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Status:
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Postdoc at EPFL🇨🇭 (Spring lab); "Semi-honest-model-unbeliever" seeking security and privacy measures that transcend trust, ideal assumptions, and heuristic security definitions (but, in reality, I spend most of my time either breaking ML models or building ML models to break stuff).
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Working on:
- Security and Privacy in Machine Learning:
- Collaborative Learning
- LLMs (yes, I know..)
- Password Security
- Security Crypto Protocols (when someone asks me to)
Preprints:
- [🔥Coming soon🔥] Neural Exec: Universal, Inline, Execution Triggers for Prompt Injection Attacks ...
Works I like to talk about:
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[IEEE S&P'24] -- Dario Pasquini, Danilo Francati, Giuseppe Ateniese, Evgenios M. Kornaropoulos.
Breach Extraction Attacks: Exposing and Addressing the Leakage in Second Generation Compromised Credential Checking Services
- [IEEE S&P'24] -- Dario Pasquini, Giuseppe Ateniese, Carmela Troncoso.
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data
- [IEEE S&P'23] -- Dario Pasquini, Mathilde Raynal, Carmela Troncoso.
On the (In)security of Peer-to-Peer Decentralized Machine Learning
- [ACM CCS'22] -- Dario Pasquini, Danilo Francati, Giuseppe Ateniese.
Eluding Secure Aggregation in Federated Learning via Model Inconsistency
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[ACM CCS'21] -- Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi.
Unleashing the Tiger: Inference Attacks on Split Learning
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[USENIX Sec'21] -- Dario Pasquini, Marco Cianfriglia, Giuseppe Ateniese, Massimo Bernaschi.
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries
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[IEEE S&P'21] -- Dario Pasquini, Ankit Gangwal, Giuseppe Ateniese, Massimo Bernaschi, Mauro Conti.
Improving Password Guessing via Representation Learning
Talks (the ones I remembered to put here):
Program Committees:
Things I don't work on, but I'd love to 🔮:
- Hardware sec, Cryptanalysis.