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Dario Pasquini
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Books and Theses
- 2021
- [b1]Dario Pasquini:
Enabling secure passwords via Deep Learning: Towards a new generation of attacks and defenses. Sapienza University of Rome, Italy, 2021
Journal Articles
- 2020
- [j2]Massimo Bernaschi, Pasqua D'Ambra
, Dario Pasquini:
AMG based on compatible weighted matching for GPUs. Parallel Comput. 92: 102599 (2020) - [j1]Massimo Bernaschi, Pasqua D'Ambra
, Dario Pasquini:
BootCMatchG: An adaptive Algebraic MultiGrid linear solver for GPUs. Softw. Impacts 6: 100041 (2020)
Conference and Workshop Papers
- 2023
- [c8]Etienne Salimbeni, Nina Mainusch, Dario Pasquini:
Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning. SP (Workshops) 2023: 94-104 - [c7]Dario Pasquini, Mathilde Raynal, Carmela Troncoso:
On the (In)security of Peer-to-Peer Decentralized Machine Learning. SP 2023: 418-436 - 2022
- [c6]Dario Pasquini, Danilo Francati, Giuseppe Ateniese:
Eluding Secure Aggregation in Federated Learning via Model Inconsistency. CCS 2022: 2429-2443 - 2021
- [c5]Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi:
Unleashing the Tiger: Inference Attacks on Split Learning. CCS 2021: 2113-2129 - [c4]Dario Pasquini, Ankit Gangwal
, Giuseppe Ateniese, Massimo Bernaschi, Mauro Conti
:
Improving Password Guessing via Representation Learning. SP 2021: 1382-1399 - [c3]Dario Pasquini, Marco Cianfriglia, Giuseppe Ateniese, Massimo Bernaschi:
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries. USENIX Security Symposium 2021: 821-838 - 2020
- [c2]Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi:
Interpretable Probabilistic Password Strength Meters via Deep Learning. ESORICS (1) 2020: 502-522 - 2019
- [c1]Dario Pasquini, Marco Mingione
, Massimo Bernaschi:
Adversarial Out-domain Examples for Generative Models. EuroS&P Workshops 2019: 272-280
Informal and Other Publications
- 2024
- [i14]Dario Pasquini, Martin Strohmeier, Carmela Troncoso:
Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks. CoRR abs/2403.03792 (2024) - [i13]Dario Pasquini, Evgenios M. Kornaropoulos, Giuseppe Ateniese:
LLMmap: Fingerprinting For Large Language Models. CoRR abs/2407.15847 (2024) - 2023
- [i12]Dario Pasquini, Giuseppe Ateniese, Carmela Troncoso:
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data. CoRR abs/2301.07628 (2023) - [i11]Mathilde Raynal, Dario Pasquini, Carmela Troncoso:
Can Decentralized Learning be more robust than Federated Learning? CoRR abs/2303.03829 (2023) - [i10]Etienne Salimbeni, Nina Mainusch, Dario Pasquini:
Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning. CoRR abs/2306.08638 (2023) - [i9]Dario Pasquini, Danilo Francati, Giuseppe Ateniese, Evgenios M. Kornaropoulos:
Breach Extraction Attacks: Exposing and Addressing the Leakage in Second Generation Compromised Credential Checking Services. IACR Cryptol. ePrint Arch. 2023: 1848 (2023) - 2022
- [i8]Dario Pasquini, Mathilde Raynal, Carmela Troncoso:
On the Privacy of Decentralized Machine Learning. CoRR abs/2205.08443 (2022) - 2021
- [i7]Dario Pasquini, Danilo Francati, Giuseppe Ateniese:
Eluding Secure Aggregation in Federated Learning via Model Inconsistency. CoRR abs/2111.07380 (2021) - 2020
- [i6]Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi:
Interpretable Probabilistic Password Strength Meters via Deep Learning. CoRR abs/2004.07179 (2020) - [i5]Dario Pasquini, Marco Cianfriglia, Giuseppe Ateniese, Massimo Bernaschi:
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries. CoRR abs/2010.12269 (2020) - [i4]Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi:
Unleashing the Tiger: Inference Attacks on Split Learning. CoRR abs/2012.02670 (2020) - 2019
- [i3]Dario Pasquini, Marco Mingione, Massimo Bernaschi:
Out-domain examples for generative models. CoRR abs/1903.02926 (2019) - [i2]Dario Pasquini, Ankit Gangwal, Giuseppe Ateniese, Massimo Bernaschi, Mauro Conti:
Improving Password Guessing via Representation Learning. CoRR abs/1910.04232 (2019) - [i1]Dario Pasquini, Ankit Gangwal, Giuseppe Ateniese, Massimo Bernaschi, Mauro Conti:
Improving Password Guessing via Representation Learning. IACR Cryptol. ePrint Arch. 2019: 1188 (2019)
Coauthor Index
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