20 citations · 30 across the 10 of their papers we have counts for
10 papers · 1 filter
FACOS: Enabling Privacy Protection Through Fine-Grained Access Control with On-chain and Off-chain System
Chao Liu, Cankun Hou, Tianyu Jiang +3
Data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and…
Fluent: Round-efficient Secure Aggregation for Private Federated Learning
Xincheng Li, Jianting Ning, Geong Sen Poh +3
Federated learning (FL) facilitates collaborative training of machine learning models among a large number of clients while safeguarding the privacy of their local datasets. Howeve…
Efficiency Boosting of Secure Cross-platform Recommender Systems over Sparse Data
Hao Ren, Guowen Xu, Tianwei Zhang +4
Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensi…
New Secure Sparse Inner Product with Applications to Machine Learning
Guowen Xu, Shengmin Xu, Jianting Ning +4
Sparse inner product (SIP) has the attractive property of overhead being dominated by the intersection of inputs between parties, independent of the actual input size. It has intri…
VerifyML: Obliviously Checking Model Fairness Resilient to Malicious Model Holder
Guowen Xu, Xingshuo Han, Gelei Deng +5
In this paper, we present VerifyML, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model. VerifyML is generic and is immune to a…
E-Tenon: An Efficient Privacy-Preserving Secure Open Data Sharing Scheme for EHR System
Zhihui Lin, Prosanta Gope, Jianting Ning +1
The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare-industry. In many cases, patients need to s…
SIMC 2.0: Improved Secure ML Inference Against Malicious Clients
Guowen Xu, Xingshuo Han, Tianwei Zhang +5
In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the serv…
A Survey of Privacy-Preserving Techniques for Encrypted Traffic Inspection over Network Middleboxes
Geong Sen Poh, Dinil Mon Divakaran, Hoon Wei Lim +2
Middleboxes in a computer network system inspect and analyse network traffic to detect malicious communications, monitor system performance and provide operational services. Howeve…
Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning
Hangyu Zhu, Rui Wang, Yaochu Jin +2
Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a t…
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space
Ao Liu, Yunpeng Wang, Tao Li
In the encrypted network traffic intrusion detection, deep learning based schemes have attracted lots of attention. However, in real-world scenarios, data is often insufficient (fe…