About me:

• Current status: Post-doc at EPFL (Spring lab), "Collaborative Machine Learning skeptic", and "honest-but-curious security model unbeliever". Working on:
• Security and Privacy in Machine Learning (Collaborative Machine Learning mainly).
• Password Security.
• HPC, GPGPU.

News:

• 👔 Looking for a position in industry.

Preprints:

• [ArXiv] - Dario Pasquini, Giuseppe Ateniese, Carmela Troncoso.
  Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data.
  • [Derived talk] - "Password Alchemy with Universal Neural-Cracking-Machines: Transmuting email addresses into Password Models" at PasswordsCon'23 (Bergen).

Recent publications:

• [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.
• [ACM CCS '21] - Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi.
  Unleashing the Tiger: Inference Attacks on Split Learning.
• [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.
• [IEEE S&P '21] - Dario Pasquini, Ankit Gangwal, Giuseppe Ateniese, Massimo Bernaschi, Mauro Conti.
  Improving Password Guessing via Representation Learning.

Program Committees:

• Current: ACM CCS 2023, USENIX Sec 2023