In recent years, federated learning was proposed as a distributed training paradigm centered on privacy preservation that enables multiple participants to train models together without sharing their data. However, despite these advantages, federated learning still presents several potential privacy leakage issues in practical applications. These issues arise from factors such as data leakage during communication, which has attracted widespread attention. The use of a local differential privacy mechanism to perturb the model weights provides an effective way to implement privacy-preserving federated learning. However, the current scheme still suffers from the problem of introducing excessive noise, which in turn affects the proper training of the model. In this paper, we propose a perturbation mechanism called the adaptive piecewise mechanism (APM) by drawing on the design of a piecewise mechanism with a personalized local differential privacy mechanism (PWP). In addition, we mitigate the problem of explosion of the privacy budget of the overall scheme by applying a parameter shuffling mechanism. As a result, the model accuracy of the APM perturbation mechanism was improved by an average of 11.59%, 14.85% and 18.55% on the MNIST, Fashion-MNIST and Cifar-10 datasets, respectively, compared with those of the layer-based adaptive local differential privacy mechanism (LALDP).

Federated learning is a distributed learning helpful approach for resolving data privacy concerns and eliminating data silos. Homomorphic encryption is a vital technology for preserving user privacy in federated learning, and current studies are mainly concentrated on a single-key environment. However, if one user key is exposed in a single-key environment, it implies that the whole system key has been revealed. To strengthen security, we should allow different participants of federated learning to choose different keys to encrypt their local models. The cloud server should finish model aggregation calculation on ciphertexts under different public keys. Besides, research in this area is insufficient to guarantee mobile users’ data integrity verification and authentication in open channels. Therefore, this article proposes a privacy protection federated learning scheme VPFL based on the BCP cryptosystem, which can verify user identity and data integrity in a multikey environment. First, this scheme employs the BCP cryptosystem with double trapdoors for data encryption and transmission, enhancing the user’s privacy security. Second, a method for verifying user data integrity and identity was created utilizing bilinear aggregate signatures and verifiable secret sharing. It can effectively eliminate some incorrect data of some users. Third, VPFL tolerates users dropping out during training while still guaranteeing high accuracy. Finally, theoretical analysis and experimental evaluation indicate that the proposed scheme is efficient and secure.

Abstract Online taxi-hailing has become people’s most popular trip mode due to its convenience and low cost. However, it also poses a privacy threat to passengers and drivers, since the online taxi-hailing service providers are able to track their precise mobility trajectories. In addition, there is a certain time delay between the time of a passenger makes a request and the time of the driver arrives the passenger’s boarding position in current online taxi-hailing system. To solve these two problems, we present a new and efficient location privacy protection scheme based on the MinHash algorithm (LPPM). With the LPPM, the exact positions of passengers and drivers are generalized into a set of points of interest around them, and the distance between them is transformed into the similarity between the two sets. Thus a service provider can efficiently match passengers and drivers by using MinHash algorithm without revealing their specific location information. In this paper, we use mobile edge computing technology in the online taxi-hailing system to address the second challenge. It can speed up data processing, drivers can make decisions in advance and reduce the possibility of road congestion. Security analysis shows that LPPM has high security, and the final experimental results confirmed that LPPM is effective.

Chinese painting is one of the representatives of our country’s outstanding traditional culture, and it embodies the long history and intellectual wisdom of the Chinese nation. In the paper, we combine the artistic characteristics of Chinese paintings and use an optimized SqueezeNet model to study the sentiment analysis of Chinese paintings. To make full use of the advantages of lightweight convolutional neural networks, we make two optimizations based on SqueezeNet. On the one hand, expand the model width to obtain more effective Chinese painting sentiment features for classification tasks, thereby improving the classification accuracy of the model. On the other hand, introduce the idea of residual network to prevent gradient disappearance and gradient explosion in the training process, thereby enhancing the model’s generalization ability. To verify the effectiveness of the optimized SqueezeNet model used in the sentiment analysis of Chinese paintings, four kinds of sentiment classifications were carried out on the multitheme Chinese paintings downloaded on the Internet. The results of comparative experiments show that the optimized SqueezeNet model used in this paper can improve the accuracy of classification and has better generalization ability. Finally, the research results of this paper can be applied to the protection of traditional culture, the appreciation of traditional Chinese painting, and art education and training, which is conducive to the inheritance and innovation of the national quintessence and promotes the prosperity and development of traditional art and culture.

