--- _id: '12856' abstract: - lang: eng text: "As the complexity and criticality of software increase every year, so does the importance of run-time monitoring. Third-party monitoring, with limited knowledge of the monitored software, and best-effort monitoring, which keeps pace with the monitored software, are especially valuable, yet underexplored areas of run-time monitoring. Most existing monitoring frameworks do not support their combination because they either require access to the monitored code for instrumentation purposes or the processing of all observed events, or both.\r\n\r\nWe present a middleware framework, VAMOS, for the run-time monitoring of software which is explicitly designed to support third-party and best-effort scenarios. The design goals of VAMOS are (i) efficiency (keeping pace at low overhead), (ii) flexibility (the ability to monitor black-box code through a variety of different event channels, and the connectability to monitors written in different specification languages), and (iii) ease-of-use. To achieve its goals, VAMOS combines aspects of event broker and event recognition systems with aspects of stream processing systems.\r\nWe implemented a prototype toolchain for VAMOS and conducted experiments including a case study of monitoring for data races. The results indicate that VAMOS enables writing useful yet efficient monitors, is compatible with a variety of event sources and monitor specifications, and simplifies key aspects of setting up a monitoring system from scratch." acknowledgement: This work was supported in part by the ERC-2020-AdG 101020093. The authors would like to thank the anonymous FASE reviewers for their valuable feedback and suggestions. alternative_title: - LNCS article_processing_charge: No author: - first_name: Marek full_name: Chalupa, Marek id: 87e34708-d6c6-11ec-9f5b-9391e7be2463 last_name: Chalupa - first_name: Fabian full_name: Mühlböck, Fabian id: 6395C5F6-89DF-11E9-9C97-6BDFE5697425 last_name: Mühlböck orcid: 0000-0003-1548-0177 - first_name: Stefanie full_name: Muroya Lei, Stefanie id: a376de31-8972-11ed-ae7b-d0251c13c8ff last_name: Muroya Lei - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: 'Chalupa M, Mühlböck F, Muroya Lei S, Henzinger TA. Vamos: Middleware for best-effort third-party monitoring. In: Fundamental Approaches to Software Engineering. Vol 13991. Springer Nature; 2023:260-281. doi:10.1007/978-3-031-30826-0_15' apa: 'Chalupa, M., Mühlböck, F., Muroya Lei, S., & Henzinger, T. A. (2023). Vamos: Middleware for best-effort third-party monitoring. In Fundamental Approaches to Software Engineering (Vol. 13991, pp. 260–281). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-30826-0_15' chicago: 'Chalupa, Marek, Fabian Mühlböck, Stefanie Muroya Lei, and Thomas A Henzinger. “Vamos: Middleware for Best-Effort Third-Party Monitoring.” In Fundamental Approaches to Software Engineering, 13991:260–81. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-30826-0_15.' ieee: 'M. Chalupa, F. Mühlböck, S. Muroya Lei, and T. A. Henzinger, “Vamos: Middleware for best-effort third-party monitoring,” in Fundamental Approaches to Software Engineering, Paris, France, 2023, vol. 13991, pp. 260–281.' ista: 'Chalupa M, Mühlböck F, Muroya Lei S, Henzinger TA. 2023. Vamos: Middleware for best-effort third-party monitoring. Fundamental Approaches to Software Engineering. FASE: Fundamental Approaches to Software Engineering, LNCS, vol. 13991, 260–281.' mla: 'Chalupa, Marek, et al. “Vamos: Middleware for Best-Effort Third-Party Monitoring.” Fundamental Approaches to Software Engineering, vol. 13991, Springer Nature, 2023, pp. 260–81, doi:10.1007/978-3-031-30826-0_15.' short: M. Chalupa, F. Mühlböck, S. Muroya Lei, T.A. Henzinger, in:, Fundamental Approaches to Software Engineering, Springer Nature, 2023, pp. 260–281. conference: end_date: 2023-04-27 location: Paris, France name: 'FASE: Fundamental Approaches to Software Engineering' start_date: 2023-04-22 date_created: 2023-04-20T08:29:42Z date_published: 2023-04-20T00:00:00Z date_updated: 2023-04-25T07:19:07Z day: '20' ddc: - '000' department: - _id: ToHe doi: 10.1007/978-3-031-30826-0_15 ec_funded: 1 file: - access_level: open_access checksum: 17a7c8e08be609cf2408d37ea55e322c content_type: application/pdf creator: dernst date_created: 2023-04-25T07:16:36Z date_updated: 2023-04-25T07:16:36Z file_id: '12865' file_name: 2023_LNCS_ChalupaM.pdf file_size: 580828 relation: main_file success: 1 file_date_updated: 