{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:24:06Z","timestamp":1773512646174,"version":"3.50.1"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031547720","type":"print"},{"value":"9783031547737","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-54773-7_15","type":"book-chapter","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:02:47Z","timestamp":1709164967000},"page":"373-402","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Privacy-Preserving Verifiable CNNs"],"prefix":"10.1007","author":[{"given":"Nuttapong","family":"Attrapadung","sequence":"first","affiliation":[]},{"given":"Goichiro","family":"Hanaoaka","sequence":"additional","affiliation":[]},{"given":"Ryo","family":"Hiromasa","sequence":"additional","affiliation":[]},{"given":"Yoshihiro","family":"Koseki","sequence":"additional","affiliation":[]},{"given":"Takahiro","family":"Matsuda","sequence":"additional","affiliation":[]},{"given":"Yutaro","family":"Nishida","sequence":"additional","affiliation":[]},{"given":"Yusuke","family":"Sakai","sequence":"additional","affiliation":[]},{"given":"Jacob C. N.","family":"Schuldt","sequence":"additional","affiliation":[]},{"given":"Satoshi","family":"Yasuda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","unstructured":"Ames, S., Hazay, C., Ishai, Y., Venkitasubramaniam, M.: Ligero: lightweight sublinear arguments without a trusted setup. In: Thuraisingham, B.M., Evans, D., Malkin, T., Xu, D. (eds.) ACM CCS 2017, pp. 2087\u20132104. ACM Press (2017). https:\/\/doi.org\/10.1145\/3133956.3134104","DOI":"10.1145\/3133956.3134104"},{"key":"15_CR2","doi-asserted-by":"publisher","unstructured":"Baum, C., Damg\u00e5rd, I., Orlandi, C.: Publicly auditable secure multi-party computation. In: Abdalla, M., Prisco, R.D. (eds.) SCN 14. LNCS, vol. 8642, pp. 175\u2013196. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10879-7_11","DOI":"10.1007\/978-3-319-10879-7_11"},{"key":"15_CR3","doi-asserted-by":"publisher","unstructured":"Ben-Sasson, E., Chiesa, A., Riabzev, M., Spooner, N., Virza, M., Ward, N.P.: Aurora: transparent succinct arguments for R1CS. In: Ishai, Y., Rijmen, V. (eds.) EUROCRYPT 2019, Part I. LNCS, vol. 11476, pp. 103\u2013128. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-17653-2_4","DOI":"10.1007\/978-3-030-17653-2_4"},{"key":"15_CR4","doi-asserted-by":"publisher","unstructured":"Bootle, J., Cerulli, A., Chaidos, P., Groth, J., Petit, C.: Efficient zero-knowledge arguments for arithmetic circuits in the discrete log setting. In: Fischlin, M., Coron, J.S. (eds.) EUROCRYPT 2016, Part II. LNCS, vol. 9666, pp. 327\u2013357. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-662-49896-5_12","DOI":"10.1007\/978-3-662-49896-5_12"},{"key":"15_CR5","doi-asserted-by":"publisher","unstructured":"Bourse, F., Minelli, M., Minihold, M., Paillier, P.: Fast homomorphic evaluation of deep discretized neural networks. In: Shacham, H., Boldyreva, A. (eds.) CRYPTO 2018, Part III. LNCS, vol. 10993, pp. 483\u2013512. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-96878-0_17","DOI":"10.1007\/978-3-319-96878-0_17"},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"B\u00fcnz, B., Bootle, J., Boneh, D., Poelstra, A., Wuille, P., Maxwell, G.: Bulletproofs: short proofs for confidential transactions and more. In: 2018 IEEE Symposium on Security and Privacy, pp. 315\u2013334. IEEE Computer Society Press (2018). https:\/\/doi.org\/10.1109\/SP.2018.00020","DOI":"10.1109\/SP.2018.00020"},{"key":"15_CR7","doi-asserted-by":"publisher","unstructured":"Byali, M., Chaudhari, H., Patra, A., Suresh, A.: FLASH: fast and robust framework for privacy-preserving machine learning. Proc. Privacy Enhanc. Technol. 2020(2), 459\u2013480 (2020). https:\/\/doi.org\/10.2478\/popets-2020-0036","DOI":"10.2478\/popets-2020-0036"},{"issue":"1","key":"15_CR8","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s001459910006","volume":"13","author":"R Canetti","year":"2000","unstructured":"Canetti, R.: Security and composition of multiparty cryptographic protocols. J. Cryptol. 