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04/10/2023
PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy
Answering database queries while preserving privacy is an important prob...
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02/04/2023
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting
Data sketching is a critical tool for distinct counting, enabling multis...
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01/05/2023
Streaming Zero-Knowledge Proofs
We initiate the study of zero-knowledge proofs for data streams. Streami...
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11/18/2022
The communication cost of security and privacy in federated frequency estimation
We consider the federated frequency estimation problem, where each user ...
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10/22/2022
Federated Calibration and Evaluation of Binary Classifiers
We address two major obstacles to practical use of supervised classifier...
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10/06/2022
Federated Boosted Decision Trees with Differential Privacy
There is great demand for scalable, secure, and efficient privacy-preser...
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08/18/2022
Verifiable Differential Privacy For When The Curious Become Dishonest
Many applications seek to produce differentially private statistics on s...
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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 ...
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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...
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01/07/2022
Weighted Random Sampling over Joins
Joining records with all other records that meet a linkage condition can...
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12/10/2021
Sample and Threshold Differential Privacy: Histograms and applications
Federated analytics relies on the collection of accurate statistics abou...
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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...
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11/15/2021
On the Importance of Difficulty Calibration in Membership Inference Attacks
The vulnerability of machine learning models to membership inference att...
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09/25/2021
Opacus: User-Friendly Differential Privacy Library in PyTorch
We introduce Opacus, a free, open-source PyTorch library for training de...
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08/04/2021
Privacy-Preserving Synthetic Location Data in the Real World
Sharing sensitive data is vital in enabling many modern data analysis an...
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08/04/2021
Real-World Trajectory Sharing with Local Differential Privacy
Sharing trajectories is beneficial for many real-world applications, suc...
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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...
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04/05/2021
Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming
We study the fundamental problem of frequency estimation under both priv...
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03/30/2021
Frequency Estimation under Local Differential Privacy [Experiments, Analysis and Benchmarks]
Private collection of statistics from a large distributed population is ...
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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...
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01/19/2021
Subspace exploration: Bounds on Projected Frequency Estimation
Given an n × d dimensional dataset A, a projection query specifies a sub...
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04/03/2020
Relative Error Streaming Quantiles
Approximating ranks, quantiles, and distributions over streaming data is...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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11/20/2019
Towards a Theory of Parameterized Streaming Algorithms
Parameterized complexity attempts to give a more fine-grained analysis o...
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10/30/2019
Iterative Hessian Sketch in Input Sparsity Time
Scalable algorithms to solve optimization and regression tasks even appr...
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05/13/2019
Streaming Algorithms for Bin Packing and Vector Scheduling
Problems involving the efficient arrangement of simple objects, as captu...
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05/09/2019
Tight Lower Bound for Comparison-Based Quantile Summaries
Quantiles, such as the median or percentiles, provide concise and useful...
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12/28/2018
Answering Range Queries Under Local Differential Privacy
Counting the fraction of a population having an input within a specified...
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12/05/2018
Correlation Clustering in Data Streams
Clustering is a fundamental tool for analyzing large data sets. A rich b...
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07/22/2018
Independent Sets in Vertex-Arrival Streams
We consider the classic maximal and maximum independent set problems in ...
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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 ...
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11/08/2017
Marginal Release Under Local Differential Privacy
Many analysis and machine learning tasks require the availability of mar...
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10/05/2017
Learning Graphical Models from a Distributed Stream
A current challenge for data management systems is to support the constr...
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10/02/2017