The IoT, or Internet of Things has been a major talking point amongst technology enthusiasts in recent years. The internet of thing (IoT) has been emerged and evolved rapidly, making the world’s fabric around us smarter and more responsive. The smart home uses one such transformation of IoT, which seems to be the wave of the future. However, with the increasing wide adoption of IoT, data security, and privacy concerns about how our data is collected and shared with others, has also risen. To solve these challenges, an approach to data privacy and security in a smart home using blockchain technology is proposed in this paper. We propose authentication scheme that combines attribute-based access control with smart contracts and edge computing to create a secure framework for IoT devices in smart home systems. The edge server adds scalability to the system by offloading heavy processing activities and using a differential privacy method to aggregate data to the cloud securely and privately. We present several aspects of testing and implementing smart contracts, the differential private stochastic gradient descent algorithm, and system architecture and design. We demonstrate the efficacy of our proposed system by fully examining its security and privacy goals in terms of confidentiality, integrity, and availability. Our framework achieves desired security and privacy goals and is resilient against modification, DoS attacks, data mining and linkage attacks. Finally, we undertake a performance evaluation to demonstrate the proposed scheme’s feasibility and efficiency.

Photo response non-uniformity (PRNU) noise-based source attribution is a well-known technique to verify the camera of an image or video. Researchers have proposed various countermeasures to prevent PRNU-based source camera attribution. Forced seam-carving is one such recently proposed counter forensics technique. This technique can disable PRNUbased source camera attribution by forcefully removing seams such that the size of most uncarved image blocks is less than 50 × 50 pixels. In this paper, we show that given multiple seamcarved images from the same camera, source attribution can still be possible even if the size of uncarved blocks in the image is less than the recommended size of 50 × 50 pixels. Theoretical analysis and experiments with multiple cameras demonstrate that the effectiveness of our scheme depends on the number of seams carved from an image and the randomness of the seam positions.

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

Photo Response Non-Uniformity (PRNU) noise-based source camera attribution is a popular digital forensic method. In this method, a camera fingerprint computed from a set of known images of the camera is matched against the extracted noise of an anonymous questionable image to find out if the camera had taken the anonymous image. The possibility of privacy leak, however, is one of the main concerns of the PRNU-based method. Using the camera fingerprint (or the extracted noise), an adversary can identify the owner of the camera by matching the fingerprint with the noise of an image (or with the fingerprint computed from a set of images) crawled from a social media account. In this article, we address this privacy concern by encrypting both the fingerprint and the noise using the Boneh-Goh-Nissim (BGN) encryption scheme, and performing the matching in encrypted domain. To overcome leakage of privacy from the content of an image that is used in the fingerprint calculation, we compute the fingerprint within a trusted environment, such as ARM TrustZone. We present e-PRNU that aims at minimizing privacy loss and allows authorized forensic experts to perform camera attribution. The security analysis shows that the proposed approach is semantically secure.

In the era of Large Language Models (LLMs), Knowledge Distillation (KD) enables the transfer of capabilities from proprietary LLMs to open-source models. This survey provides a detailed discussion of the basic principles, algorithms, and implementation methods of knowledge distillation. It explores KD’s impact on LLMs, emphasizing its utility in model compression, performance enhancement, and self-improvement. Through the analysis of practical examples such as DistilBERT, TinyBERT, and MobileBERT, the paper demonstrates how knowledge distillation can markedly enhance the efficiency and applicability of large language models in real-world scenarios. The discussion encompasses the varied applications of KD across multiple domains, including industrial systems, embedded systems, Natural Language Processing (NLP), multi-modal processing, and vertical domains, such as medicine, law, science, finance, and materials science. This survey outlines current KD methodologies and future research directions, highlighting its role in advancing AI technologies and fostering innovation across different sectors.

Advances in cloud computing have allowed volume rendering tasks, typically done by volume ray-casting, to be outsourced to cloud data centers. The availability of volume data and rendered images (which can contain important information such as the disease information of a patient) to a third-party cloud provider, however, presents security and privacy challenges. This paper addresses these challenges by proposing a secure cloud-based volume ray-casting framework that distributes the rendering tasks among the data centers and hides the information that is exchanged between the server and a data center, between two data centers, and between a data center and the client by using Shamir's secret sharing, such that none of the data centers has enough information to know the secret data and/or rendered image. Experiments and analyses show that our framework is highly secure and requires low computation cost.