Aiming at the problem of adopting the same level of privacy protection for sensitive data in the process of data collection and ignoring the difference in privacy protection requirements, the... Show More

Aiming at the problem of adopting the same level of privacy protection for sensitive data in the process of data collection and ignoring the difference in privacy protection requirements, the authors propose an adaptive personalized randomized response method based on local differential privacy (LDP-APRR). LDP-APRR determines the sensitive level through the user scoring strategy, introduces the concept of sensitive weights for adaptive allocation of privacy budget, and realizes the personalized privacy protection of sensitive attributes and attribute values. To verify the distorted data availability, LDP-APRR is applied to frequent items mining scenarios and compared with mining associations with secrecy konstraints (MASK), and grouping-based randomization for privacy-preserving frequent pattern mining (GR-PPFM). Results show that the LDP-APRR achieves personalized protection of sensitive attributes and attribute values with user participation, and the maxPrivacy and avgPrivacy are improved by 1.2% and 4.3%, respectively, while the availability of distorted data is guaranteed.

Website link detection is an important means to ensure the security of the external chain. In the past, it was mainly realized through blacklisting and feature engineering-based machine learning... Show More

Website link detection is an important means to ensure the security of the external chain. In the past, it was mainly realized through blacklisting and feature engineering-based machine learning, which has the problems of slow detection speed and weak model generalization ability. The development of neural networks has brought a new solution to the security detection of the external chain of the website. To address the performance bottleneck caused by the variable content length of web pages, this article introduces an innovative approach: a website external link security detection algorithm based on multi-modal fusion. It extracts text, dynamic script, and image features separately, and constructs a deep fusion model that combines these multi-modal features. Compared with the previous research results, the proposed method is superior to the traditional single-mode method, and can quickly and accurately identify malicious web pages. The accuracy and F1 value are improved by 2.7% and 0.026.

Almost every living species has a motive to communicate electronically with one another and preserve data for immediate or future use. These data are becoming too large to be maintained on personal... Show More

Almost every living species has a motive to communicate electronically with one another and preserve data for immediate or future use. These data are becoming too large to be maintained on personal storage devices. Technological innovation has cleared the path for vast, remote storage known as the cloud. This innovation is being provided as a service to people and organizations due to the high cost of investment and the high-tech skills needed for its maintenance. Despite the many benefits of cloud computing, data privacy, integrity, and access control are issues that require immediate attention. Many studies have been conducted in order to find solutions to these challenges. In this review, the authors look at the numerous methods that have been proposed to address these security challenges. The research revealed that elliptic curve cryptography and the advance encryption system (AES) were the techniques that were most frequently used to address security issues in the digital world.

Vulnerability can lead to data loss, privacy leakage and financial loss. Accurate detection and identification of vulnerabilities is essential to prevent information leakage and APT attacks. This... Show More

Vulnerability can lead to data loss, privacy leakage and financial loss. Accurate detection and identification of vulnerabilities is essential to prevent information leakage and APT attacks. This paper explores the possibility of digging the valuable information in vulnerability reports deeply. We propose a new model, VCGERG, which products a graph using key information from vulnerability reports and embeds the graph into the vector space using a keywords-LINE graph embedding algorithm based on the attention of neighboring nodes. VCGERG model uses the OVR random forest algorithm to classify vulnerabilities. Our model can get the complicated local and global information of the graph in large-scale dataset and achieve better results. In order to verify the effectiveness of our model, it is evaluated on many experiments. Compared with other models, our method has a higher accuracy rate of 0.975.