56 citations · 197 across the 22 of their papers we have counts for
7 papers · 1 filter
Privacy-Preserving Synthetic Location Data in the Real World
Teddy Cunningham, Graham Cormode, Hakan Ferhatosmanoglu
Sharing sensitive data is vital in enabling many modern data analysis and machine learning tasks. However, current methods for data release are insufficiently accurate or granular…
Real-World Trajectory Sharing with Local Differential Privacy
Teddy Cunningham, Graham Cormode, Hakan Ferhatosmanoglu +1
Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel…
Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics
Graham Cormode, Igor L. Markov
Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of so…
Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming
Ziyue Huang, Yuan Qiu, Ke Yi +1
We study the fundamental problem of frequency estimation under both privacy and communication constraints, where the data is distributed among parties. We consider two applicat…
Frequency Estimation under Local Differential Privacy [Experiments, Analysis and Benchmarks]
Graham Cormode, Samuel Maddock, Carsten Maple
Private collection of statistics from a large distributed population is an important problem, and has led to large scale deployments from several leading technology companies. The…
Theory meets Practice at the Median: a worst case comparison of relative error quantile algorithms
Graham Cormode, Abhinav Mishra, Joseph Ross +1
Estimating the distribution and quantiles of data is a foundational task in data mining and data science. We study algorithms which provide accurate results for extreme quantile qu…