43 citations · 45 across the 3 of their papers we have counts for
8 papers
Efficient Hyperparameter Optimization for Differentially Private Deep Learning
Aman Priyanshu, Rakshit Naidu, Fatemehsadat Mireshghallah +1
Tuning the hyperparameters in the differentially private stochastic gradient descent (DPSGD) is a fundamental challenge. Unlike the typical SGD, private datasets cannot be used man…
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs
Mohammad Malekzadeh, Anastasia Borovykh, Deniz Gündüz
It is known that deep neural networks, trained for the classification of non-sensitive target attributes, can reveal sensitive attributes of their input data through internal repre…
Dopamine: Differentially Private Federated Learning on Medical Data
Mohammad Malekzadeh, Burak Hasircioglu, Nitish Mital +3
While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (…
Running Neural Networks on the NIC
Giuseppe Siracusano, Salvator Galea, Davide Sanvito +4
In this paper we show that the data plane of commodity programmable (Network Interface Cards) NICs can run neural network inference tasks required by packet monitoring applications…
Privacy and Utility Preserving Sensor-Data Transformations
Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro +1
Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To…
Privacy-Preserving Bandits
Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi +1
Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to pr…