Search Results for author:
Found 7 papers, 2 papers with code
Towards a Governance Framework for Brain Data
The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance.
Constrained Differentially Private Federated Learning for Low-bandwidth Devices
This bandwidth and corresponding processing costs could be prohibitive if the participating entities are, for example, mobile devices.
Compression Boosts Differentially Private Federated Learning
In this paper, compressive sensing is used to reduce the model size and hence increase model quality without sacrificing privacy.
Federated Learning in Adversarial Settings
This paper presents a new federated learning scheme that provides different trade-offs between robustness, privacy, bandwidth efficiency, and model accuracy.
Differentially Private Mixture of Generative Neural Networks
We model the generator distribution of the training data with a mixture of $k$ generative neural networks.
When Privacy meets Security: Leveraging personal information for password cracking
In extensive experiments we show that it can crack up to 69% of passwords at 10 billion guesses, more than all probabilistic password crackers we compared again t. Second, we systematically analyze the idea that additional personal information about a user helps in speeding up password guessing.
Cryptography and Security
Code injection attacks on harvard-architecture devices
To our knowledge, this is the first result that presents a code injection technique for such devices.
Cryptography and Security
