Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
The distributed discrete gaussian mechanism for federated learning with secure aggregation
We consider training models on private data that are distributed across user devices. To
ensure privacy, we add on-device noise and use secure aggregation so that only the noisy …
ensure privacy, we add on-device noise and use secure aggregation so that only the noisy …
The fundamental price of secure aggregation in differentially private federated learning
We consider the problem of training a $ d $ dimensional model with distributed differential
privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees …
privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees …
Dp-cryptography: marrying differential privacy and cryptography in emerging applications
DP-cryptography: marrying differential privacy and cryptography in emerging applications
Page 1 84 COMMUNICATIONS OF THE ACM | FEBRUARY 2021 | VOL. 64 | NO. 2 review …
Page 1 84 COMMUNICATIONS OF THE ACM | FEBRUARY 2021 | VOL. 64 | NO. 2 review …
Scenario-based Adaptations of Differential Privacy: A Technical Survey
Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …
privacy preservation. It has had great success in scenarios of local data privacy and …
Private summation in the multi-message shuffle model
The shuffle model of differential privacy (Erlingsson et al. SODA 2019; Cheu et al.
EUROCRYPT 2019) and its close relative encode-shuffle-analyze (Bittau et al. SOSP 2017) …
EUROCRYPT 2019) and its close relative encode-shuffle-analyze (Bittau et al. SOSP 2017) …
Stronger privacy amplification by shuffling for rényi and approximate differential privacy
The shuffle model of differential privacy has gained significant interest as an intermediate
trust model between the standard local and central models [18, 12]. A key result in this …
trust model between the standard local and central models [18, 12]. A key result in this …
On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy
It is well-known that general secure multi-party computation can in principle be applied to
implement differentially private mechanisms over distributed data with utility matching the …
implement differentially private mechanisms over distributed data with utility matching the …
User-level differentially private learning via correlated sampling
Most works in learning with differential privacy (DP) have focused on the setting where each
user has a single sample. In this work, we consider the setting where each user holds $ m …
user has a single sample. In this work, we consider the setting where each user holds $ m …
Scalable and differentially private distributed aggregation in the shuffled model
Federated learning promises to make machine learning feasible on distributed, private
datasets by implementing gradient descent using secure aggregation methods. The idea is …
datasets by implementing gradient descent using secure aggregation methods. The idea is …