13(1), 143\u2013202 (2000). https:\/\/doi.org\/10.1007\/s001459910006","journal-title":"J. Cryptol."},{"key":"15_CR9","unstructured":"Chandran, N., Gupta, D., Rastogi, A., Sharma, R., Tripathi, S.: EzPC: programmable, efficient, and scalable secure two-party computation for machine learning. Cryptology ePrint Archive, Report 2017\/1109 (2017). https:\/\/eprint.iacr.org\/2017\/1109"},{"key":"15_CR10","doi-asserted-by":"publisher","unstructured":"Chaudhari, H., Choudhury, A., Patra, A., Suresh, A.: ASTRA: high throughput 3pc over rings with application to secure prediction. In: Sion, R., Papamanthou, C. (eds.) Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop, CCSW@CCS 2019, London, 11 November 2019, pp. 81\u201392. ACM (2019). https:\/\/doi.org\/10.1145\/3338466.3358922","DOI":"10.1145\/3338466.3358922"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Chaudhari, H., Rachuri, R., Suresh, A.: Trident: efficient 4PC framework for privacy preserving machine learning. In: NDSS 2020. The Internet Society (2020)","DOI":"10.14722\/ndss.2020.23005"},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Chiesa, A., Hu, Y., Maller, M., Mishra, P., Vesely, N., Ward, N.P.: Marlin: preprocessing zkSNARKs with universal and updatable SRS. In: Canteaut, A., Ishai, Y. (eds.) EUROCRYPT 2020, Part I. LNCS, vol. 12105, pp. 738\u2013768. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-45721-1_26","DOI":"10.1007\/978-3-030-45721-1_26"},{"key":"15_CR13","doi-asserted-by":"publisher","unstructured":"Chiesa, A., Ojha, D., Spooner, N.: Fractal: post-quantum and transparent recursive proofs from holography. In: Canteaut, A., Ishai, Y. (eds.) EUROCRYPT 2020, Part I. LNCS, vol. 12105, pp. 769\u2013793. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45721-1_27","DOI":"10.1007\/978-3-030-45721-1_27"},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Damg\u00e5rd, I., Nielsen, J.B.: Universally composable efficient multiparty computation from threshold homomorphic encryption. In: Boneh, D. (ed.) CRYPTO 2003. LNCS, vol. 2729, pp. 247\u2013264. Springer, Cham (2003). https:\/\/doi.org\/10.1007\/978-3-540-45146-4_15","DOI":"10.1007\/978-3-540-45146-4_15"},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Damg\u00e5rd, I., Pastro, V., Smart, N.P., Zakarias, S.: Multiparty computation from somewhat homomorphic encryption. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 643\u2013662. Springer, Cham (2012). https:\/\/doi.org\/10.1007\/978-3-642-32009-5_38","DOI":"10.1007\/978-3-642-32009-5_38"},{"issue":"2","key":"15_CR16","doi-asserted-by":"publisher","first-page":"517","DOI":"10.2478\/popets-2022-0055","volume":"2022","author":"P Dayama","year":"2022","unstructured":"Dayama, P., Patra, A., Paul, P., Singh, N., Vinayagamurthy, D.: How to prove any NP statement jointly? Efficient distributed-prover zero-knowledge protocols. PoPETs 2022(2), 517\u2013556 (2022). https:\/\/doi.org\/10.2478\/popets-2022-0055","journal-title":"Efficient distributed-prover zero-knowledge protocols. PoPETs"},{"key":"15_CR17","unstructured":"Feng, B., Qin, L., Zhang, Z., Ding, Y., Chu, S.: ZEN: An optimizing compiler for verifiable, zero-knowledge neural network inferences. Cryptology ePrint Archive, Report 2021\/087 (2021). https:\/\/eprint.iacr.org\/2021\/087"},{"key":"15_CR18","unstructured":"Gabizon, A., Williamson, Z.J., Ciobotaru, O.: PLONK: permutations over Lagrange-bases for oecumenical noninteractive arguments of knowledge. Cryptology ePrint Archive, Report 2019\/953 (2019). https:\/\/eprint.iacr.org\/2019\/953"},{"key":"15_CR19","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, ICML 2016. JMLR Workshop and Conference Proceedings, vol. 48, pp. 201\u2013210. JMLR.org (2016)"},{"key":"15_CR20","unstructured":"Goldreich, O.: Foundations of Cryptography: Basic Applications, vol. 2. Cambridge University Press, Cambridge (2004)"},{"key":"15_CR21","doi-asserted-by":"publisher","unstructured":"Groth, J.: On the size of pairing-based non-interactive arguments. In: Fischlin, M., Coron, J.S. (eds.) EUROCRYPT 2016, Part II. LNCS, vol. 9666, pp. 305\u2013326. