMLR Workshop and Conference Proceedings, ICML 2016, New York City, NY, USA, 19\u201324 June 2016, vol. 48, pp. 201\u2013210. JMLR.org (2016). http:\/\/proceedings.mlr.press\/v48\/gilad-bachrach16.html"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Halevi, S., Shoup, V.: Algorithms in HElib. IACR Cryptol. ePrint Arch. 2014, 106 (2014)","DOI":"10.1007\/978-3-662-44371-2_31"},{"key":"25_CR18","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 770\u2013778. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR19","unstructured":"Huynh, D.: Cryptotree: fast and accurate predictions on encrypted structured data. CoRR abs\/2006.08299 (2020). https:\/\/arxiv.org\/abs\/2006.08299"},{"key":"25_CR20","doi-asserted-by":"publisher","unstructured":"McKeen, F., et al.: Innovative instructions and software model for isolated execution. In: Lee, R.B., Shi, W. (eds.) The Second Workshop on Hardware and Architectural Support for Security and Privacy, HASP 2013, Tel-Aviv, Israel, 23\u201324 June 2013, p. 10. ACM (2013). https:\/\/doi.org\/10.1145\/2487726.2488368","DOI":"10.1145\/2487726.2488368"},{"key":"25_CR21","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, 28 June\u20132 July 2011, pp. 689\u2013696. Omnipress (2011). https:\/\/icml.cc\/2011\/papers\/399_icmlpaper.pdf"},{"key":"25_CR22","unstructured":"Rahulamathavan, Y.: Privacy-preserving similarity calculation of speaker features using fully homomorphic encryption. CoRR abs\/2202.07994 (2022). https:\/\/arxiv.org\/abs\/2202.07994"},{"issue":"6","key":"25_CR23","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","volume":"34","author":"D Ramachandram","year":"2017","unstructured":"Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Sig. Process. Mag. 34(6), 96\u2013108 (2017). https:\/\/doi.org\/10.1109\/MSP.2017.2738401","journal-title":"IEEE Sig. Process. Mag."},{"key":"25_CR24","unstructured":"Rivest, R.L., Dertouzos, M.L.: On Data Banks and Privacy Homomorphisms (1978)"},{"key":"25_CR25","unstructured":"Microsoft SEAL (release 4.0). Microsoft Research, Redmond, WA, March 2022. https:\/\/github.com\/Microsoft\/SEAL"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Sun, L., Wang, J., Zhang, K., Su, Y., Weng, F.: RpBERT: a text-image relation propagation-based BERT model for multimodal NER. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2\u20139 February 2021, pp. 13860\u201313868. AAAI Press (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17633","DOI":"10.1609\/aaai.v35i15.17633"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Wang, D., Xiong, D.: Efficient object-level visual context modeling for multimodal machine translation: masking irrelevant objects helps grounding. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2\u20139 February 2021, pp. 2720\u20132728. AAAI Press (2021).https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16376","DOI":"10.1609\/aaai.v35i4.16376"},{"key":"25_CR28","doi-asserted-by":"publisher","unstructured":"Yao, A.C.: Protocols for secure computations (extended abstract). In: 23rd Annual Symposium on Foundations of Computer Science, Chicago, Illinois, USA, 3\u20135 November 1982, pp. 160\u2013164. IEEE Computer Society (1982). https:\/\/doi.org\/10.1109\/SFCS.1982.38","DOI":"10.1109\/SFCS.1982.38"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Yu, F., et al.: ERNIE-ViL: knowledge enhanced vision-language representations through scene graphs. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2\u20139 February 2021, pp. 3208\u20133216. AAAI Press (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16431","DOI":"10.1609\/aaai.v35i4.16431"},{"key":"25_CR30","doi-asserted-by":"publisher","unstructured":"Yu, W., et al.: CH-SIMS: a Chinese multimodal sentiment analysis dataset with fine-grained annotation of modality. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5\u201310 July 2020, pp. 3718\u20133727. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.343","DOI":"10.18653\/v1\/2020.acl-main.343"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Yu, W., Xu, H., Yuan, Z., Wu, J.: Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2\u20139 February 2021, pp. 10790\u201310797. AAAI Press (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17289","DOI":"10.1609\/aaai.v35i12.17289"},{"key":"25_CR32","doi-asserted-by":"publisher","unstructured":"Zadeh, A., Chen, M., Poria, S., Cambria, E., Morency, L.: Tensor fusion network for multimodal sentiment analysis. In: Palmer, M., Hwa, R., Riedel, S. (eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9\u201311 September 2017, pp. 1103\u20131114. Association for Computational Linguistics (2017). https:\/\/doi.org\/10.18653\/v1\/d17-1115","DOI":"10.18653\/v1\/d17-1115"},{"issue":"6","key":"25_CR33","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MIS.2016.94","volume":"31","author":"A Zadeh","year":"2016","unstructured":"Zadeh, A., Zellers, R., Pincus, E., Morency, L.: Multimodal sentiment intensity analysis in videos: facial gestures and verbal messages. IEEE Intell. Syst. 31(6), 82\u201388 (2016). https:\/\/doi.org\/10.1109\/MIS.2016.94","journal-title":"IEEE Intell. Syst."}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-5834-4_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T06:06:23Z","timestamp":1693807583000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-5834-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819958337","9789819958344"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-5834-4_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Phuket","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2023\/","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":"224","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":"50","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":"0","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":"22% - 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":"3,87","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":"2,82","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)"}}]}}