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Found 10 papers, 2 papers with code
On the Robustness of Malware Detectors to Adversarial Samples
They have been demonstrated to pose significant challenges in domains like image classification, with results showing that an adversarially perturbed image to evade detection against one classifier is most likely transferable to other classifiers.
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.
On the Adversarial Inversion of Deep Biometric Representations
For the biometric authentication use case, we need to investigate this under adversarial settings where an attacker has access to a feature-space representation but no direct access to the exact original dataset nor the original learned model.
Those Aren't Your Memories, They're Somebody Else's: Seeding Misinformation in Chat Bot Memories
We generate 150 examples of misinformation, of which 114 (76%) were remembered by BlenderBot 2 when combined with a personal statement.
Unintended Memorization and Timing Attacks in Named Entity Recognition Models
In this paper, we study the setting when NER models are available as a black-box service for identifying sensitive information in user documents and show that these models are vulnerable to membership inference on their training datasets.
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.
On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models
In this paper, we take a closer look at another inference attack reported in literature, called attribute inference, whereby an attacker tries to infer missing attributes of a partially known record used in the training dataset by accessing the machine learning model as an API.
On the Resilience of Biometric Authentication Systems against Random Inputs
The average false positive rate (FPR) of the system, i. e., the rate at which an impostor is incorrectly accepted as the legitimate user, may be interpreted as a measure of the success probability of such an attack.
On Inferring Training Data Attributes in Machine Learning Models
A number of recent works have demonstrated that API access to machine learning models leaks information about the dataset records used to train the models.
Not All Attributes are Created Equal: $d_{\mathcal{X}}$-Private Mechanisms for Linear Queries
However, it provides the same level of protection for all elements (individuals and attributes) in the data.
