64 citations · 233 across the 32 of their papers we have counts for
24 papers · 1 filter
CDN Tsunami: Exploiting HTTP/3-HTTP/1.1 Conversion for DoS Attacks
Ziyu Lin, Tianlong Su, Yingjie Lin +4
Content Delivery Networks (CDNs) provide high availability, accelerate content delivery for their host websites, but are also vulnerable to different types of Denial-of-Service (Do…
Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain
Weijie Wang, Jinwen Liang, Chuan Zhang +3
Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments. Meanwhile, emerging blockchain technolog…
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
Zhichao You, Xuewen Dong, Shujun Li +3
Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effec…
MORSE: An Efficient Homomorphic Secret Sharing Scheme Enabling Non-Linear Operation
Weiquan Deng, Bowen Zhao, Yang Xiao +3
Homomorphic secret sharing (HSS) enables two servers to locally perform functions on encrypted data directly and obtain the results in the form of shares. A Paillier-based HSS solu…
Detecting and Measuring Security Implications of Entangled Domain Verification in CDN
Ziyu Lin, Zhiwei Lin, Run Guo +6
Content Delivery Networks (CDNs) offer a protection layer for enhancing the security of websites. However, a significant security flaw named Absence of Domain Verification (DVA) ha…
Unveiling the Bandwidth Nightmare: CDN Compression Format Conversion Attacks
Ziyu Lin, Zhiwei Lin, Ximeng Liu +2
Content Delivery Networks (CDNs) are designed to enhance network performance and protect against web attack traffic for their hosting websites. And the HTTP compression request mec…
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images
Mingyuan Fan, Yang Liu, Cen Chen +1
Adversarial attack has garnered considerable attention due to its profound implications for the secure deployment of robots in sensitive security scenarios. To potentially push for…
A fully decentralized auditing approach for edge computing: A Game-Theoretic Perspective
Zahra Seyedi, Farhad Rahmati, Mohammad Ali +1
Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. Ho…
Federated Online/Offline Remote Data Inspection for Distributed Edge Computing
Mohammad Ali, Ximeng Liu
In edge computing environments, app vendors can cache their data to be shared with their users in many geographically distributed edge servers. However, the cached data is particul…
SoK: Fully Homomorphic Encryption Accelerators
Junxue Zhang, Xiaodian Cheng, Liu Yang +3
Fully Homomorphic Encryption~(FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the un…
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring
Wenyuan Yang, Shuo Shao, Yue Yang +5
Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protecti…
Federated Learning based on Defending Against Data Poisoning Attacks in IoT
Jiayin Li, Wenzhong Guo, Xingshuo Han +2
The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but the data privacy and security exposure in IoT devices, especially in the…
Backdoor Defense with Machine Unlearning
Yang Liu, Mingyuan Fan, Cen Chen +4
Backdoor injection attack is an emerging threat to the security of neural networks, however, there still exist limited effective defense methods against the attack. In this paper,…
Too Expensive to Attack: Enlarge the Attack Expense through Joint Defense at the Edge
Jianhua Li, Ximeng Liu, Jiong JIn +1
The distributed denial of service (DDoS) attack is detrimental to businesses and individuals as people are heavily relying on the Internet. Due to remarkable profits, crackers favo…
Too Expensive to Attack: A Joint Defense Framework to Mitigate Distributed Attacks for the Internet of Things Grid
Jianhua Li, Ximeng Liu, Jiong Jin +1
The distributed denial of service (DDoS) attack is detrimental to businesses and individuals as we are heavily relying on the Internet. Due to remarkable profits, crackers favor DD…
Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloud
Zhuoran Ma, Jianfeng Ma, Yinbin Miao +3
Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e. Data Islands (DIs)). Ho…
Privacy-preserving Medical Treatment System through Nondeterministic Finite Automata
Yang Yang, Robert H. Deng, Ximeng Liu +4
In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medica…
VerifyTL: Secure and Verifiable Collaborative Transfer Learning
Zhuoran Ma, Jianfeng Ma, Yinbin Miao +4
Getting access to labelled datasets in certain sensitive application domains can be challenging. Hence, one often resorts to transfer learning to transfer knowledge learned from a…
Cloud-based Federated Boosting for Mobile Crowdsensing
Zhuzhu Wang, Yilong Yang, Yang Liu +3
The application of federated extreme gradient boosting to mobile crowdsensing apps brings several benefits, in particular high performance on efficiency and classification. However…
Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning
Zhuo Ma, Haoran Ge, Zhuzhu Wang +2
Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts fo…
Revocable Federated Learning: A Benchmark of Federated Forest
Yang Liu, Zhuo Ma, Ximeng Liu +3
A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the…
Lightning-Fast and Privacy-Preserving Outsourced Computation in the Cloud
Ximeng Liu, Robert H. Deng, Pengfei Wu +1
In this paper, we propose a framework for lightning-fast privacy-preserving outsourced computation framework in the cloud, which we refer to as LightCom. Using LightCom, a user can…
Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing
Yang Liu, Zhuo Ma, Ximeng Liu +3
Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE)…
Achieving Trust-Based and Privacy-Preserving Customer Selection in Ubiquitous Computing
Chuan Zhang, Liehuang Zhu, Chang Xu +4
The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime & anywhere computing possible. In such a ubiqu…