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03/26/2023
PORE: Provably Robust Recommender Systems against Data Poisoning Attacks
Data poisoning attacks spoof a recommender system to make arbitrary, att...
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03/03/2023
PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees
Point cloud classification is an essential component in many security-cr...
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01/07/2023
REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service
Encoder as a service is an emerging cloud service. Specifically, a servi...
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12/06/2022
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
Classifiers in supervised learning have various security and privacy iss...
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11/15/2022
CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning
Contrastive learning (CL) pre-trains general-purpose encoders using an u...
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10/20/2022
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information
Federated learning is vulnerable to poisoning attacks in which malicious...
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10/03/2022
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
Multi-label classification, which predicts a set of labels for an input,...
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10/02/2022
FLCert: Provably Secure Federated Learning against Poisoning Attacks
Due to its distributed nature, federated learning is vulnerable to poiso...
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07/19/2022
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients
Federated learning (FL) is vulnerable to model poisoning attacks, in whi...
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05/13/2022
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning
Contrastive learning pre-trains an image encoder using a large amount of...
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01/15/2022
StolenEncoder: Stealing Pre-trained Encoders
Pre-trained encoders are general-purpose feature extractors that can be ...
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11/22/2021
Poisoning Attacks to Local Differential Privacy Protocols for Key-Value Data
Local Differential Privacy (LDP) protocols enable an untrusted server to...
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10/28/2021
10 Security and Privacy Problems in Self-Supervised Learning
Self-supervised learning has achieved revolutionary progress in the past...
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08/25/2021
EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning
Given a set of unlabeled images or (image, text) pairs, contrastive lear...
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08/01/2021
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning
Self-supervised learning in computer vision aims to pre-train an image e...
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03/04/2021
PointGuard: Provably Robust 3D Point Cloud Classification
3D point cloud classification has many safety-critical applications such...
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02/03/2021
Provably Secure Federated Learning against Malicious Clients
Federated learning enables clients to collaboratively learn a shared glo...
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12/24/2020
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
Semi-supervised node classification on graph-structured data has many ap...
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12/07/2020
Certified Robustness of Nearest Neighbors against Data Poisoning Attacks
Data poisoning attacks aim to corrupt a machine learning model via modif...
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11/15/2020
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
Top-k predictions are used in many real-world applications such as machi...
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10/26/2020
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes
In the era of deep learning, a user often leverages a third-party machin...
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08/24/2020
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation
Graph neural networks (GNNs) have recently gained much attention for nod...
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08/22/2020
On the Intrinsic Differential Privacy of Bagging
Differentially private machine learning trains models while protecting p...
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08/11/2020
Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks
In a data poisoning attack, an attacker modifies, deletes, and/or insert...
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06/19/2020
Backdoor Attacks to Graph Neural Networks
Node classification and graph classification are two basic graph analyti...
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05/05/2020
Stealing Links from Graph Neural Networks
Graph data, such as social networks and chemical networks, contains a we...
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02/26/2020
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
Backdoor attack is a severe security threat to deep neural networks (DNN...
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02/09/2020
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing
Community detection plays a key role in understanding graph structure. H...
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12/20/2019
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
It is well-known that classifiers are vulnerable to adversarial perturba...
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11/26/2019
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
In federated learning, multiple client devices jointly learn a machine l...
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11/05/2019
Data Poisoning Attacks to Local Differential Privacy Protocols
Local Differential Privacy (LDP) protocols enable an untrusted data coll...
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10/28/2019
IPGuard: Protecting the Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
A deep neural network (DNN) classifier represents a model owner's intell...
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09/23/2019
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
In a membership inference attack, an attacker aims to infer whether a da...
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09/17/2019
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges
As machine learning (ML) becomes more and more powerful and easily acces...
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05/16/2019
Statistical Analysis and Modeling of the Geometry and Topology of Plant Roots
Root is an important organ of a plant since it is responsible for water ...
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12/05/2018
Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge
Estimating frequencies of certain items among a population is a basic st...
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12/04/2018
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation
Many security and privacy problems can be modeled as a graph classificat...
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05/13/2018
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
Users in various web and mobile applications are vulnerable to attribute...
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03/12/2018