2023-04-25T07:16:36Z has_accepted_license: '1' intvolume: ' 13991' language: - iso: eng month: '04' oa: 1 oa_version: Published Version page: 260-281 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: Fundamental Approaches to Software Engineering publication_identifier: eisbn: - '9783031308260' eissn: - 1611-3349 isbn: - '9783031308253' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '12407' relation: earlier_version status: public status: public title: 'Vamos: Middleware for best-effort third-party monitoring' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 13991 year: '2023' ... --- _id: '12407' abstract: - lang: eng text: "As the complexity and criticality of software increase every year, so does the importance of run-time monitoring. Third-party monitoring, with limited knowledge of the monitored software, and best-effort monitoring, which keeps pace with the monitored software, are especially valuable, yet underexplored areas of run-time monitoring. Most existing monitoring frameworks do not support their combination because they either require access to the monitored code for instrumentation purposes or the processing of all observed events, or both.\r\n\r\nWe present a middleware framework, VAMOS, for the run-time monitoring of software which is explicitly designed to support third-party and best-effort scenarios. The design goals of VAMOS are (i) efficiency (keeping pace at low overhead), (ii) flexibility (the ability to monitor black-box code through a variety of different event channels, and the connectability to monitors written in different specification languages), and (iii) ease-of-use. To achieve its goals, VAMOS combines aspects of event broker and event recognition systems with aspects of stream processing systems.\r\n\r\nWe implemented a prototype toolchain for VAMOS and conducted experiments including a case study of monitoring for data races. The results indicate that VAMOS enables writing useful yet efficient monitors, is compatible with a variety of event sources and monitor specifications, and simplifies key aspects of setting up a monitoring system from scratch." acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093. \r\nThe authors would like to thank the anonymous FASE reviewers for their valuable feedback and suggestions." alternative_title: - IST Austria Technical Report article_processing_charge: No author: - first_name: Marek full_name: Chalupa, Marek id: 87e34708-d6c6-11ec-9f5b-9391e7be2463 last_name: Chalupa - first_name: Fabian full_name: Mühlböck, Fabian id: 6395C5F6-89DF-11E9-9C97-6BDFE5697425 last_name: Mühlböck orcid: 0000-0003-1548-0177 - first_name: Stefanie full_name: Muroya Lei, Stefanie id: a376de31-8972-11ed-ae7b-d0251c13c8ff last_name: Muroya Lei - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: 'Chalupa M, Mühlböck F, Muroya Lei S, Henzinger TA. VAMOS: Middleware for Best-Effort Third-Party Monitoring. Institute of Science and Technology Austria; 2023. doi:10.15479/AT:ISTA:12407' apa: 'Chalupa, M., Mühlböck, F., Muroya Lei, S., & Henzinger, T. A. (2023). VAMOS: Middleware for Best-Effort Third-Party Monitoring. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:12407' chicago: 'Chalupa, Marek, Fabian Mühlböck, Stefanie Muroya Lei, and Thomas A Henzinger. VAMOS: Middleware for Best-Effort Third-Party Monitoring. Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/AT:ISTA:12407.' ieee: 'M. Chalupa, F. Mühlböck, S. Muroya Lei, and T. A. Henzinger, VAMOS: Middleware for Best-Effort Third-Party Monitoring. Institute of Science and Technology Austria, 2023.' ista: 'Chalupa M, Mühlböck F, Muroya Lei S, Henzinger TA. 2023. VAMOS: Middleware for Best-Effort Third-Party Monitoring, Institute of Science and Technology Austria, 38p.' mla: 'Chalupa, Marek, et al. VAMOS: Middleware for Best-Effort Third-Party Monitoring. Institute of Science and Technology Austria, 2023, doi:10.15479/AT:ISTA:12407.' short: 'M. Chalupa, F. Mühlböck, S. Muroya Lei, T.A. Henzinger, VAMOS: Middleware for Best-Effort Third-Party Monitoring, Institute of Science and Technology Austria, 2023.' date_created: 2023-01-27T03:18:08Z date_published: 2023-01-27T00:00:00Z date_updated: 2023-04-25T07:19:06Z day: '27' ddc: - '005' department: - _id: ToHe doi: 10.15479/AT:ISTA:12407 ec_funded: 1 file: - access_level: open_access checksum: 55426e463fdeafe9777fc3ff635154c7 content_type: application/pdf creator: fmuehlbo date_created: 2023-01-27T03:18:34Z date_updated: 2023-01-27T03:18:34Z