Search Results for author:
Found 19 papers, 10 papers with code
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
In this study, we present Uldp-FL, a novel FL framework designed to guarantee user-level DP in cross-silo FL where a single user's data may belong to multiple silos.
Local Differential Privacy Image Generation Using Flow-based Deep Generative Models
To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images.
BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information
Time is an important aspect of documents and is used in a range of NLP and IR tasks.
Network Shuffling: Privacy Amplification via Random Walks
However, introducing a centralized entity to the originally local privacy model loses some appeals of not having any centralized entity as in local differential privacy.
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification
First, we theoretically analyze the leakage of memory access patterns, revealing the risk of sparsified gradients, which are commonly used in FL to enhance communication efficiency and model accuracy.
ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections
In the last few years, open-domain question answering (ODQA) has advanced rapidly due to the development of deep learning techniques and the availability of large-scale QA datasets.
Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries
In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS).
Understanding the Interplay between Privacy and Robustness in Federated Learning
Federated Learning (FL) is emerging as a promising paradigm of privacy-preserving machine learning, which trains an algorithm across multiple clients without exchanging their data samples.
FL-Market: Trading Private Models in Federated Learning
FL-Market decouples ML from the need to centrally gather training data on the broker's side using federated learning, an emerging privacy-preserving ML paradigm in which data owners collaboratively train an ML model by uploading local gradients (to be aggregated into a global gradient for model updating).
Quantifying the Privacy-Utility Trade-offs in COVID-19 Contact Tracing Apps
How to contain the spread of the COVID-19 virus is a major concern for most countries.
Computers and Society Cryptography and Security 68P27 H.3.4
FLAME: Differentially Private Federated Learning in the Shuffle Model
In this work, by leveraging the \textit{privacy amplification} effect in the recently proposed shuffle model of differential privacy, we achieve the best of two worlds, i. e., accuracy in the curator model and strong privacy without relying on any trusted party.
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
The state-of-the-art approach for this problem is to build a generative model under differential privacy, which offers a rigorous privacy guarantee.
PGLP: Customizable and Rigorous Location Privacy through Policy Graph
Third, we design a private location trace release framework that pipelines the detection of location exposure, policy graph repair, and private trajectory release with customizable and rigorous location privacy.
Cryptography and Security Computers and Society
PANDA: Policy-aware Location Privacy for Epidemic Surveillance
Our system has three primary functions for epidemic surveillance: location monitoring, epidemic analysis, and contact tracing.
Databases Cryptography and Security
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing
In this paper, we propose a novel news bias dataset which facilitates the development and evaluation of approaches for detecting subtle bias in news articles and for understanding the characteristics of biased sentences.
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
To prevent privacy leakages from gradients that are calculated on users' sensitive data, local differential privacy (LDP) has been considered as a privacy guarantee in federated SGD recently.
Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training
Extensive experiments are conducted with 8K images, among which 1. 5K image are randomly picked for evaluation.
Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations
Our analysis reveals that, the event-level privacy loss of a DP mechanism may \textit{increase over time}.
Databases
Quantifying Differential Privacy under Temporal Correlations
Our analysis reveals that the privacy leakage of a DP mechanism may accumulate and increase over time.
Databases Cryptography and Security
