Recommendation systems powered by artificial intelligence (AI) are widely used to improve user experience. However, AI inevitably raises privacy leakage and other security issues due to the utilization of extensive user data. Addressing these challenges can protect users' personal information, benefit service providers, and foster service ecosystems. Presently, numerous techniques based on differential privacy have been proposed to solve this problem. However, existing solutions encounter issues such as inadequate data utilization and a tenuous trade-off between privacy protection and recommendation effectiveness. To enhance recommendation accuracy and protect users' private data, we propose ID-SR, a novel privacy-preserving social recommendation scheme for trustworthy AI based on the infinite divisibility of Laplace distribution. We first introduce a novel recommendation method adopted in ID-SR, which is established based on matrix factorization with a newly designed social regularization term for improving recommendation effectiveness. We then propose a differential privacy-preserving scheme tailored to the above method that leverages the Laplace distribution's characteristics to safeguard user data. Theoretical analysis and experimentation evaluation on two publicly available datasets demonstrate that our scheme achieves a superior balance between privacy protection and recommendation effectiveness, ultimately delivering an enhanced user experience.

Existing approaches to defending Use-After-Free (UAF) exploits are usually done using static or dynamic analysis. However, both static and dynamic analysis suffer from intrinsic deficiencies. The existing static analysis is limited in handling loops, optimization of memory representation. The existing dynamic analysis, which is characterized by lacking the maintenance of pointer information, may lead to flaws that the relationships between pointers and memory cannot be precisely identified. In this work, we propose a new method called UAF-GUARD without the above barriers, in the aim to defending against UAF exploits using fine-grained memory permission management. In particular, we design a key data structure to support the fine-grained memory permission management, which can maintain more information to capture the relationship between pointers and memory. Moreover, we design code instrumentation to enable UAF-GUARD to precisely locate the position of UAF vulnerabilities to further terminate malicious programs when anomalies are detected. We implement UAF-GUARD on a 64-bit Linux system. We carry out experiments to compare UAF-GUARD with the main existing approaches. The experimental results demonstrate that UAF-GUARD is able to effectively and efficiently defend against three types of UAF exploits with acceptable space overhead and time overhead.