Homomorphic permutation is fundamental to privacy-preserving computations based on batch-encoding homomorphic encryption. It underpins nearly all homomorphic matrix operations and predominantly influences their complexity. Permutation decomposition as a potential approach to optimize this critical component remains underexplored. In this paper, we propose novel decomposition techniques to optimize homomorphic permutations, advancing homomorphic encryption-based privacy-preserving computations. We start by defining an ideal decomposition form for permutations and propose an algorithm searching for depth-1 ideal decompositions. Based on this, we prove the full-depth ideal decomposability of permutations used in specific homomorphic matrix transposition (HMT) and multiplication (HMM) algorithms, allowing them to achieve asymptotic improvement in speed and rotation key reduction. As a demonstration of applicability, substituting the HMM components in the best-known inference framework of encrypted neural networks with our enhanced version shows up to a 3.9× reduction in latency. We further devise a new method for computing arbitrary homomorphic permutations, specifically those with weak structures that cannot be ideally decomposed. We design a network structure that deviates from the conventional scope of decomposition and outperforms the state-of-the-art technique under a limited rotation key budget, achieving a speed-up of up to 1.69 ×.

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models by collecting the intermediate parameters instead of the real user data, which greatly enhances user privacy. In addition, federated recommendation systems (FedRSs) can cooperate with other data platforms to improve recommendation performance while meeting the regulation and privacy constraints. However, FedRSs face many new challenges such as privacy, security, heterogeneity, and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this article, we: 1) summarize some common privacy mechanisms used in FedRSs and discuss the advantages and limitations of each mechanism; 2) review several novel attacks and defenses against security; 3) summarize some approaches to address heterogeneity and communication costs problems; 4) introduce some realistic applications and public benchmark datasets for FedRSs; and 5) present some prospective research directions in the future. This article can guide researchers and practitioners understand the research progress in these areas.

Vertical Federated Learning (VFL) is a private-preserving distributed machine learning paradigm that collaboratively trains machine learning models with participants whose local data overlap largely in the sample space, but not so in the feature space. Existing VFL methods are mainly based on synchronous computation and homomorphic encryption (HE). Due to the differences in the communication and computation resources of the participants, straggling participants can cause delays during synchronous VFL model training, resulting in low computational efficiency. In addition, HE incurs high computation and communication costs. Moreover, it is difficult to establish a VFL coordinator (a.k.a. server) that all participants can trust. To address these problems, we propose an efficient Asynchronous Multi-participant Vertical Federated Learning method (AMVFL). AMVFL leverages asynchronous training which reduces waiting time. At the same time, secret sharing is used instead of HE for privacy protection, which further reduces the computational cost. In addition, AMVFL does not require a trusted entity to serve as the VFL coordinator. Experimental results based on real-world and synthetic datasets demonstrate that AMVFL can significantly reduce computational cost and improve the accuracy of the model compared to five state-of-the-art VFL methods.

Major depressive disorder (MDD) is the most common psychological disorder that affects mental and physical health. To narrow the gap in real world mental healthcare and improve the effectiveness of MDD treatment, an increasing number of artificial intelligence (AI) methods have been proposed to explore electroencephalography (EEG) features, including traditional signal features and measures of brain functional connectivity network (BFCN), for the recognition of depression-related patterns. However, these methods fail to capture long-term dependencies and limit the modeling ability of information transmission dependencies in MDD brain regions. To address these issues, we propose a novel brain functional residual temporal convolution network (BFRTCN) method for MDD recognition. On one hand, this model directly focuses on the connectivity weights of BFCNs to model the information transmission between brain regions, allowing for better differentiation of the differences in information transmission patterns between MDD and normal control (NC). On the other hand, we introduce a residual temporal convolution network (ResiTCN) that utilizes temporal convolution layers to capture short-term changes in brain regions and establish residual connections to help maintain long-term dependencies for improving ability to capture disease variations. Experimental results on benchmark datasets validate the superior performance and time complexity of BFRTCN. Analysis shows that the Beta band MDD transmission mode is relatively stable. There are defects in the brain functional connections between the frontal and right temporal (RT) regions on Alpha and Gamma bands, which can serve as potential biomarkers for MDD recognition.

Edge computing has been widely used to deploy and service deep learning applications. Equipped with GPUs, edge nodes can process concurrent incoming inference requests of the deep learning model. However, existing methods for inference tasks do not allow efficient parallel handling of user requests. This paper investigates the popular Transformer deep learning model and develops ParaTra, a parallel transformer inference framework for providing parallel inference services to users. In the framework, the Transformer model is partitioned and deployed in users’ devices and the edge node to efficiently utilize their processing power. The concurrent inference tasks with different sizes are dynamically packaged in a scheduling queue and sent in batch to an encoder-decoder pipeline for processing. ParaTra can significantly reduce the overheads of parallel processing and the usage of GPU memory. Experiment results show that ParaTra can save up to 37.1% of GPU memory usage and improve 8.4 times of processing speed.

The increasing popularity of crowdsourcing has resulted in the emergence of multiple crowdsourcing service providers (CSPs), such as Mechnical Turk and Crowdflower, which compete to attract crowd workers (CWs). CWs can share their experience working for various CSPs, which forms the basis of CSP reputation score. This information can be used for trust building and facilitating future CWs’ decisions on which CSP to work for. Existing reputation management research in crowdsourcing has mainly focused on controlling task quality and improving revenue from the perspective of CSPs. Little attention has been paid to helping CSPs manage their reputation to attract and retain CWs. In this paper, we propose the Crowdsourcing Service Provider Reputation Management (CSP-RM) framework to bridge this important gap. Based on the current reputation of CSPs, it dynamically balances the trade-off between the reputation maintenance cost and the long-term profit for a given CSP. It performs dynamic commission allocation for a CSP based on Lyapunov optimization to guide the recruitment of CWs, while considering the revenue and the changes in the number of CWs. Extensive experiments based on highly competitive crowdsourcing market demonstrate that CSP-RM makes the most advantageous cost-benefit trade-off compared to existing approaches, outperforming the best baseline by 23.83%, 39.21% and 3.36% in terms of average cumulative revenue, average number of CWs and public reputation, respectively. To the best of our knowledge, it is the first decision support framework for enabling CSPs to recruit more CWs in a highly competitive market, while maintaining their reputation and ensuring long-term benefit.