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.

Line-commutated converter (LCC) is a promising choice for integrating large offshore wind farms. In such a system, grid-forming (GFM) control for wind turbine converters is necessary for establishing the offshore ac grid. However, during severe offshore grid ac faults, the GFM converter is susceptible to overcurrent and transient synchronization stability issues, given its internal voltage source nature. In this study, the offshore ac fault ride-through (FRT) issue is studied for the LCC-HVdc connected offshore wind farms. A unified model for the transient synchronization analysis considering current limiting is proposed. On this basis, key new factors, including current limiting and low emulated inertia are revealed to have detrimental impacts on the reliable FRT. A FRT strategy is proposed to address this problem, and simulations validate the feasibility of the method.

The series-connected hybrid diode rectifier (SCH-DR) based high voltage direct current transmission system (HVdc) for integrating offshore wind farm (OWF) confronts challenges in startup, which comprises of a largecapacity DR and a small-capacity auxiliary modular multilevel converter (A-MMC) in series-connection. To achieve DR’s inputting onto OWF’s grid in zero dc current while maintaining energy balance of offshore system, this study proposes a startup strategy based on coordinated energy balance method. Based on this method, OWF needs to generate certain active power to meet consumption of offshore necessary auxiliary devices. To dissipate the surplus power, the proposed method parallels an energy-consuming resistor on AMMC’s dc side. Hence, through its adjustable dc voltage capability, A-MMC can actively and smoothly regulate the dissipated power, thus realizing the coordinated energy balance. The proposed startup strategy exhibits feasibility and engineering practicality, without much escalating investment cost or space requirements. The effectiveness of the proposed startup strategy is verified in MATLAB/Simulink using a scenario of a 2000 MW/±500 kV SCH-DR-HVdc system.

Conventional modular multilevel converters (MMCs) face significant challenges due to their considerable weight and size in high voltage direct current (HVdc) transmission applications for connecting distant offshore wind farms (OFWs). A 1000 MW OFW is selected as a case study to explore the application of the unidirectional-current hybrid MMC (UC-HYB-MMC) in offshore HVDC transmission systems, aiming to harness its advantages of compactness, lightweight construction, and wide-range dc voltage output. Detailed design of the parameters of UC-HYB-MMC is conducted, and a preliminary design and comparison of the converter valve structure are carried out based on the designed parameters. The design results of the studied case show that compared with conventional HB-MMC, the UC-HYB-MMC scheme reduces the total footprint and volume of the converter valve by approximately 41%. This paper also proposes the control strategy for the HVdc transmission system based on UC-HYB-MMC. A system simulation model is established to simulate the startup process and operation control of the system, validating the feasibility of the HVDC transmission system based on the UC-HYB-MMC with asymmetric wide dc voltage for connecting distant OFWs.

Considering the voltage source characteristics, grid-forming (GFM) control is regarded as an effective method to enhance the voltage support capability of inverters, especially under weak grid condition. However, relatively mature grid-forming control methods such as virtual synchronous generator control typically assume a constant voltage source on the dc side, which is not suitable for the modular multilevel converter (MMC) that controls the dc voltage. To address this issue, a GFM control strategy based on the decoupled control is proposed. This strategy achieves grid synchronization based on the dynamic variation of submodule capacitor voltage rather than the dc voltage, by linking the swing equation of the synchronous generator with the energy dynamic equation of the MMC. Additionally, the energy stored in submodule capacitors can be flexibly adjusted to provide inertial responses for the connected grid. With the proposed GFM control, the MMC can provide voltage and reactive power support under weak grid condition. Meanwhile, the dc voltage can be regulated independently through the decoupled control of MMC. The effectiveness of the proposed control method is verified through the simulation results in MATLAB/Simulink.

To relieve the high backhaul load and long transmission time caused by the huge mobile data traffic, caching devices are deployed at the edge of mobile networks. The key to efficient caching is to predict the content popularity accurately while touching the users’ privacy as little as possible. Recently, many studies have applied federated learning in content caching to improve data security. However, they still give away some privacy of participants, especially ignoring the private data leakage in the trained model. To solve this problem and further improve the cache hit ratio, we propose an efficient content popularity prediction of privacy-preserving (CPPPP) scheme based on federated learning and Wasserstein generative adversarial network (WGAN), which achieves a high cache hit ratio. Benefited by the Federated-WGAN and the generated fake samples, the private data, the content preferences of individual users, and so on are well protected. In particular, gradient clipping and model parameter limitation are introduced in the model training, and the security of the modified model is greatly improved compared with the original model. Results show that the proposed scheme has a higher cache hit ratio than the existing federated learning-based methods while limiting the privacy leakage caused by the trained model to a quite low level.

Graph analytics are widely used in real-world applications, and GPUs are major accelerators for such applications. However, as graph sizes become significantly larger than the capacity of GPU memory, the performance can degrade significantly due to the heavy overhead required in moving a large amount of graph data between CPU main memory and GPU memory.

Web services have covered all areas of social life, and various browsers have become necessary software on computers and mobile phones, and they are also the entrances to Web services. All kinds of threats to web data security continue to appear, so web services and browsers have become the focus of security. In response to the requirements of Web service for access entity identification and data access control, this paper proposes a multi-dimensional browser fingerprint detection method based on adversarial learning, and designs a Web service access control framework combined with browser fingerprint detection. Through the joint use of multi-dimensional browser features, adversarial learning is used to improve the accuracy and robustness of browser fingerprint detection; a cross-server and browser-side Web service access control framework is established by creating tags for Web data resources and access entities. Based on the mapping relationship between browser fingerprint detection entities and data resources, fine-grained hierarchical data access control is realized. Through experiments and analysis, the browser fingerprint detection method proposed in this paper is superior to existing machine learning detection methods in terms of accuracy and robustness. Based on the adversarial learning method, good detection results can be obtained in the case of a small number of user samples. At the same time, the open source data set is further used to verify the advantages of the method in this paper. The Web service access control framework can satisfy the requirements of Web data security control, is an effective supplement to user identification technology, and is implementable.