1

NetShaper: A Differentially Private Network Side-Channel Mitigation System
The widespread adoption of encryption in network protocols has significantly improved the overall security of many Internet …
Preprint PDF Cite Project
NetShaper: A Differentially Private Network Side-Channel Mitigation System
Toward Automated DNS Tampering Detection Using Machine Learning
Abstract: DNS manipulation is one of the most prevalent and effective techniques for censoring Internet access and interfering with users’ online activities worldwide. Reliable detection of DNS tampering is crucial, but challenging due to evolving censorship tactics and the lack of complete ground truth data.
PDF Cite
Toward Automated DNS Tampering Detection Using Machine Learning
KAIROS: Practical Intrusion Detection and Investigation using Whole-system Provenance
Provenance graphs are structured audit logs that describe the history of a system’s execution. Recent studies have explored a …
PDF Cite Project
KAIROS: Practical Intrusion Detection and Investigation using Whole-system Provenance
Turbo: Effective Caching in Differentially-Private Databases
The Adam optimizer is a popular choice in contemporary deep learning due to its strong empirical performance. However we observe that …
Preprint Cite
Turbo: Effective Caching in Differentially-Private Databases
Turbo: Effective Caching in Differentially-Private Databases
Differentially-private (DP) databases allow for privacy-preserving analytics over sensitive datasets or data streams. In these systems, …
Preprint Cite
Turbo: Effective Caching in Differentially-Private Databases
Unleashing Unprivileged eBPF Potential with Dynamic Sandboxing
For safety reasons, unprivileged users today have only limited ways to customize the kernel through the extended Berkeley Packet Filter …
PDF Cite Project
Unleashing Unprivileged eBPF Potential with Dynamic Sandboxing
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
We develop a new, principled algorithm for estimating the contribution of training data points to the behavior of a deep learning …
Preprint Cite
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation
We observe that the traditional use of DP with the Adam optimizer introduces a bias in the second moment estimation, due to the …
Preprint Cite
DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation
Pacer: Comprehensive Network Side-Channel Mitigation in the Cloud
Network side channels (NSCs) leak secrets through packet timing and packet sizes. They are of particular concern in public IaaS Clouds, …
PDF Cite Code
Pacer: Comprehensive Network Side-Channel Mitigation in the Cloud
Secure Namespaced Kernel Audit for Containers
Despite the wide usage of container-based cloud computing, container auditing for security analysis relies mostly on built-in host …
PDF Cite
Secure Namespaced Kernel Audit for Containers