With the development of the live broadcast industry, security issues in the live broadcast process have become increasingly apparent. At present, the supervision of various live broadcast platforms is basically in a state of human supervision. Manpower supervision is mainly through user reporting and platform supervision measures. However, there are a large number of live broadcast rooms at the same time, and only relying on human supervision can no longer meet the monitoring needs of live broadcasts. Based on this situation, this study proposes a violation information recognition method of a live‐broadcasting platform based on machine learning technology. By analyzing the similarities and differences between normal live broadcasts and violation live broadcasts, combined with the characteristics of violation image data, this study mainly detects human skin color and sensitive parts. A prominent feature of violation images is that they contain a large area of naked skin, and the ratio of the area of naked skin to the overall image area of the violation image will exceed the threshold. Skin color recognition plays a role in initial target positioning. The accuracy of skin color recognition is directly related to the recognition accuracy of the entire system, so skin color recognition is the most important part of violation information recognition. Although there are many effective skin color recognition technologies, the accuracy and stability of skin color recognition still need to be improved due to the influence of various external factors, such as light intensity, light source color, and physical equipment. When it is detected that the area of the skin color in the live screen exceeds the threshold, it is preliminarily determined to be a suspected violation video. In order to improve the recognition accuracy, it is necessary to detect sensitive parts of the suspected video. Naked female breasts are a very obvious feature in violation images. This study uses a chest feature extraction method to detect the chest in the image. When the recognition result is a violation image, it is determined that the live broadcast involves violation content. The machine learning algorithm is simple to implement, and the parameters are easy to adjust. The classifier training requires a short time and is suitable for live violation information recognition scenarios. The experimental results on the adopted data set show that the method used in this article can effectively detect videos with violation content. The recognition rate is as high as 85.98%, which is suitable for a real‐life environment and has good practical significance.

A digital campus will generate a large amount of student‐related data. How to analyze and apply these data has become the key to improving the management level of students. The analysis of student behavior data can not only assist schools in early warning of dangerous events and strengthen school safety but also can use real data to describe student behavior, thereby providing quantitative data support for scholarship and grant evaluation. This paper takes a university student as the research object, collects various data in the digital campus platform, and uses an adaptive K ‐means algorithm in the machine learning algorithm to cluster the data. Analyze the behavior of college students from the clustering results, so as to provide a basis for the education management and learning ability improvement of college students. Specifically, the student’s study, life, and consumption data are selected as the data to describe the student’s behavior at school. This data is input into the adaptive K ‐means algorithm to obtain different types of student consumption habits, living habits, and learning habits. Through the analysis results, it can be found that the problem of the group of students with low financial ability, the problem of too long online time for students, and the number of books borrowed are too low. According to the characteristics of these problems, teachers and schools are provided with targeted management suggestions. The analysis of student behavior based on machine learning technology provides a reference for the formulation of students’ school management policies and provides teachers with information on students’ personality characteristics, which is conducive to improving teachers’ teaching effects. In short, the management of the results of student behavior analysis can provide a basis for the school to formulate reasonable management policies, thereby promoting precision management and scientific decision‐making.

Linear secret-sharing scheme (LSSS) is a useful tool for supporting flexible access policy in building attribute-based encryption (ABE) schemes. But in lattice-based ABE constructions, there is a subtle security problem in the sense that careless usage of LSSS-based secret sharing over vectors would lead to the leakage of the master secret key. In this paper, we propose a new method that employs LSSS to build lattice-based key-policy attribute-based encryption (KP-ABE) that resolves this security issue. More specifically, no adversary can reconstruct the master secret key since we introduce a new trapdoor generation algorithm to generate a strong trapdoor (instead of a lattice basis), that is, the master secret key, and remove the dependency of the master secret key on the total number of system attributes. Meanwhile, with the purpose of reducing the storage cost and support dynamic updating on attributes, we extended the traditional 1-dimensional attribute structure to 2-dimensional one. This makes our construction remarkably efficient in space cost, with acceptable time cost. Finally, our scheme is proved to be secure in the standard model.

Blockchain, as one of the crypto-intensive creatures, has become a very hot topic recently. Although many surveys have recently been dedicated to the security and privacy issues of blockchains, there still lacks a systematic examination on the cryptographic primitives in blockchains. To this end, we in this paper conduct a systematic study on the cryptographic primitives in blockchains by comprehensive analysis on top-30 mainstream cryptocurrencies, in terms of the usages, functionalities, and evolutions of these primitives. We hope that it would be helpful for cryptographers who are going to devote themselves to the blockchain research, and the financial engineers/managers who want to evaluate cryptographic solutions for blockchain-based projects.