1 citations · 1 across the 7 of their papers we have counts for
9 papers
Edit-Neighboring Data Streams and Privacy under Continual Observation
Joel Daniel Andersson, Anamay Chaturvedi, Monika Henzinger +1
Differential privacy under Continual Observation (CO) quantifies the loss in privacy that occurs when outputs generated using a stream of sensitive input data are published in the…
Beyond Square Roots: A Memory-Efficient Explicit Factorization for Multi-Epoch Private Learning
Nikita P. Kalinin, Aki Rehn, Joel Daniel Andersson +2
Correlated-noise mechanisms are among the most promising approaches for improving the utility of differentially private model training, but rigorous guarantees require explicit, an…
Improved Accuracy for Private Continual Cardinality Estimation in Fully Dynamic Streams via Matrix Factorization
Joel Daniel Andersson, Palak Jain, Satchit Sivakumar
We study differentially-private statistics in the fully dynamic continual observation model, where many updates can arrive at each time step and updates to a stream can involve bot…
Learning Rate Scheduling with Matrix Factorization for Private Training
Nikita P. Kalinin, Joel Daniel Andersson
We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correlated noise, in particular via ma…
Private Lossless Multiple Release
Joel Daniel Andersson, Lukas Retschmeier, Boel Nelson +1
Koufogiannis et al. (2016) showed a result for Laplace noise-based differentially private mechanisms: given an -DP release, a new release wi…
On the Space Complexity of Online Convolution
Joel Daniel Andersson, Amir Yehudayoff
We study a discrete convolution streaming problem. An input arrives as a stream of numbers , and at time our goal is to output where is a…
Streaming Private Continual Counting via Binning
Joel Daniel Andersson, Rasmus Pagh
In differential privacy, refers to problems in which we wish to continuously release a function of a dataset that is revealed one element at a time…
Count on Your Elders: Laplace vs Gaussian Noise
Joel Daniel Andersson, Rasmus Pagh, Teresa Anna Steiner +1
In recent years, Gaussian noise has become a popular tool in differentially private algorithms, often replacing Laplace noise which dominated the early literature. Gaussian noise i…
Continual Counting with Gradual Privacy Expiration
Joel Daniel Andersson, Monika Henzinger, Rasmus Pagh +2
Differential privacy with gradual expiration models the setting where data items arrive in a stream and at a given time the privacy loss guaranteed for a data item seen at time…