Thakurta, Abhradeep

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

Ponomareva, N., Hazimeh, H., Kurakin, A., Xu, Z., Denison, C., McMahan, H. B., Vassilvitskii, S., Chien, S., & Thakurta, A. G. (2023) · Journal of Artificial Intelligence Research

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 166

Practical and private (deep) learning without sampling or shuffling

Kairouz, P., McMahan, B., Song, S., Thakkar, O., Thakurta, A., & Xu, Z. (2021) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 52

Tempered Sigmoid Activations for Deep Learning with Differential Privacy

Papernot, N., Thakurta, A., Song, S., Chien, S., & Erlingsson, Ú. (2020) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 23

Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs

Gorantala, S., Tong, J., Ali, A., Li, B., Katz, J., Kun, J., Steinke, T., Thakurta, A., Walker, J., & Yazdanbakhsh, A. (2026) · arXiv

Cryptography and Data SecurityCryptographic Implementations and Security

Cited by 0