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-49896-5_11","DOI":"10.1007\/978-3-662-49896-5_11"},{"key":"15_CR22","unstructured":"Kang, D., Hashimoto, T., Stoica, I., Sun, Y.: Scaling up trustless DNN inference with zero-knowledge proofs. arXiv preprint arXiv:2210.08674 (2022)"},{"key":"15_CR23","doi-asserted-by":"publisher","unstructured":"Keller, M., Orsini, E., Scholl, P.: MASCOT: faster malicious arithmetic secure computation with oblivious transfer. In: Weippl, E.R., Katzenbeisser, S., Kruegel, C., Myers, A.C., Halevi, S. (eds.) ACM CCS 2016, pp. 830\u2013842. ACM Press (2016). https:\/\/doi.org\/10.1145\/2976749.2978357","DOI":"10.1145\/2976749.2978357"},{"key":"15_CR24","doi-asserted-by":"publisher","unstructured":"Kitai, H., et al.: MOBIUS: model-oblivious binarized neural networks. IEEE Access 7, 139021\u2013139034 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2939410","DOI":"10.1109\/ACCESS.2019.2939410"},{"key":"15_CR25","unstructured":"Knott, B., Venkataraman, S., Hannun, A.Y., Sengupta, S., Ibrahim, M., van\u00a0der Maaten, L.: Crypten: secure multi-party computation meets machine learning. In: Ranzato, M., Beygelzimer, A., Dauphin, Y.N., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, pp. 4961\u20134973 (2021)"},{"key":"15_CR26","unstructured":"Koti, N., Pancholi, M., Patra, A., Suresh, A.: SWIFT: super-fast and robust privacy-preserving machine learning. In: Bailey, M., Greenstadt, R. (eds.) USENIX Security 2021, pp. 2651\u20132668. USENIX Association (2021)"},{"issue":"11","key":"15_CR27","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"15_CR28","unstructured":"Lee, S., Ko, H., Kim, J., Oh, H.: vCNN: verifiable convolutional neural network. Cryptology ePrint Archive, Report 2020\/584 (2020). https:\/\/eprint.iacr.org\/2020\/584"},{"key":"15_CR29","doi-asserted-by":"publisher","unstructured":"Liu, J., Juuti, M., Lu, Y., Asokan, N.: Oblivious neural network predictions via MiniONN transformations. In: Thuraisingham, B.M., Evans, D., Malkin, T., Xu, D. (eds.) ACM CCS 2017, pp. 619\u2013631. ACM Press (2017). https:\/\/doi.org\/10.1145\/3133956.3134056","DOI":"10.1145\/3133956.3134056"},{"key":"15_CR30","doi-asserted-by":"publisher","unstructured":"Liu, T., Xie, X., Zhang, Y.: zkCNN: zero knowledge proofs for convolutional neural network predictions and accuracy. In: Vigna, G., Shi, E. (eds.) ACM CCS 2021, pp. 2968\u20132985. ACM Press (2021). https:\/\/doi.org\/10.1145\/3460120.3485379","DOI":"10.1145\/3460120.3485379"},{"key":"15_CR31","doi-asserted-by":"publisher","unstructured":"Mohassel, P., Rindal, P.: ABY$$^3$$: a mixed protocol framework for machine learning. In: Lie, D., Mannan, M., Backes, M., Wang, X. (eds.) ACM CCS 2018, pp. 35\u201352. ACM Press (2018). https:\/\/doi.org\/10.1145\/3243734.3243760","DOI":"10.1145\/3243734.3243760"},{"key":"15_CR32","doi-asserted-by":"publisher","unstructured":"Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: 2017 IEEE Symposium on Security and Privacy, pp. 19\u201338. IEEE Computer Society Press (2017). https:\/\/doi.org\/10.1109\/SP.2017.12","DOI":"10.1109\/SP.2017.12"},{"key":"15_CR33","doi-asserted-by":"publisher","unstructured":"Nishide, T., Ohta, K.: Multiparty computation for interval, equality, and comparison without bit-decomposition protocol. In: Okamoto, T., Wang, X. (eds.) PKC 2007. LNCS, vol. 4450, pp. 343\u2013360. Springer, Cham (2007). https:\/\/doi.org\/10.1007\/978-3-540-71677-8_23","DOI":"10.1007\/978-3-540-71677-8_23"},{"key":"15_CR34","unstructured":"Ozdemir, A., Boneh, D.: Experimenting with collaborative zk-SNARKs: zero-knowledge proofs for distributed secrets. Cryptology ePrint Archive, Report 