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Found 6 papers, 1 papers with code
Near-Optimal Algorithms for Constrained k-Center Clustering with Instance-level Background Knowledge
Center-based clustering has attracted significant research interest from both theory and practice.
Practical, Private Assurance of the Value of Collaboration
In this work, we construct an interactive protocol for this problem based on the fully homomorphic encryption scheme over the Torus (TFHE) and label differential privacy, where the underlying machine learning model is a neural network.
VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprints
(2) We introduce a novel approach using less private samples to enhance the performance of ownership testing.
A Differentially Private Framework for Deep Learning with Convexified Loss Functions
Under a black-box setting, based on this global sensitivity, to control the overall noise injection, we propose a novel output perturbation framework by injecting DP noise into a randomly sampled neuron (via the exponential mechanism) at the output layer of a baseline non-private neural network trained with a convexified loss function.
Data and Model Dependencies of Membership Inference Attack
Our results reveal the relationship between MIA accuracy and properties of the dataset and training model in use.
Differentially Private k-Means Clustering with Guaranteed Convergence
This problem severely impacts the clustering quality and the efficiency of a differentially private algorithm.
