66 citations · 129 across the 11 of their papers we have counts for
12 papers
VerIDeep: Verifying Integrity of Deep Neural Networks through Sensitive-Sample Fingerprinting
Zecheng He, Tianwei Zhang, Ruby B. Lee
Deep learning has become popular, and numerous cloud-based services are provided to help customers develop and deploy deep learning applications. Meanwhile, various attack techniqu…
Privacy-preserving Machine Learning through Data Obfuscation
Tianwei Zhang, Zecheng He, Ruby B. Lee
As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications.…
Practical and Scalable Security Verification of Secure Architectures
Jakub Szefer, Tianwei Zhang, Ruby B. Lee
We present a new and practical framework for security verification of secure architectures. Specifically, we break the verification task into external verification and internal ver…
Time Series Segmentation through Automatic Feature Learning
Wei-Han Lee, Jorge Ortiz, Bongjun Ko +1
Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and ac…
Blind De-anonymization Attacks using Social Networks
Wei-Han Lee, Changchang Liu, Shouling Ji +2
It is important to study the risks of publishing privacy-sensitive data. Even if sensitive identities (e.g., name, social security number) were removed and advanced data perturbati…
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Wei-Han Lee, Ruby B. Lee
Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and service…
Secure Pick Up: Implicit Authentication When You Start Using the Smartphone
Wei-Han Lee, Xiaochen Liu, Yilin Shen +2
We propose Secure Pick Up (SPU), a convenient, lightweight, in-device, non-intrusive and automatic-learning system for smartphone user authentication. Operating in the background,…
Quantification of De-anonymization Risks in Social Networks
Wei-Han Lee, Changchang Liu, Shouling Ji +2
The risks of publishing privacy-sensitive data have received considerable attention recently. Several de-anonymization attacks have been proposed to re-identify individuals even if…
Implicit Sensor-based Authentication of Smartphone Users with Smartwatch
Wei-Han Lee, Ruby Lee
Smartphones are now frequently used by end-users as the portals to cloud-based services, and smartphones are easily stolen or co-opted by an attacker. Beyond the initial log-in mec…
Multi-sensor authentication to improve smartphone security
Wei-Han Lee, Ruby Lee
The widespread use of smartphones gives rise to new security and privacy concerns. Smartphone thefts account for the largest percentage of thefts in recent crime statistics. Using…
Cloud Server Benchmarks for Performance Evaluation of New Hardware Architecture
Hao Wu, Fangfei Liu, Ruby B. Lee
Adding new hardware features to a cloud computing server requires testing both the functionalities and the performance of the new hardware mechanisms. However, commonly used cloud…
Memory DoS Attacks in Multi-tenant Clouds: Severity and Mitigation
Tianwei Zhang, Yinqian Zhang, Ruby B. Lee
In cloud computing, network Denial of Service (DoS) attacks are well studied and defenses have been implemented, but severe DoS attacks on a victim's working memory by a single hos…