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10/28/2022
Differentially Private CutMix for Split Learning with Vision Transformer
Recently, vision transformer (ViT) has started to outpace the convention...
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08/08/2022
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices
Differential privacy has become crucial in the real-world deployment of ...
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07/08/2022
Private independence testing across two parties
We introduce π-test, a privacy-preserving algorithm for testing statisti...
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07/01/2022
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
This article seeks for a distributed learning solution for the visual tr...
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01/25/2022
The Privacy-Welfare Trade-off: Effects of Differential Privacy on Influence Welfare in Social Choice
This work studies a fundamental trade-off between privacy and welfare in...
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12/11/2021
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
In recent years, there have been great advances in the field of decentra...
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10/19/2021
Private measurement of nonlinear correlations between data hosted across multiple parties
We introduce a differentially private method to measure nonlinear correl...
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08/19/2021
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity
Classes of set functions along with a choice of ground set are a bedrock...
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05/02/2021
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning
Wireless channels can be inherently privacy-preserving by distorting the...
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02/22/2021
Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval
Differential Privacy offers strong guarantees such as immutable privacy ...
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12/20/2020
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks
Recent deep learning models have shown remarkable performance in image c...
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08/20/2020
NoPeek: Information leakage reduction to share activations in distributed deep learning
For distributed machine learning with sensitive data, we demonstrate how...
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08/15/2020
PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic
Several contact tracing solutions have been proposed and implemented all...
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08/07/2020
SplitNN-driven Vertical Partitioning
In this work, we introduce SplitNN-driven Vertical Partitioning, a confi...
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07/06/2020
Splintering with distributions: A stochastic decoy scheme for private computation
Performing computations while maintaining privacy is an important proble...
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06/04/2020
COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers
A number of groups, from governments to non-profits, have quickly acted ...
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04/25/2020
Privacy in Deep Learning: A Survey
The ever-growing advances of deep learning in many areas including visio...
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03/31/2020
Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy
Governments and researchers around the world are implementing digital co...
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03/31/2020
Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic
Governments and researchers around the world are implementing digital co...
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03/19/2020
Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic
Containment, the key strategy in quickly halting an epidemic, requires r...
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12/27/2019
Split Learning for collaborative deep learning in healthcare
Shortage of labeled data has been holding the surge of deep learning in ...
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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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10/09/2019
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
Recently, there has been the development of Split Learning, a framework ...
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10/05/2019
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
In this work we introduce ExpertMatcher, a method for automating deep le...
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09/27/2019
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest
In this paper we investigate the usage of adversarial perturbations for ...
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09/18/2019
Detailed comparison of communication efficiency of split learning and federated learning
We compare communication efficiencies of two compelling distributed mach...
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05/14/2019
Data Markets to support AI for All: Pricing, Valuation and Governance
We discuss a data market technique based on intrinsic (relevance and uni...
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12/08/2018
No Peek: A Survey of private distributed deep learning
We survey distributed deep learning models for training or inference wit...
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12/06/2018
A Review of Homomorphic Encryption Libraries for Secure Computation
In this paper we provide a survey of various libraries for homomorphic e...
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12/03/2018
Split learning for health: Distributed deep learning without sharing raw patient data
Can health entities collaboratively train deep learning models without s...
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12/28/2016
Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in high-dimensional data
We provide a way to infer about existence of topological circularity in ...
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01/03/2016
Supervised Dimensionality Reduction via Distance Correlation Maximization
In our work, we propose a novel formulation for supervised dimensionalit...
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06/11/2013