Autonomous vehicles use LiDAR sensors to navigate 3D maps of their surroundings, enabling improved decision-making and road control systems. It utilizes laser light to measure the speed, distance, shape, direction, and perturbation of any 3D object in a point cloud. Data on point clouds is in high resolution and used to identify different objects of regular or irregular shapes. It is also used in safe path planning, path optimization, and object avoidance routes. Although point cloud data is robust and beneficial for safe navigation, errors and inconsistencies in the interpretation lead to erroneous judgments. Processing wrong information potentially resulting in hazardous circumstances with severe consequences of casualties and road accidents. In contrast, each path planning and path optimization policy should be thoroughly tested and validated before being applied to real-world scenarios. To provide a safer and collision-free path and to verify optimized path policy, we have proposed a technique called ”LiDAR Fusion-based Optimized Path Selection (LOPS) for Autonomous Vehicles using the Verification Model.” The proposed method uses an object detection strategy and instructs autonomous vehicles to use optimized paths for safe travel and collision-free path selection. The proposed scheme is formally modeled and verified for defined states in UPPAAL, and each state with defined properties has been checked. LOPS has been evaluated with different parameters, including the jerking rate, which ranges from 1.0% to 0.06% in various densities of autonomous vehicles. We have also calculated the effect of optimization of the path on the distance covered in unit time, and it is found that 50 vehicles require 20% more time to cover 50 km than 10, 5, or 2 vehicles. The lane usage is calculated in unit time and shows that using optimization in the lane scenario, lane 2 is 5-19 times more used in 5 minutes. LOPS is evaluated with two datasets, CARLA and NuScenes for different parameters, including path length, path smoothness, object avoidance success rate, path tracking accuracy, and decision delay comparisons.

In this article, we propose secure plaintext image storage protocols in the cloud environment for image owners managing and controlling their outsourced images. To solve control and privacy issues, conventional schemes suggest outsourcing images in the encrypted form. However, encrypted images lose their usability and visibility. For example, image owners cannot quickly find their stored images in the cloud. The proposed protocols encourage plaintext image storage in the cloud with copyright protection of images, visible display, lossless retrieval and controlled deletion via techniques of homomorphic encryption and digital watermarking. We allow the image owner to embed and remove the watermark in a privacy-preserving way without compromising the security of the original data and the computed results. Moreover, the image owner can securely detect the cloud's dishonest act of not completely deleting an image as required or leaking an image without permission. Compared with existing works, our work achieves more functions. We prove that the proposed work achieves the controllable management of the outsourced plaintext images without privacy leakage to unauthorized parties and demonstrate the utility and the efficiency of our protocols through experimental evaluation.

Advances in signal processing in the encrypted domain and cloud computing have given rise to privacy‐preserving technologies. In recent years, reversible data hiding in encrypted images (RDH‐EI) has received attention from the research community because additional data can be embedded into an encrypted image without accessing its original content, and the encrypted image can be losslessly recovered after extracting the embedded data. Although the recent development of RDH‐EI compatible with homomorphic public key cryptosystems has intensified research interest, most of the existing mature RDH schemes cannot be transplanted to the encrypted domain due to the limitations of the underlying cryptosystems. In this paper, prediction error expansion based RDH‐ED using probabilistic and homomorphic properties of the Paillier cryptosystem is presented. This work implements non‐integer mean value computation in the encrypted domain without any interactive protocol between the content owner and the cloud server. This work presents mathematical detail of pixel prediction (mean), prediction error, error expansion and data embedding in the encrypted domain and data extraction and content recovery in the plain domain. Experimental results from standard test images reveal that the proposed scheme outperforms other state‐of‐the‐art encrypted domain schemes.

Due to its large storage facility and high-end computing capability, cloud computing has received great attention as a huge amount of personal multimedia data and computationally expensive tasks can be outsourced to the cloud. However, the cloud being third-party semi-trusted, is prone to information leakage, raising privacy risks. Signal processing in the encrypted domain has emerged as a new research paradigm on privacy-preserving processing over outsourced data by semi-trusted cloud. In this article, we propose a solution for non-integer mean value computation in the homomorphic encrypted domain without any interactive protocol between the client and the service provider. Using the proposed solution, various image processing operations, such as local smoothing filter, un-sharp masking, and histogram equalization, can be performed in the encrypted domain at the cloud server without any privacy concerns. Our experimental results from standard test images reveal that these image processing operations can be performed without pre-processing, without client-server interactive protocol, and without any error between the encrypted domain and the plain domain.