Abstract The rapid expansion of smart devices leads to the increasing demand for vulnerability detection in the cyber security field. Writing secure source codes is crucial to protect applications and software. Recent vulnerability detection methods are mainly using machine learning and deep learning. However, there are still some challenges, how to learn accurate source code semantic embedding at the function level, how to effectively perform vulnerability detection using the learned semantic embedding of source code and how to solve the overfitting problem of learning‐based models. In this paper, we consider codes as various graphs with node features and propose a tensor‐based gated graph neural network called TensorGNN to produce code embedding for function‐level vulnerability detection. First, we propose a high‐dimensional tensor for combining different code graph representations. Second, inspired by the work of tensor technology, we propose the TensorGNN model to produce accurate code representations using the graph tensor. We evaluate our model on 7 C and C++ large open‐source code corpus (e.g. SARD&NVD, Debian, SATE IV, FFmpeg, libpng&LibTiff, Wireshark and Github datasets), which contains 13 types of vulnerabilities. Our TensorGNN model improves on existing state‐of‐the‐art works by 10%–30% on average in terms of vulnerability detection accuracy and F1, while our TensorGNN model needs less training time and model parameters. Specifically, compared with other existing works, our model reduces 25–47 times of the number of parameters and decreases 3–10 times of training time. Results of evaluations show that TensorGNN has better performance while using fewer training parameters and less training time.

Synthetic aperture radar (SAR) images have been widely used in water body information extraction. However, SAR images suffer from speckles and the additive noise, which affect the performance of automatic information extraction. Thus, we propose the nonconvex-nonlocal total variation (NLTV) regularization to suppress speckles and the additive noise, and improve the performance of water body information extraction using the enhanced images. Experiments using Qilu-1 (QL-1) SAR data verify the effectiveness of the method.

Ridge regression is an important statistical method that is widely used in real life, such as health prediction. As clients with limited computing resources may not be able to handle large training datasets, it is necessary to outsource this operation to a powerful cloud server. Data privacy is raised due to outsourcing. Some previous works on secure outsourcing ridge regression were provided for privacy preserving. However, there are some issues such as having heavy workloads and inefficient. In this paper, we propose an efficient privacy-preserving protocol for outsourcing ridge regression. In our design, we utilize blinding technology to protect the privacy of clients by transforming the original problem into an encryption problem with orthogonal and diagonal matrices as the secret keys. The following advantages can be seen from the experimental result and theoretical analysis: (1) Our protocol is more efficient than related works. When the calculation dimension is greater than 6000×1500, clients computations greatly reduce to 60 percent of other schemes and the total computing is close to the original calculation; (2) Edge server provides the public verifiability, which allows any verifier to verify whether the result returned by the cloud server is correct; (3) We also give a formal proof of security based on the standard simulation model.

Private set intersection (PSI) allows participants to securely compute the intersection of their inputs, which has a wide range of applications such as privacy-preserving contact tracing of COVID-19. Most existing PSI protocols were based on asymmetric/symmetric cryptosystem. Therefore, keys-related operations would burden these systems. In this paper, we transform the problem of the intersection of sets into the problem of finding roots of polynomials by using point-value polynomial representation, blind polynomials’ point-value pairs for secure transportation and computation with the pseudorandom function, and then propose an efficient PSI protocol without any cryptosystem. We optimize the protocol based on the permutation-based hash technique which divides a set into multisubsets to reduce the degree of the polynomial. The following advantages can be seen from the experimental result and theoretical analysis: (1) there is no cryptosystem for data hiding or encrypting and, thus, our design provides a lightweight system; (2) with set elements less than 212 , our protocol is highly efficient compared to the related protocols; and (3) a detailed formal proof is given in the semihonest model.

ABSTRACT With the development of modern Internet and mobile networks, there is an increasing need for collaborative privacy‐preserving applications. Secure multi‐party computation (SMPC) gives a general solution to these applications and has become a hot topic. Yao's garbled circuit approach is a leading method in designing protocols for secure two‐party computation (2PC), which is a very important base in SMPC. However, there are only few protocols obtaining the fairness of secure 2PC, and only one of them was constructed within the standard simulation framework but with very low efficiency. In this paper, we propose an efficient fair secure Yao's garbled circuit protocol within the universally composable (UC) framework. By comparing with all other fair secure Yao's protocols, our new protocol enjoys three advantages. First, our protocol is more efficient than any other fair secure Yao's protocols within the standard simulation framework. Second, our protocol is the first fair UC‐secure Yao's garbled circuit protocol, so it is more secure than other fair Yao's protocols. Third, there does not require any third party involved in our protocol; thus, it is very suitable for many applications. Copyright © 2013 John Wiley & Sons, Ltd.

Yao first introduced a constant-round protocol for secure two-party computation (2PC) withstanding semi-honest adversaries by using a tool called "garbled circuit". Later, many protocols based on garbled circuit approach have been presented, most of which discussed malicious adversaries and efficiency about 2PC. However, there only have a few protocols dealing with the fundamental property of fairness for Yao's garbled circuit approach, in which one involved a trusted third party and the others are very expensive. In the paper, we propose' an efficient Yao's garbled circuit protocol for fair secure 2PC based on ElGamal encryption, Pedersen commitment, Cachin et al.'s verifiable oblivious transfer and Ou-Ruan et al.'s gradual release homomorphic timed commitment. The protocol achieves two advantages: it doesn't need the third party and it is more efficient than other fair secure Yao's protocols.