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Found 10 papers, 5 papers with code
QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems
Query-based systems (QBSs) are one of the key approaches for sharing data.
A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data
To measure the risk of an MIA performed by a realistic adversary, we develop the first Zero Auxiliary Knowledge (ZK) MIA on aggregate location data, which eliminates the need for an auxiliary dataset of real individual traces.
Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks
We evaluate six neural networks architectures as the embedding model.
Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data
While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset.
Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing
The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective.
Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting
We here present the first systematic analysis of the causes of misalignment in shadow models and show the use of a different weight initialisation to be the main cause.
QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems
We show the attacks found by QS to consistently equate or outperform, sometimes by a large margin, the best attacks from the literature.
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
We use a language-model-based approach for pronoun resolution in combination with our WikiCREM dataset.
A Surprisingly Robust Trick for the Winograd Schema Challenge
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning.
A Surprisingly Robust Trick for Winograd Schema Challenge
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning.
Ranked #13 on
Natural Language Inference
on WNLI
