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Intel software guard extensions applications: A survey
Data confidentiality is a central concern in modern computer systems and services, as
sensitive data from users and companies are being increasingly delegated to such systems …
sensitive data from users and companies are being increasingly delegated to such systems …
Elf: accelerate high-resolution mobile deep vision with content-aware parallel offloading
As mobile devices continuously generate streams of images and videos, a new class of
mobile deep vision applications are rapidly emerging, which usually involve running deep …
mobile deep vision applications are rapidly emerging, which usually involve running deep …
Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey
With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous
networks, mobile broadband users are generating massive volumes of data that undergo …
networks, mobile broadband users are generating massive volumes of data that undergo …
SPDS: A secure and auditable private data sharing scheme for smart grid based on blockchain
The exponential growth of data generated from increasing smart meters and smart
appliances brings about huge potentials for more efficient energy production, pricing, and …
appliances brings about huge potentials for more efficient energy production, pricing, and …
Fedgraph: Federated graph learning with intelligent sampling
Federated learning has attracted much research attention due to its privacy protection in
distributed machine learning. However, existing work of federated learning mainly focuses …
distributed machine learning. However, existing work of federated learning mainly focuses …
Melon: Breaking the memory wall for resource-efficient on-device machine learning
On-device learning is a promising technique for emerging privacy-preserving machine
learning paradigms. However, through quantitative experiments, we find that commodity …
learning paradigms. However, through quantitative experiments, we find that commodity …
No privacy left outside: On the (in-) security of tee-shielded dnn partition for on-device ml
On-device ML introduces new security challenges: DNN models become white-box
accessible to device users. Based on white-box information, adversaries can conduct …
accessible to device users. Based on white-box information, adversaries can conduct …
DarKnight: An accelerated framework for privacy and integrity preserving deep learning using trusted hardware
Privacy and security-related concerns are growing as machine learning reaches diverse
application domains. The data holders want to train or infer with private data while exploiting …
application domains. The data holders want to train or infer with private data while exploiting …
Adversarial attacks against lidar semantic segmentation in autonomous driving
Today, most autonomous vehicles (AVs) rely on LiDAR (Light Detection and Ranging)
perception to acquire accurate information about their immediate surroundings. In LiDAR …
perception to acquire accurate information about their immediate surroundings. In LiDAR …