file_id: '12408' file_name: main.pdf file_size: 662409 relation: main_file success: 1 file_date_updated: 2023-01-27T03:18:34Z has_accepted_license: '1' keyword: - runtime monitoring - best effort - third party language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: '38' project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication_identifier: eissn: - 2664-1690 publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '12856' relation: later_version status: public status: public title: 'VAMOS: Middleware for Best-Effort Third-Party Monitoring' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: technical_report user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '13048' abstract: - lang: eng text: In this paper we introduce a pruning of the medial axis called the (λ,α)-medial axis (axλα). We prove that the (λ,α)-medial axis of a set K is stable in a Gromov-Hausdorff sense under weak assumptions. More formally we prove that if K and K′ are close in the Hausdorff (dH) sense then the (λ,α)-medial axes of K and K′ are close as metric spaces, that is the Gromov-Hausdorff distance (dGH) between the two is 1/4-Hölder in the sense that dGH (axλα(K),axλα(K′)) ≲ dH(K,K′)1/4. The Hausdorff distance between the two medial axes is also bounded, by dH (axλα(K),λα(K′)) ≲ dH(K,K′)1/2. These quantified stability results provide guarantees for practical computations of medial axes from approximations. Moreover, they provide key ingredients for studying the computability of the medial axis in the context of computable analysis. acknowledgement: "We are greatly indebted to Erin Chambers for posing a number of questions that eventually led to this paper. We would also like to thank the other organizers of the workshop on ‘Algorithms\r\nfor the medial axis’. We are also indebted to Tatiana Ezubova for helping with the search for and translation of Russian literature. The second author thanks all members of the Edelsbrunner and Datashape groups for the atmosphere in which the research was conducted.\r\nThe research leading to these results has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement No. 339025 GUDHI (Algorithmic Foundations of Geometry Understanding in Higher Dimensions). Supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 754411. The Austrian science fund (FWF) M-3073." article_processing_charge: No author: - first_name: André full_name: Lieutier, André last_name: Lieutier - first_name: Mathijs full_name: Wintraecken, Mathijs id: 307CFBC8-F248-11E8-B48F-1D18A9856A87 last_name: Wintraecken orcid: 0000-0002-7472-2220 citation: ama: 'Lieutier A, Wintraecken M. Hausdorff and Gromov-Hausdorff stable subsets of the medial axis. In: Proceedings of the 55th Annual ACM Symposium on Theory of Computing. Association for Computing Machinery; 2023:1768-1776. doi:10.1145/3564246.3585113' apa: 'Lieutier, A., & Wintraecken, M. (2023). Hausdorff and Gromov-Hausdorff stable subsets of the medial axis. In Proceedings of the 55th Annual ACM Symposium on Theory of Computing (pp. 1768–1776). Orlando, FL, United States: Association for Computing Machinery. https://doi.org/10.1145/3564246.3585113' chicago: Lieutier, André, and Mathijs Wintraecken. “Hausdorff and Gromov-Hausdorff Stable Subsets of the Medial Axis.” In Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 1768–76. Association for Computing Machinery, 2023. https://doi.org/10.1145/3564246.3585113. ieee: A. Lieutier and M. Wintraecken, “Hausdorff and Gromov-Hausdorff stable subsets of the medial axis,” in Proceedings of the 55th Annual ACM Symposium on Theory of Computing, Orlando, FL, United States, 2023, pp. 1768–1776. ista: 'Lieutier A, Wintraecken M. 2023. Hausdorff and Gromov-Hausdorff stable subsets of the medial axis. Proceedings of the 55th Annual ACM Symposium on Theory of Computing. STOC: Symposium on Theory of Computing, 1768–1776.' mla: Lieutier, André, and Mathijs Wintraecken. “Hausdorff and Gromov-Hausdorff Stable Subsets of the Medial Axis.” Proceedings of the 55th Annual ACM Symposium on Theory of Computing, Association for Computing Machinery, 2023, pp. 1768–76, doi:10.1145/3564246.3585113. short: A. Lieutier, M. Wintraecken, in:, Proceedings of the 55th Annual ACM Symposium on Theory of Computing, Association for Computing Machinery, 2023, pp. 1768–1776. conference: end_date: 2023-06-23 location: Orlando, FL, United States name: 'STOC: Symposium on Theory of Computing' start_date: 2023-06-20 date_created: 2023-05-22T08:02:02Z date_published: 