Search Results for author:
Found 6 papers, 1 papers with code
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.
DDoD: Dual Denial of Decision Attacks on Human-AI Teams
Artificial Intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient.
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.
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.
A Geometric Approach for Real-time Monitoring of Dynamic Large Scale Graphs: AS-level graphs illustrated
The monitoring of large dynamic networks is a major chal- lenge for a wide range of application.
