The proliferation of IoT devices has led to an exponential increase in data generation, creating new opportunities for data marketplaces. However, due to the security and privacy issues arising from the sensitive nature of IoT data, as well as the need for efficient management of vast amounts of IoT data, a robust solution is necessary. Therefore, this paper proposes a secure data sharing framework with fine-grained access control and privacy protection for the internet of things (IoT) data marketplace. For fine-grained access control of the data in the proposed protocol, we develop the hidden attributes and encryption outsourced key-policy attribute-based encryption (HAEO-KP-ABE) that outsources high-complex operations to peripheral devices with high capability to reduce the computation burden of IoT device. It achieves data privacy by hiding attributes in the ciphertext and by preventing entities that do not hold the data consumer’s secret key material (including SA/CS) from running the match test on stored ciphertexts before decryption. It also has an efficient match test algorithm which can verify that the hidden attributes of the ciphertext match the access policy of the data consumer’s private key without revealing those attributes. We demonstrate the proposed protocol satisfies the security features required for the data sharing process in an IoT data marketplace environment. Furthermore, we evaluate the execution time of the proposed protocol according to the number of attributes and show the practicality and efficiency of the proposed protocol compared to the related works.

Large language models (LLMs) have emerged as a promising tool for detecting code vulnerabilities, potentially offering advantages over traditional rule-based methods. This paper proposes an enhanced framework for vulnerability detection using LLMs, incorporating various prompt engineering strategies to improve performance. We evaluate several techniques, including role-based prompting, zero-shot chain-of-thought, and structured prompting approaches, on the DiverseVul dataset of C/C++ vulnerabilities. Our experiments assess the framework’s performance across different code structures, contextual information levels, and LLM capabilities. Our results show that using our dynamic prompt engineering technique, you can improve the F1 score by up to 100% with GPT-3.5, a widely used LLM model. We also observe that GPT-4o, Gemini 2.0 Flash, and Meta Llama 3.1 generally outperform GPT-3.5, and all models are very poor when it comes to correctly identifying the type of vulnerability in the code, with the best F1 score of 0.16 observed. However, our follow-up experiments on LLM-based vulnerability correction (i.e., patching) show a 45.77% success rate using GPT-4o, demonstrating promising results in leveraging LLMs for enhancing software security and providing insights into optimizing prompt engineering for vulnerability detection tasks.

We present a practical implicit authentication system for Virtual Reality (VR) that uses natural interaction tasksgrabbing, pointing, and typing-as behavioral biometrics. The system extracts 221 features from head-mounted and controller sensors and is trained as a lightweight SVM-based binary classifier using data from legitimate users and a small set of reference users to simulate attacker behavior. In a 24-participant study, our system achieved strong authentication performance, with median Equal Error Rates (EERs) of 0.4% for grabbing, 2.6 % for pointing, and 0.3 % for typing. Designed for on-device deployment, it requires no GPU support, completes inference within 1 second, and maintains a compact model size under 0.2 MB, enabling efficient, real-time authentication on standalone VR headsets. Security evaluations with attacker-in-the-loop experiments across no-knowledge, shoulder-surfing, and videoreplay conditions revealed clear trade-offs. Typing and pointing offered strong resistance to impersonation, while grabbing, despite high usability, was more vulnerable under video replay with a $23.8 \%$ attack success rate. These results demonstrate that secure, accurate, and real-time implicit authentication is feasible in VR, with task-specific characteristics enabling flexible deployment based on security and usability needs.

In this paper we report on an investigation of the effects of a self-avatar's visual similarity to a user's actual appearance, on their perceptions of the avatar in an immersive virtual reality (IVR) experience. We conducted a user study to examine the participant's sense of body ownership, presence and visual realism under three levels of avatar-owner visual similarity: (L1) an avatar reconstructed from real imagery of the participant's appearance, (L2) a cartoon-like virtual avatar created by a 3D artist for each participant, where the avatar shoes and clothing mimic that of the participant, but using a low-fidelity model, and (L3) a cartoon-like virtual avatar with a pre-defined appearance for the shoes and clothing. Surprisingly, the results indicate that the participants generally exhibited the highest sense of body ownership and presence when inhabiting the cartoon-like virtual avatar mimicking the outft of the participant (L2), despite the relatively low participant similarity. We present our experiment and main findings, also, discuss the potential impact of a self-avatar's visual differences on human perceptions in IVR.