2023-06-02T00:00:00Z date_updated: 2023-05-22T08:15:19Z day: '02' department: - _id: HeEd doi: 10.1145/3564246.3585113 ec_funded: 1 external_id: arxiv: - '2303.04014' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2303.04014 month: '06' oa: 1 oa_version: Preprint page: 1768-1776 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships - _id: fc390959-9c52-11eb-aca3-afa58bd282b2 grant_number: M03073 name: Learning and triangulating manifolds via collapses publication: Proceedings of the 55th Annual ACM Symposium on Theory of Computing publication_identifier: isbn: - '9781450399135' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' status: public title: Hausdorff and Gromov-Hausdorff stable subsets of the medial axis type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '13053' abstract: - lang: eng text: 'Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior is stable under compression operations such as pruning. Thus, dense models trained via CrAM should be compressible post-training, in a single step, without significant accuracy loss. Experimental results on standard benchmarks, such as residual networks for ImageNet classification and BERT models for language modelling, show that CrAM produces dense models that can be more accurate than the standard SGD/Adam-based baselines, but which are stable under weight pruning: specifically, we can prune models in one-shot to 70-80% sparsity with almost no accuracy loss, and to 90% with reasonable (∼1%) accuracy loss, which is competitive with gradual compression methods. Additionally, CrAM can produce sparse models which perform well for transfer learning, and it also works for semi-structured 2:4 pruning patterns supported by GPU hardware. The code for reproducing the results is available at this https URL .' acknowledged_ssus: - _id: ScienComp acknowledgement: "AP, EK, DA received funding from the European Research Council (ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). AV acknowledges the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-21-CE48-0016 (project COMCOPT). We further acknowledge the support from the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp)-" article_processing_charge: No author: - first_name: Elena-Alexandra full_name: Peste, Elena-Alexandra id: 32D78294-F248-11E8-B48F-1D18A9856A87 last_name: Peste - first_name: Adrian full_name: Vladu, Adrian last_name: Vladu - first_name: Eldar full_name: Kurtic, Eldar id: 47beb3a5-07b5-11eb-9b87-b108ec578218 last_name: Kurtic - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. In: 11th International Conference on Learning Representations .' apa: 'Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (n.d.). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda .' chicago: 'Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations , n.d.' ieee: 'E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda .' ista: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. 11th International Conference on Learning Representations . ICLR: International Conference on Learning Representations.' mla: 'Peste, Elena-Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th International Conference on Learning Representations .' short: E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , n.d. conference: end_date: 2023-05-05 location: 'Kigali, Rwanda ' name: 'ICLR: International Conference on Learning Representations' start_date: 2023-05-01 date_created: 2023-05-23T11:36:18Z date_published: 2023-05-01T00:00:00Z date_updated: 2023-06-01T12:54:45Z department: - _id: GradSch - _id: DaAl - _id: ChLa ec_funded: 1 external_id: arxiv: - '2207.14200' language: - iso: eng main_file_link: - open_access: '1' url: https://openreview.net/pdf?id=_eTZBs-yedr month: '05' oa: 1 oa_version: Preprint project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: '11th International Conference on Learning Representations ' publication_status: accepted quality_controlled: '1' related_material: record: - id: '13074' relation: dissertation_contains status: public status: public title: 'CrAM: A Compression-Aware Minimizer' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '13143' abstract: - lang: eng text: "GIMPS and PrimeGrid are large-scale distributed projects dedicated to searching giant prime numbers, usually of special forms like Mersenne and Proth primes. The numbers in the current search-space are millions of digits large and the participating volunteers need to run resource-consuming primality tests. Once a candidate prime N has been found, the only way for another party to independently verify the primality of N used to be by repeating the expensive primality test. To avoid the need for second recomputation of each primality test, these projects have recently adopted certifying mechanisms that enable efficient verification of performed tests. However, the mechanisms presently in place only detect benign errors and there is no guarantee against adversarial behavior: a malicious volunteer can mislead the project to reject a giant prime as being non-prime.