A survey on differential privacy for unstructured data content
Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …
generated and shared, and it is a challenge to protect sensitive personal information in …
A survey on intelligent Internet of Things: Applications, security, privacy, and future directions
The rapid advances in the Internet of Things (IoT) have promoted a revolution in
communication technology and offered various customer services. Artificial intelligence (AI) …
communication technology and offered various customer services. Artificial intelligence (AI) …
Piranha: A {GPU} platform for secure computation
Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine
learning (ML). However, secure training of large-scale ML models currently requires a …
learning (ML). However, secure training of large-scale ML models currently requires a …
Orca: Fss-based secure training and inference with gpus
Secure Two-party Computation (2PC) allows two parties to compute any function on their
private inputs without revealing their inputs to each other. In the offline/on-line model for …
private inputs without revealing their inputs to each other. In the offline/on-line model for …
" Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security Conferences
D Olszewski, A Lu, C Stillman, K Warren… - Proceedings of the …, 2023 - dl.acm.org
Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …
seemingly contradictory results and expands the boundaries of known discoveries …
Sigma: Secure gpt inference with function secret sharing
K Gupta, N Jawalkar, A Mukherjee… - Cryptology ePrint …, 2023 - eprint.iacr.org
Abstract Secure 2-party computation (2PC) enables secure inference that offers protection
for both proprietary machine learning (ML) models and sensitive inputs to them. However …
for both proprietary machine learning (ML) models and sensitive inputs to them. However …
PECAM: Privacy-enhanced video streaming and analytics via securely-reversible transformation
As Video Streaming and Analytics (VSA) systems become increasingly popular, serious
privacy concerns have risen on exposing too much unnecessary private information to the …
privacy concerns have risen on exposing too much unnecessary private information to the …
Snoopy: Surpassing the scalability bottleneck of oblivious storage
Existing oblivious storage systems provide strong security by hiding access patterns, but do
not scale to sustain high throughput as they rely on a central point of coordination. To …
not scale to sustain high throughput as they rely on a central point of coordination. To …
Gemel: Model Merging for {Memory-Efficient},{Real-Time} Video Analytics at the Edge
Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth
overheads and privacy violations, but in doing so, face an ever-growing resource tension …
overheads and privacy violations, but in doing so, face an ever-growing resource tension …
Honeycomb: Secure and Efficient {GPU} Executions via Static Validation
Graphics Processing Units (GPUs) unlock emerging use cases like large language models
and autonomous driving. They process a large amount of sensitive data, where security is of …
and autonomous driving. They process a large amount of sensitive data, where security is of …