---
_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'
...