2021\/1530 (2021). https:\/\/eprint.iacr.org\/2021\/1530"},{"key":"15_CR35","unstructured":"Ozdemir, A., Boneh, D.: Experimenting with collaborative zk-SNARKs: zero-knowledge proofs for distributed secrets. In: Butler, K.R.B., Thomas, K. (eds.) USENIX Security 2022, pp. 4291\u20134308. USENIX Association (2022)"},{"key":"15_CR36","doi-asserted-by":"crossref","unstructured":"Patra, A., Suresh, A.: BLAZE: Blazing fast privacy-preserving machine learning. In: NDSS 2020. The Internet Society (2020)","DOI":"10.14722\/ndss.2020.24202"},{"key":"15_CR37","doi-asserted-by":"crossref","unstructured":"Riazi, M.S., Weinert, C., Tkachenko, O., Songhori, E.M., Schneider, T., Koushanfar, F.: Chameleon: a hybrid secure computation framework for machine learning applications. In: Kim, J., Ahn, G.J., Kim, S., Kim, Y., L\u00f3pez, J., Kim, T. (eds.) ASIACCS 18, pp. 707\u2013721. ACM Press (2018)","DOI":"10.1145\/3196494.3196522"},{"key":"15_CR38","doi-asserted-by":"publisher","unstructured":"Rouhani, B.D., Riazi, M.S., Koushanfar, F.: Deepsecure: scalable provably-secure deep learning. In: Proceedings of the 55th Annual Design Automation Conference (DAC 2018), pp. 2:1\u20132:6. ACM (2018). https:\/\/doi.org\/10.1145\/3195970.3196023","DOI":"10.1145\/3195970.3196023"},{"key":"15_CR39","unstructured":"Setty, S.: Spartan: efficient and general-purpose zkSNARKs without trusted setup. Cryptology ePrint Archive, Report 2019\/550 (2019). https:\/\/eprint.iacr.org\/2019\/550"},{"key":"15_CR40","doi-asserted-by":"publisher","unstructured":"Smart, N.P., Talibi Alaoui, Y.: Distributing any elliptic curve based protocol. In: Albrecht, M. (ed.) 17th IMA International Conference on Cryptography and Coding. LNCS, vol. 11929, pp. 342\u2013366. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-35199-1_17","DOI":"10.1007\/978-3-030-35199-1_17"},{"issue":"3","key":"15_CR41","doi-asserted-by":"publisher","first-page":"26","DOI":"10.2478\/popets-2019-0035","volume":"2019","author":"S Wagh","year":"2019","unstructured":"Wagh, S., Gupta, D., Chandran, N.: SecureNN: 3-party secure computation for neural network training. PoPETs 2019(3), 26\u201349 (2019). https:\/\/doi.org\/10.2478\/popets-2019-0035","journal-title":"PoPETs"},{"key":"15_CR42","unstructured":"Weng, J., Weng, J., Tang, G., Yang, A., Li, M., Liu, J.N.: pvcnn: privacy-preserving and verifiable convolutional neural network testing (2022). https:\/\/arxiv.org\/abs\/2201.09186"},{"key":"15_CR43","unstructured":"LeCun, Y., Corinna Cortes, C.J.B.: The ch1MNIST database of handwritten digits (2010). http:\/\/yann.lecun.com\/exdb\/mnist\/"}],"container-title":["Lecture Notes in Computer Science","Applied Cryptography and Network Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-54773-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T16:12:00Z","timestamp":1709655120000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-54773-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031547720","9783031547737"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-54773-7_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACNS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Cryptography and Network Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Abu Dhabi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Arab Emirates","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 March 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 March 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acns2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wp.nyu.edu\/acns2024\/","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":"HotCRP","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"230","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":"54","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":"23% - 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":"4-6","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)"}}]}}