Community detection refers to mechanisms that aim to identify groups of interacting nodes in a network according to the structural properties of the network. It has been used to analyze various graphs. In the context of social networks, it requires the collection of each user's social relations, posing the risk of user privacy intrusion caused by untrusted servers. Local differential privacy is a widely adopted approach for providing privacy protection while allowing acceptable utility of the protected data for analytics. There has been growing research interest in applying local differential privacy protection to community detection. However, such protection approaches typically suffer from poor accuracy due to the excessive noise in the protected data. This paper proposes LDP-Cd, a two-phase community detection framework under local differential privacy. LDP-Cd initializes the community groups using the Louvain community detection algorithm and iteratively refines the community in the second phase. Besides, we propose an order-consistent data perturbation method over the degree vector, thus ensuring the ordering consistency of the fitness between the user and community groups, thereby improving the accuracy of community detection. Experimental results on real datasets show that LDP-Cd has significant advantages over existing methods regarding community detection accuracy and a trade-off between user privacy and community detection utility.

In recent years, with the rapid development of information technology, the volume of image data has grown exponentially. However, these datasets typically contain a large amount of redundant information. To extract effective features and reduce redundancy from images, a representation learning method based on the Vision Transformer (ViT) has been proposed, and to our best knowledge, Transformer was first applied to zero-shot learning (ZSL). The method adopts a symmetric encoder–decoder structure, where the encoder incorporates Multi-Head Self-Attention (MSA) mechanism of ViT to reduce the dimensionality of image features, eliminate redundant information, and decrease computational burden. Consequently, it effectively extracts features, and the decoder is utilized for reconstructing image data. We evaluated the representation learning capability of the proposed method in various tasks, including data visualization, image reconstruction, face recognition, and ZSL. By comparing with state-of-the-art representation learning methods, the outstanding results obtained validate the effectiveness of this method in the field of representation learning.

Collecting users’ historical data such as movie watching and music listening, and mining frequent items from them, can improve the utility of smart services, but there is also a risk of compromising user privacy. Local differential privacy is a strict definition of privacy and has been widely used in various privacy-preserving data collection scenarios. However, the accuracy of existing locally differentially private frequent items mining methods decreases significantly with the increase in the dimensions of data to be collected. In this paper, we propose a new locally differentially private frequent item mining method for high-dimensional data, which decreases the dimension used for data perturbation by grouping the contents and improving the interference matrix generation method, so as to improve the data reconstruction accuracy. The experimental results show that our proposed method can significantly improve the accuracy of frequent item mining and provide a better trade-off between privacy and accuracy compared with existing methods.

Many enterprises have a growing interest in service composition to construct their business applications. With the increase of alternative services, Quality of Service (QoS) becomes an important indicator of obtaining optimal composite services. Due to the dynamic nature of the service environment, a composite service may not guarantee to deliver an overall optimal QoS. Re-optimization approaches have been developed to handle a dynamic environment. However, these approaches do not consider the diversity of alternative solutions, which may lead to better solutions. In this work, we introduce an adaptive approach, called estimation of distribution algorithm based on Restricted Boltzmann Machine (rEDA). rEDA effectively maintains the diversity of alternative solutions, by leveraging the inference ability of Restricted Boltzmann Machine to capture the potential solutions. It also provides a predictive guidance for the exploration of solution space, by considering the degree of how well a service contributes to the global QoS. The experimental evaluation shows that rEDA has a significant improvement on effectiveness and efficiency over existing approaches.