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Innovative blockchain architecture that significantly increases transaction processing speed without modifying proof-of-work consensus, using novel data structures and I/O optimization techniques.
Efficient coverage measurement technique for fuzzing, reducing overhead in runtime collection and post-execution processing. Improves fuzzer performance and coverage exploration.
Explore SGXDedup, a novel approach leveraging Intel SGX to accelerate encrypted deduplication for outsourced storage, offering significant performance improvements while maintaining security and efficiency.
Explores vulnerabilities in low Earth orbit satellite networks, introducing ICARUS attacks that exploit global accessibility, low-latency constraints, and network predictability to disrupt communications over large areas.
Innovative techniques for reducing memory usage in streaming graph processing systems, enabling analysis of larger dynamic graphs while maintaining performance and accuracy.
Explore Avocado, a secure in-memory distributed storage system for untrusted cloud environments, offering strong security, fault-tolerance, consistency, and performance through innovative design and implementation.
Automated system for distributed inference serving, optimizing model selection and resource allocation to meet performance and accuracy requirements while reducing costs and complexity for developers.
Scalable, adaptive edge stream processing engine for IoT applications, enabling fast processing of concurrent queries in dynamic environments through dynamic dataflow abstraction and P2P overlay networks.
Innovative system for metadata-private voice communication, scaling to 32K users with low latency. Utilizes untrusted infrastructure and novel mailbox assignment, improving on existing solutions for secure communication.
Explore TEMERAIRE, a hugepage-aware memory allocator enhancing TCMALLOC to reduce CPU overheads, improve application performance, and optimize fleet efficiency in warehouse-scale computing.
Efficient federated learning system that improves model training and testing by intelligently selecting participants based on data utility and device capabilities, enhancing accuracy and performance.
Optimizing tensor programs with partially equivalent transformations, improving DNN efficiency by up to 2.5×. Introduces PET framework for automated corrections and theoretical foundations for equivalence examination.
Explore a novel Linux kernel storage stack architecture achieving microsecond-scale latency and high throughput, adapting network switch techniques for improved performance without application modifications.
Innovative hardware-software design for scalable, dynamic secure memory in enclaves, featuring Guarded Page Tables and Mountable Merkle Trees to enhance protection, performance, and resource utilization.
MAGE: An execution engine for secure computation, enabling efficient processing of large-scale encrypted data by leveraging oblivious memory access patterns to optimize virtual memory management.
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