Graham Cormode

research

∙ 04/10/2023

PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy

Answering database queries while preserving privacy is an important prob...

0 Kuntai Cai, et al. ∙

research

∙ 02/04/2023

Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting

Data sketching is a critical tool for distinct counting, enabling multis...

0 Jonathan Hehir, et al. ∙

research

∙ 01/05/2023

Streaming Zero-Knowledge Proofs

We initiate the study of zero-knowledge proofs for data streams. Streami...

0 Graham Cormode, et al. ∙

research

∙ 11/18/2022

The communication cost of security and privacy in federated frequency estimation

We consider the federated frequency estimation problem, where each user ...

0 Wei-Ning Chen, et al. ∙

research

∙ 10/22/2022

Federated Calibration and Evaluation of Binary Classifiers

We address two major obstacles to practical use of supervised classifier...

0 Graham Cormode, et al. ∙

research

∙ 10/06/2022

Federated Boosted Decision Trees with Differential Privacy

There is great demand for scalable, secure, and efficient privacy-preser...

0 Samuel Maddock, et al. ∙

research

∙ 08/18/2022

Verifiable Differential Privacy For When The Curious Become Dishonest

Many applications seek to produce differentially private statistics on s...

0 Ari Biswas, et al. ∙

research

∙ 06/02/2022

Impact of Sampling on Locally Differentially Private Data Collection

With the recent bloom of data, there is a huge surge in threats against ...

0 Sayan Biswas, et al. ∙

research

∙ 01/31/2022

Aggregation and Transformation of Vector-Valued Messages in the Shuffle Model of Differential Privacy

Advances in communications, storage and computational technology allow s...

0 Mary Scott, et al. ∙

research

∙ 01/07/2022

Weighted Random Sampling over Joins

Joining records with all other records that meet a linkage condition can...

0 Michael Shekelyan, et al. ∙

research

∙ 12/10/2021

Sample and Threshold Differential Privacy: Histograms and applications

Federated analytics relies on the collection of accurate statistics abou...

0 Akash Bharadwaj, et al. ∙

research

∙ 12/10/2021

Applying the Shuffle Model of Differential Privacy to Vector Aggregation

In this work we introduce a new protocol for vector aggregation in the c...

0 Mary Scott, et al. ∙

research

∙ 11/15/2021

On the Importance of Difficulty Calibration in Membership Inference Attacks

The vulnerability of machine learning models to membership inference att...

1 Lauren Watson, et al. ∙

research

∙ 09/25/2021

Opacus: User-Friendly Differential Privacy Library in PyTorch

We introduce Opacus, a free, open-source PyTorch library for training de...

0 Ashkan Yousefpour, et al. ∙

research

∙ 08/04/2021

Privacy-Preserving Synthetic Location Data in the Real World

Sharing sensitive data is vital in enabling many modern data analysis an...

0 Teddy Cunningham, et al. ∙

research

∙ 08/04/2021

Real-World Trajectory Sharing with Local Differential Privacy

Sharing trajectories is beneficial for many real-world applications, suc...

0 Teddy Cunningham, et al. ∙

research

∙ 08/03/2021

Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics

Private data generated by edge devices – from smart phones to automotive...

0 Graham Cormode, et al. ∙

research

∙ 04/05/2021

Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming

We study the fundamental problem of frequency estimation under both priv...

0 Ziyue Huang, et al. ∙

research

∙ 03/30/2021

Frequency Estimation under Local Differential Privacy [Experiments, Analysis and Benchmarks]

Private collection of statistics from a large distributed population is ...

0 Graham Cormode, et al. ∙

research

∙ 02/18/2021

Theory meets Practice at the Median: a worst case comparison of relative error quantile algorithms

Estimating the distribution and quantiles of data is a foundational task...

0 Graham Cormode, et al. ∙

research

∙ 01/19/2021

Subspace exploration: Bounds on Projected Frequency Estimation

Given an n × d dimensional dataset A, a projection query specifies a sub...

0 Graham Cormode, et al. ∙

research

∙ 04/03/2020

Relative Error Streaming Quantiles

Approximating ranks, quantiles, and distributions over streaming data is...

0 Graham Cormode, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

research

∙ 11/20/2019

Towards a Theory of Parameterized Streaming Algorithms

Parameterized complexity attempts to give a more fine-grained analysis o...

0 Rajesh Chitnis, et al. ∙

research

∙ 10/30/2019

Iterative Hessian Sketch in Input Sparsity Time

Scalable algorithms to solve optimization and regression tasks even appr...

0 Graham Cormode, et al. ∙

research

∙ 05/13/2019

Streaming Algorithms for Bin Packing and Vector Scheduling

Problems involving the efficient arrangement of simple objects, as captu...

0 Graham Cormode, et al. ∙

research

∙ 05/09/2019

Tight Lower Bound for Comparison-Based Quantile Summaries

Quantiles, such as the median or percentiles, provide concise and useful...

0 Graham Cormode, et al. ∙

research

∙ 12/28/2018

Answering Range Queries Under Local Differential Privacy

Counting the fraction of a population having an input within a specified...

0 Tejas Kulkarni, et al. ∙

research

∙ 12/05/2018

Correlation Clustering in Data Streams

Clustering is a fundamental tool for analyzing large data sets. A rich b...

0 Kook Jin Ahn, et al. ∙

research

∙ 07/22/2018

Independent Sets in Vertex-Arrival Streams

We consider the classic maximal and maximum independent set problems in ...

0 Graham Cormode, et al. ∙

research

∙ 07/06/2018

Leveraging Well-Conditioned Bases: Streaming & Distributed Summaries in Minkowski p-Norms

Work on approximate linear algebra has led to efficient distributed and ...

0 Graham Cormode, et al. ∙

research

∙ 11/08/2017

Marginal Release Under Local Differential Privacy

Many analysis and machine learning tasks require the availability of mar...

0 Tejas Kulkarni, et al. ∙

research

∙ 10/05/2017

Learning Graphical Models from a Distributed Stream

A current challenge for data management systems is to support the constr...

0 Yu Zhang, et al. ∙

research

∙ 10/02/2017

Constrained Differential Privacy for Count Data

Concern about how to aggregate sensitive user data without compromising ...

0 Graham Cormode, et al. ∙

Graham Cormode

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