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Found 8 papers, 3 papers with code
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator.
Detecting AI Trojans Using Meta Neural Analysis
To train the meta-model without knowledge of the attack strategy, we introduce a technique called jumbo learning that samples a set of Trojaned models following a general distribution.
Understanding Membership Inferences on Well-Generalized Learning Models
Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model.
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Plausible Deniability for Privacy-Preserving Data Synthesis
We demonstrate the efficiency of this generative technique on a large dataset; it is shown to preserve the utility of original data with respect to various statistical analysis and machine learning measures.
Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be
We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas.
Cryptography and Security
Scalable Differentially Private Data Generation via Private Aggregation of Teacher Ensembles
We present a novel approach named G-PATE for training differentially private data generator.
EDoG: Adversarial Edge Detection For Graph Neural Networks
In this paper, we propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation.
