174 citations · 393 across the 18 of their papers we have counts for
18 papers
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice
Andrea Gadotti, Florimond Houssiau, Meenatchi Sundaram Muthu Selva Annamalai +1
Behavioral data generated by users' devices, ranging from emoji use to pages visited, are collected at scale to improve apps and services. These data, however, contain fine-grained…
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information
Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang +1
Federated learning is vulnerable to poisoning attacks in which malicious clients poison the global model via sending malicious model updates to the server. Existing defenses focus…
FLCert: Provably Secure Federated Learning against Poisoning Attacks
Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia +1
Due to its distributed nature, federated learning is vulnerable to poisoning attacks, in which malicious clients poison the training process via manipulating their local training d…
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients
Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia +1
Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing…
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients
Xiaoyu Cao, Neil Zhenqiang Gong
Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realis…
Provably Secure Federated Learning against Malicious Clients
Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
Federated learning enables clients to collaboratively learn a shared global model without sharing their local training data with a cloud server. However, malicious clients can corr…
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu +1
Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious. The key idea of existing Byz…
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
Jinyuan Jia, Binghui Wang, Xiaoyu Cao +2
Top-k predictions are used in many real-world applications such as machine learning as a service, recommender systems, and web searches. -norm adversarial perturbation char…
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation
Binghui Wang, Jinyuan Jia, Xiaoyu Cao +1
Graph neural networks (GNNs) have recently gained much attention for node and graph classification tasks on graph-structured data. However, multiple recent works showed that an att…
Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks
Jinyuan Jia, Xiaoyu Cao, Neil Zhenqiang Gong
In a \emph{data poisoning attack}, an attacker modifies, deletes, and/or inserts some training examples to corrupt the learnt machine learning model. \emph{Bootstrap Aggregating (b…
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
Binghui Wang, Xiaoyu Cao, Jinyuan jia +1
Backdoor attack is a severe security threat to deep neural networks (DNNs). We envision that, like adversarial examples, there will be a cat-and-mouse game for backdoor attacks, i.…
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing
Jinyuan Jia, Binghui Wang, Xiaoyu Cao +1
Community detection plays a key role in understanding graph structure. However, several recent studies showed that community detection is vulnerable to adversarial structural pertu…
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
Jinyuan Jia, Xiaoyu Cao, Binghui Wang +1
It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived.…
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia +1
In federated learning, multiple client devices jointly learn a machine learning model: each client device maintains a local model for its local training dataset, while a master dev…
Data Poisoning Attacks to Local Differential Privacy Protocols
Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
Local Differential Privacy (LDP) protocols enable an untrusted data collector to perform privacy-preserving data analytics. In particular, each user locally perturbs its data to pr…
IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
A deep neural network (DNN) classifier represents a model owner's intellectual property as training a DNN classifier often requires lots of resource. Watermarking was recently prop…
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification
Xiaoyu Cao, Neil Zhenqiang Gong
Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However,…
PIANO: Proximity-based User Authentication on Voice-Powered Internet-of-Things Devices
Neil Zhenqiang Gong, Altay Ozen, Yu Wu +5
Voice is envisioned to be a popular way for humans to interact with Internet-of-Things (IoT) devices. We propose a proximity-based user authentication method (called PIANO) for acc…