Security surveillance is critical to social harmony and people’s peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S2-VAE, for anomaly detection from video data. The S2-VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (SF - VAE) and a Skip Convolutional VAE (SC-VAE). The SF -VAE is a shallow generative network to obtain a model like Gaussian mixture to fit the distribution of the actual data. The SC-VAE, as a key component of S2- VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both SF -VAE and SC-VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed S2-VAE is evaluated using four public datasets. The experimental results show that the S2-VAE outperforms the state-of-the-art algorithms. The code is available publicly at https://github.com/tianwangbuaa/.

Over the past years, the increasingly severe data island problem has spawned an emerging distributed deep learning framework—federated learning, in which the global model can be constructed over multiple participants without directly sharing their raw data. Despite its promising prospect, there are still many security challenges in federated learning, such as privacy preservation and integrity verification. Furthermore, federated learning is usually performed with the assistance of a center, which is prone to cause trust worries and communicational bottlenecks. To tackle these challenges, in this paper, we propose a privacy-preserving and verifiable decentralized federated learning framework, named PVD-FL, which can achieve secure deep learning model training under a decentralized architecture. Specifically, we first design an efficient and verifiable cipher-based matrix multiplication (EVCM) algorithm to execute the most basic calculation in deep learning. Then, by employing EVCM, we design a suite of decentralized algorithms to construct the PVD-FL framework, which ensures the confidentiality of both global model and local update and the verification of every training step. Detailed security analysis shows that PVD-FL can well protect privacy against various inference attacks and guarantee training integrity. In addition, the extensive experiments on real-world datasets also demonstrate that PVD-FL can achieve lossless accuracy and practical performance.

With the popularity of cloud storage services, people are increasingly accustomed to storing images in the cloud. However, cloud storage services raise privacy concerns, e.g., leakage of images to unauthorized third parties and service providers may exploit image detection technologies to portrait users without permission. Although privacy concerns can be solved by encrypting images before they are uploaded to the cloud, traditional encryption methods significantly affect the usability and user experience, for example, users cannot preview images in the cloud. Recently, Marohn et al. proposed two approximate thumbnail-preserving encryption schemes, called DRPE and TPE-LSB, to balance the privacy and usability of images in the cloud. However, both schemes have defects that either the decryption may fail or the ciphertext images have poor performance in perceived quality and too many noise points after decryption. To this end, we pertinently propose a high-fidelity thumbnail-preserving encryption scheme (HF-TPE). Compared with the previous works, on the one hand, the HF-TPE scheme not only ensures the correct decryption of ciphertext images, but also makes the ciphertext thumbnails more close to the plaintext images perceptually. On the other hand, the decrypted thumbnails have lower noise intensity and upper limit of the number of noises. In addition, simulation experiments further show that the HF-TPE scheme can guarantee users’ usability.

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factorization-based models are a representative of solving this problem. However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability. Moreover, a series of large-scale algorithms that bear one or more hyperparameters are impractical in real-world applications. To address the two issues, we propose an auto-weighted multi-view clustering (AWMVC) algorithm. Specifically, AWMVC first learns coefficient matrices from corresponding base matrices of different dimensions, then fuses them to obtain an optimal consensus matrix. By mapping original features into distinctive low-dimensional spaces, we can attain more comprehensive knowledge, thus obtaining better clustering results. Moreover, we design a six-step alternative optimization algorithm proven to be convergent theoretically. Also, AWMVC shows excellent performance on various benchmark datasets compared with existing ones. The code of AWMVC is publicly available at https://github.com/wanxinhang/AAAI-2023-AWMVC.

Natural language processing (NLP) allows a computer program to understand human language as it is spoken, and has been increasingly deployed in a growing number of applications, such as machine translation, sentiment analysis, and electronic voice assistant. While information obtained from different sources can enhance the accuracy of NLP models, there are also privacy implications in the collection of such massive data. Thus, in this paper, we design a privacy-preserving system SecureNLP, focusing on the instance of recurrent neural network (RNN)-based sequence-to-sequence with attention model for neural machine translation. Specifically, for non-linear functions such as sigmoid and tanh, we design two efficient multi-party protocols using secure multi-party computation (MPC), which are used to carry out the respective tasks in the SecureNLP. We also prove the security of these two protocols (i.e., privacy-preserving long short-term memory network PrivLSTM, and privacy-preserving sequence to sequence transformation PrivSEQ2SEQ) in the semi-honest adversary model, in the sense that any honest-but-curious adversary cannot learn anything else from the messages they receive from other parties. The proposed system is implemented in C++ and Python, and the findings from the evaluation demonstrate the utility of the protocols in cross-domain NLP.