Search Results for author:
Found 43 papers, 13 papers with code
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Federated Graph Classification over Non-IID Graphs
Federated learning has emerged as an important paradigm for training machine learning models in different domains.
Regularizing Neural Networks via Minimizing Hyperspherical Energy
Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.
Orthogonal Over-Parameterized Training
The inductive bias of a neural network is largely determined by the architecture and the training algorithm.
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
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
Learning with Hyperspherical Uniformity
Due to the over-parameterization nature, neural networks are a powerful tool for nonlinear function approximation.
Projected Federated Averaging with Heterogeneous Differential Privacy
The challenge is how to use such information without biasing the joint model.
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
Classification Auto-Encoder based Detector against Diverse Data Poisoning Attacks
Poisoning attacks are a category of adversarial machine learning threats in which an adversary attempts to subvert the outcome of the machine learning systems by injecting crafted data into training data set, thus increasing the machine learning model's test error.
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
The key idea is to decouple the feature projection and message passing via a DP PageRank algorithm which learns the structure information and uses the top-$K$ neighbors determined by the PageRank for feature aggregation.
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.
Aesthetic-based Clothing Recommendation
Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner.
Visually-aware Recommendation with Aesthetic Features
While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect.
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.
Spatio-Temporal Tensor Sketching via Adaptive Sampling
In this paper, we propose SkeTenSmooth, a novel tensor factorization framework that uses adaptive sampling to compress the tensor in a temporally streaming fashion and preserves the underlying global structure.
Generative Fairness Teaching
We propose a generative fairness teaching framework that provides a model with not only real samples but also synthesized samples to compensate the data biases during training.
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain
In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution.
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
Modern deep learning algorithms have the highest potential of such leakage due to complexity of the models.
CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data
Samples with ground truth labels may not always be available in numerous domains.
View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data
Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view.
PAM: Understanding Product Images in Cross Product Category Attribute Extraction
Understanding product attributes plays an important role in improving online shopping experience for customers and serves as an integral part for constructing a product knowledge graph.
Certified Robustness to Word Substitution Attack with Differential Privacy
The robustness and security of natural language processing (NLP) models are significantly important in real-world applications.
RobustFed: A Truth Inference Approach for Robust Federated Learning
Federated learning is a prominent framework that enables clients (e. g., mobile devices or organizations) to train a collaboratively global model under a central server's orchestration while keeping local training datasets' privacy.
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks
Adversarial data examples have drawn significant attention from the machine learning and security communities.
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling
Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy.
Temporal Network Embedding via Tensor Factorization
Representation learning on static graph-structured data has shown a significant impact on many real-world applications.
Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks
Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients' history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts.
Two Birds, One Stone: Achieving both Differential Privacy and Certified Robustness for Pre-trained Classifiers via Input Perturbation
To solve this challenge, a reconstruction network is built before the public pre-trained classifiers to offer certified robustness and defend against adversarial examples through input perturbation.
Bit-aware Randomized Response for Local Differential Privacy in Federated Learning
In this paper, we develop BitRand, a bit-aware randomized response algorithm, to preserve local differential privacy (LDP) in federated learning (FL).
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy
In this paper, we develop a framework called PRECAD, which simultaneously achieves differential privacy (DP) and enhances robustness against model poisoning attacks with the help of cryptography.
Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries
Among the modalities of medical data, the clinical summaries have higher risks to be attacked because they are generated by third-party companies.
Multi-View Active Learning for Short Text Classification in User-Generated Data
Our model has three novelties: 1) It is the first approach to employ multi-view active learning in this domain.
MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning
By supervising the tensor factorization with downstream prediction tasks and leveraging information from multiple related predictive tasks, MULTIPAR can yield not only more meaningful phenotypes but also better predictive performance for downstream tasks.
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels
PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on.
Wasserstein Adversarial Examples on Univariant Time Series Data
In this work, we propose adversarial examples in the Wasserstein space for time series data for the first time and utilize Wasserstein distance to bound the perturbation between normal examples and adversarial examples.
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model
In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model.
MUter: Machine Unlearning on Adversarially Trained Models
However, existing methods focus exclusively on unlearning from standard training models and do not apply to adversarial training models (ATMs) despite their popularity as effective defenses against adversarial examples.
Does Differential Privacy Prevent Backdoor Attacks in Practice?
We also explore the role of different components of DP algorithms in defending against backdoor attacks and will show that PATE is effective against these attacks due to the bagging structure of the teacher models it employs.
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
Machine unlearning aims to eliminate the influence of a subset of training samples (i. e., unlearning samples) from a trained model.
Cross-silo Federated Learning with Record-level Personalized Differential Privacy
Federated learning enhanced by differential privacy has emerged as a popular approach to better safeguard the privacy of client-side data by protecting clients' contributions during the training process.
