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DP-Cryptography: Marrying Differential Privacy and Cryptography in
  Emerging Applications

DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications

19 April 2020
Sameer Wagh
Xi He
Ashwin Machanavajjhala
Prateek Mittal
ArXivPDFHTML

Papers citing "DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications"

5 / 5 papers shown
Title
Active Membership Inference Attack under Local Differential Privacy in
  Federated Learning
Active Membership Inference Attack under Local Differential Privacy in Federated Learning
Truc D. T. Nguyen
Phung Lai
K. Tran
Nhathai Phan
My T. Thai
FedML
18
18
0
24 Feb 2023
Private, Efficient, and Accurate: Protecting Models Trained by
  Multi-party Learning with Differential Privacy
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy
Wenqiang Ruan
Ming Xu
Wenjing Fang
Li Wang
Lei Wang
Wei Han
32
12
0
18 Aug 2022
Private Aggregation from Fewer Anonymous Messages
Private Aggregation from Fewer Anonymous Messages
Badih Ghazi
Pasin Manurangsi
Rasmus Pagh
A. Velingker
FedML
45
55
0
24 Sep 2019
Amplification by Shuffling: From Local to Central Differential Privacy
  via Anonymity
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Ulfar Erlingsson
Vitaly Feldman
Ilya Mironov
A. Raghunathan
Kunal Talwar
Abhradeep Thakurta
141
420
0
29 Nov 2018
Prochlo: Strong Privacy for Analytics in the Crowd
Prochlo: Strong Privacy for Analytics in the Crowd
Andrea Bittau
Ulfar Erlingsson
Petros Maniatis
Ilya Mironov
A. Raghunathan
David Lie
Mitch Rudominer
Ushasree Kode
J. Tinnés
B. Seefeld
91
278
0
02 Oct 2017
1