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Found 14 papers, 6 papers with code
Auditing $f$-Differential Privacy in One Run
Empirical auditing has emerged as a means of catching some of the flaws in the implementation of privacy-preserving algorithms.
Noisy Neighbors: Efficient membership inference attacks against LLMs
The potential of transformer-based LLMs risks being hindered by privacy concerns due to their reliance on extensive datasets, possibly including sensitive information.
ReMasker: Imputing Tabular Data with Masked Autoencoding
We present ReMasker, a new method of imputing missing values in tabular data by extending the masked autoencoding framework.
Federated Linear Contextual Bandits with User-level Differential Privacy
This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP).
Evaluating Privacy Leakage in Split Learning
In particular, on-device machine learning allows us to avoid sharing raw data with a third-party server during inference.
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Despite FL's initial success, many important deep learning use cases, such as ranking and recommendation tasks, have been limited from on-device learning.
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device.
Differentially Private Query Release Through Adaptive Projection
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like $k$-way marginals, subject to differential privacy.
Adversarial Robustness with Non-uniform Perturbations
Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors.
On Collaborative Predictive Blacklisting
Unfortunately, however, research on CPB has only focused on increasing the number of predicted attacks but has not considered the impact on false positives and false negatives.
Cryptography and Security
Exploiting Unintended Feature Leakage in Collaborative Learning
First, we show that an adversarial participant can infer the presence of exact data points -- for example, specific locations -- in others' training data (i. e., membership inference).
Differentially Private Mixture of Generative Neural Networks
We model the generator distribution of the training data with a mixture of $k$ generative neural networks.
LOGAN: Membership Inference Attacks Against Generative Models
Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution.
Building and Measuring Privacy-Preserving Predictive Blacklists
(Withdrawn) Collaborative security initiatives are increasingly often advocated to improve timeliness and effectiveness of threat mitigation.
