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Found 11 papers, 5 papers with code
The Adversarial Implications of Variable-Time Inference
In our examination of the timing side-channel vulnerabilities associated with this algorithm, we identified the potential to enhance decision-based attacks.
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
FL is promoted as a privacy-enhancing technology (PET) that provides data minimization: data never "leaves" personal devices and users share only model updates with a server (e. g., a company) coordinating the distributed training.
Learned Systems Security
We analyze the root causes of potentially-increased attack surface in learned systems and develop a framework for identifying vulnerabilities that stem from the use of ML.
Understanding Transformer Memorization Recall Through Idioms
In this work, we offer the first methodological framework for probing and characterizing recall of memorized sequences in transformer LMs.
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning
When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns.
When the Curious Abandon Honesty: Federated Learning Is Not Private
Instead, these devices share gradients, parameters, or other model updates, with a central party (e. g., a company) coordinating the training.
Transformer Feed-Forward Layers Are Key-Value Memories
Feed-forward layers constitute two-thirds of a transformer model's parameters, yet their role in the network remains under-explored.
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion
We demonstrate that neural code autocompleters are vulnerable to poisoning attacks.
De-Anonymizing Text by Fingerprinting Language Generation
Components of machine learning systems are not (yet) perceived as security hotspots.
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning
Word embeddings, i. e., low-dimensional vector representations such as GloVe and SGNS, encode word "meaning" in the sense that distances between words' vectors correspond to their semantic proximity.
The Limitations of Stylometry for Detecting Machine-Generated Fake News
Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading misinformation.
