8 citations · 21 across the 7 of their papers we have counts for
8 papers
Link Stealing Attacks Against Inductive Graph Neural Networks
Yixin Wu, Xinlei He, Pascal Berrang +4
A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in two settings, includi…
Quantifying Privacy Risks of Prompts in Visual Prompt Learning
Yixin Wu, Rui Wen, Michael Backes +4
Large-scale pre-trained models are increasingly adapted to downstream tasks through a new paradigm called prompt learning. In contrast to fine-tuning, prompt learning does not upda…
Fine-Tuning Is All You Need to Mitigate Backdoor Attacks
Zeyang Sha, Xinlei He, Pascal Berrang +2
Backdoor attacks represent one of the major threats to machine learning models. Various efforts have been made to mitigate backdoors. However, existing defenses have become increas…
Data Poisoning Attacks Against Multimodal Encoders
Ziqing Yang, Xinlei He, Zheng Li +4
Recently, the newly emerged multimodal models, which leverage both visual and linguistic modalities to train powerful encoders, have gained increasing attention. However, learning…
Accountable Javascript Code Delivery
Ilkan Esiyok, Pascal Berrang, Katriel Cohn-Gordon +1
The internet is a major distribution platform for web applications, but there are no effective transparency and audit mechanisms in place for the web. Due to the ephemeral nature o…
On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy
Zhipeng Wang, Stefanos Chaliasos, Kaihua Qin +5
Zero-knowledge proof (ZKP) mixers are one of the most widely-used blockchain privacy solutions, operating on top of smart contract-enabled blockchains. We find that ZKP mixers are…
Albatross: An optimistic consensus algorithm
Pascal Berrang, Inês Cruz, Bruno França +2
The consensus protocol is a critical component of distributed ledgers and blockchains. Achieving consensus over a decentralized network poses challenges to transaction finality and…
From Closed-world Enforcement to Open-world Assessment of Privacy
Michael Backes, Pascal Berrang, Praveen Manoharan
In this paper, we develop a user-centric privacy framework for quantitatively assessing the exposure of personal information in open settings. Our formalization addresses key-chall…