\r\nIn this paper, we propose a practical, cryptographically-sound mechanism for certifying the non-primality of Proth numbers. That is, a volunteer can – parallel to running the primality test for N – generate an efficiently verifiable proof at a little extra cost certifying that N is not prime. The interactive protocol has statistical soundness and can be made non-interactive using the Fiat-Shamir heuristic.\r\nOur approach is based on a cryptographic primitive called Proof of Exponentiation (PoE) which, for a group G, certifies that a tuple (x,y,T)∈G2×N satisfies x2T=y (Pietrzak, ITCS 2019 and Wesolowski, J. Cryptol. 2020). In particular, we show how to adapt Pietrzak’s PoE at a moderate additional cost to make it a cryptographically-sound certificate of non-primality." acknowledgement: 'We are grateful to Pavel Atnashev for clarifying via e-mail several aspects of the primality tests implementated in the PrimeGrid project. Pavel Hubáček is supported by the Czech Academy of Sciences (RVO 67985840), the Grant Agency of the Czech Republic under the grant agreement no. 19-27871X, and by the Charles University project UNCE/SCI/004. Chethan Kamath is supported by Azrieli International Postdoctoral Fellowship, ISF grants 484/18 and 1789/19, and ERC StG project SPP: Secrecy Preserving Proofs.' alternative_title: - LNCS article_processing_charge: No author: - first_name: Charlotte full_name: Hoffmann, Charlotte id: 0f78d746-dc7d-11ea-9b2f-83f92091afe7 last_name: Hoffmann - first_name: Pavel full_name: Hubáček, Pavel last_name: Hubáček - first_name: Chethan full_name: Kamath, Chethan last_name: Kamath - first_name: Krzysztof Z full_name: Pietrzak, Krzysztof Z id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87 last_name: Pietrzak orcid: 0000-0002-9139-1654 citation: ama: 'Hoffmann C, Hubáček P, Kamath C, Pietrzak KZ. Certifying giant nonprimes. In: Public-Key Cryptography - PKC 2023. Vol 13940. Springer Nature; 2023:530-553. doi:10.1007/978-3-031-31368-4_19' apa: 'Hoffmann, C., Hubáček, P., Kamath, C., & Pietrzak, K. Z. (2023). Certifying giant nonprimes. In Public-Key Cryptography - PKC 2023 (Vol. 13940, pp. 530–553). Atlanta, GA, United States: Springer Nature. https://doi.org/10.1007/978-3-031-31368-4_19' chicago: Hoffmann, Charlotte, Pavel Hubáček, Chethan Kamath, and Krzysztof Z Pietrzak. “Certifying Giant Nonprimes.” In Public-Key Cryptography - PKC 2023, 13940:530–53. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-31368-4_19. ieee: C. Hoffmann, P. Hubáček, C. Kamath, and K. Z. Pietrzak, “Certifying giant nonprimes,” in Public-Key Cryptography - PKC 2023, Atlanta, GA, United States, 2023, vol. 13940, pp. 530–553. ista: 'Hoffmann C, Hubáček P, Kamath C, Pietrzak KZ. 2023. Certifying giant nonprimes. Public-Key Cryptography - PKC 2023. PKC: Public-Key Cryptography, LNCS, vol. 13940, 530–553.' mla: Hoffmann, Charlotte, et al. “Certifying Giant Nonprimes.” Public-Key Cryptography - PKC 2023, vol. 13940, Springer Nature, 2023, pp. 530–53, doi:10.1007/978-3-031-31368-4_19. short: C. Hoffmann, P. Hubáček, C. Kamath, K.Z. Pietrzak, in:, Public-Key Cryptography - PKC 2023, Springer Nature, 2023, pp. 530–553. conference: end_date: 2023-05-10 location: Atlanta, GA, United States name: 'PKC: Public-Key Cryptography' start_date: 2023-05-07 date_created: 2023-06-18T22:00:47Z date_published: 2023-05-02T00:00:00Z date_updated: 2023-06-19T08:03:37Z day: '02' department: - _id: KrPi doi: 10.1007/978-3-031-31368-4_19 intvolume: ' 13940' language: - iso: eng main_file_link: - open_access: '1' url: https://eprint.iacr.org/2023/238 month: '05' oa: 1 oa_version: Submitted Version page: 530-553 publication: Public-Key Cryptography - PKC 2023 publication_identifier: eissn: - 1611-3349 isbn: - '9783031313677' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Certifying giant nonprimes type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 13940 year: '2023' ...