Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARK) schemes have been a promising technique in verified computation. Zk-SNARK schemes were designed to be mathematically secure against cryptographic attacks and it remains unclear whether they are vulnerable to fault injection attacks. In this work, we provide a positive answer by presenting ZK-Hammer, which leaks secrets from zk-SNARK schemes via Rowhammer. We incur faults in the exponentiate variables in the Quadratic Arithmetic Program (QAP) problem. Then we analyze the faulty proof using the bilinear pairing technique and manage to recover the secret. We employ a Rowhammer fault evaluation in libsnark and identify 3 CVEs.

Audio is an important medium in people’s daily life, secret information can be embedded into audio for covert communication. However, traditional audio information hiding techniques cannot achieve large hiding capacity and good imperceptibility at the same time, and rely on complex encryption, which limits their applicability in resource-constrained Internet of Things (IoT) environments. In this article, we propose a new audio information hiding method, named AdvAudio, which can achieve large high capacity, as well as good imperceptibility, without reliance on cryptographic encryption. Specifically, AdvAudio leverages adversarial example technique to train a well-designed perturbation for cover audio and the secret information can only be extracted by the private automatic speech recognition (ASR) model. To achieve this, we implement two adversarial example algorithms tailored for both online transmission and physical-world transmission scenarios. In particular, our embedding algorithm dynamically adjusts the addition of simulated environmental noise depending on whether the audio is intended to propagate in the physical world. The iterative optimization process is guided by targeted adversarial attack objectives, ensuring that the private ASR model decodes the embedded secret information accurately. Taking DeepSpeech as the private model, we implement a prototype of AdvAudio, which achieves a high embedding capacity of 383.8 bps with excellent imperceptibility, yielding a Perceptual Evaluation of Speech Quality (PESQ) score of 2.351. Furthermore, it offers robust security, achieving a 100% defense success rate against both internal and external attacks. In the physical world, AdvAudio still maintains effectiveness across six different types of noise and retaining 82% accuracy even under sudden loud noises. Additionally, the secret information can only be extracted in the target environment, with a success rate of 26%, and 0% in non-target environments. In the future, we aim at enhancing the steganalysis resistance of AdvAudio and explore its potential applications in various environments or with alternative ASR models.

Due to excellent performance, LSM-tree key-value stores have been widely used in various applications in recent years. However, LSM-tree's inherent batched data processing approach makes it suffer from poor SLA behaviors, such as a very unstable throughput and high tail latency. Unlike the I/O isolation or prioritization methods that cannot solve the SLA problem thoroughly, we have designed and implemented a new SLA-oriented LSM-tree KV store, i.e., CruiseDB, to solve both the essential and the direct SLA problems of LSM-tree KV stores by introducing an adaptive admission mechanism and improving the LSM-tree structure. According to reliable estimation of the service capacity of the LSM-tree, CruiseDB adaptively admits only an appropriate number of user requests to enter the LSM-tree memory buffer in unit time and removes the internal roadblocks of the request processing, with the advantages of preventing the write stall phenomenon, which leads to SLA declines. CruiseDB can promote the guaranteed throughput by 2.08 times on average compared with the state-of-the-art LSM-tree or B-tree KV stores.

With the rapid increase in distributed wind generation, considerable efforts have been devoted to the microgrid day-ahead scheduling. The effectiveness of those methods will highly depend on the selection of the uncertainty sets. We propose a distribution-free approach for wind power scenario generation, using sequence generative adversarial networks. To capture the temporal correlation, the model adopts the long short-term memory architecture and uses generative adversarial networks coupled with reinforcement learning, which, in contrast to the existing methods, avoids manual labeling and captures the complex dynamics of the weather. We conduct case studies based on the data from the Bonneville Power Administration and the National Renewable Energy Laboratory, and show that the generated scenarios can better characterize the variability of wind power and reduce the risk of uncertainties, compared with those produced by Gaussian distribution, vanilla long short-term memory, and multivariate kernel density estimation. Moreover, the proposed method achieves better performance when applied to the day-ahead scheduling of microgrids.