Backdoor attacks represent a significant threat to the security of deep learning models. Due to the stealthiness of backdoor attacks, effectively detecting whether a model has been compromised by such attacks remains a major challenge. Previous backdoor detection methods either rely on backdoor datasets to identify anomalies in backdoor samples or depend on prior knowledge of existing backdoor attacks. This results in difficulties in detecting backdoors when backdoor samples are unavailable and leads to poor generalization capabilities when addressing new attack methods. This work proposes a novel approach for detecting backdoor attacks called Distill To Detect (D2D), that does not depend on backdoor samples or any prior knowledge. It utilizes knowledge distillation to amplify more general and universal backdoor anomalies exhibited on clean samples for detection. This approach is not only more efficient but also exhibits strong generalization capabilities, enabling the detection of most backdoor attacks with low time and computational costs. We tested our approach against six backdoor attacks and three different model architectures, demonstrating the effectiveness of our proposed method.

Transaction priority is a critical feature for real-world database systems. Under high contention, certain classes of transactions should be given a higher chance to commit than others. Such a prioritization mechanism is commonly implemented in locking-based concurrency control protocols as some lock scheduling mechanisms, but it is rarely supported in the world of optimistic concurrency control. We present Polaris, an optimistic concurrency control protocol that supports multiple priority levels. To enforce priority, Polaris introduces a minimal amount of pessimism through a lightweight reservation mechanism. The protocol is fully optimistic among transactions within the same priority level and preserves the high throughput advantage of optimistic protocols. Our evaluation with YCSB workload shows that Polaris can make the p999 tail latency of high-priority transactions 13x lower than that of low-priority ones. With an abort-aware priority assignment policy, Polaris can deliver 1.9x higher throughput and 17x lower tail latency compared to Silo for high-contention workloads.

We present uFS, a user-level filesystem semi-microkernel. uFS takes advantage of a high-performance storage development kit to realize a fully-functional, crash-consistent, highly-scalable filesystem, with relative developer ease. uFS delivers scalable high performance with a number of novel techniques: careful partitioning of in-memory and on-disk data structures to enable concurrent access without locking, inode migration for balancing load across filesystem threads, and a dynamic scaling algorithm for determining the number of filesystem threads to serve the current workload. Through measurements, we show that uFS has good base performance and excellent scalability; for example, uFS delivers nearly twice the throughput of ext4 for LevelDB on YCSB workloads.