Cryptographic cloud storage (CCS) is a secure architecture built in the upper layer of a public cloud infrastructure. In the CCS system, a user can define and manage the access control of the data by himself without the help of cloud storage service provider. The ciphertext-policy attribute-based encryption (CP-ABE) is considered as the critical technology to implement such access control. However, there still exists a large security obstacle to the implementation of CP-ABE in CCS. That is, how to identify the malicious cloud user who illegally shares his private keys with others or applies his keys to construct a decryption device/black-box, and provides the decryption service. Although several CP-ABE schemes with black-box traceability have been proposed to address the problem, most of them are not practical in CCS systems, due to the absence of scalability and expensive computation cost, especially the cost of tracing. Thus, we present a new black-box traceable CP-ABE scheme that is scalable and high efficient. To achieve a much better performance, our work is designed on the prime order bilinear groups that results in a great improvement in the efficiency of group operations, and the cost of tracing is reduced greatly to $O\left(N\right)$ or $O\left(1\right)$ , where N is the number of users of a system. Furthermore, our scheme is proved secure in a selective standard model. To the best of our knowledge, this work is the first such practical and provably secure CP-ABE scheme for CCS, which is black-box traceable.

For mobile agent path planning, traditional path planning algorithms frequently induce abrupt variations in path curvature and steering angles, increasing the risk of lateral tire slippage and undermining operational safety. Concurrently, conventional reinforcement learning methods struggle to converge rapidly, leading to an insufficient efficiency in planning to meet the demand for energy economy. This study proposes LSTM Bézier–Double Deep Q-Network (LB-DDQN), an advanced path-planning framework for mobile agents based on deep reinforcement learning. The architecture first enables mapless navigation through a DDQN foundation, subsequently integrates long short-term memory (LSTM) networks for the fusion of environmental features and preservation of training information, and ultimately enhances the path’s quality through redundant node elimination via an obstacle–path relationship analysis, combined with Bézier curve-based trajectory smoothing. A sensor-driven three-dimensional simulation environment featuring static obstacles was constructed using the ROS and Gazebo platforms, where LiDAR-equipped mobile agent models were trained for real-time environmental perception and strategy optimization prior to deployment on experimental vehicles. The simulation and physical implementation results reveal that LB-DDQN achieves effective collision avoidance, while demonstrating marked enhancements in critical metrics: the path’s smoothness, energy efficiency, and motion stability exhibit average improvements exceeding 50%. The framework further maintains superior safety standards and operational efficiency across diverse scenarios.

In application scenarios such as cloud storage, a symmetric algorithm can be used to encrypt massive data. However, using the symmetric operation and keys in encryption and decryption, it is very difficult to realize an efficient access control system. The asymmetric encryption algorithm ciphertext-policy attribute-based encryption (CP-ABE), as a versatile one-to-many encryption algorithm, is considered an ideal access control tool to solve the user’s distrust of the service provider of cloud storage. However, in a traditional CP-ABE system, the malicious users may deliberately leak their attribute keys to others or build a decryption black box (DB) to provide illegal decryption services for benefits, while no one can determine their identities. To deal with the problems, especially the problem of tracing the DB builder, CP-ABE schemes with black-box traceability have been proposed in the past few years. But in this paper, we point out that for now, the tracing algorithms in all existing schemes actually work on an impractical assumption. That is, whenever a DB receives a decryption request, it always performs the decryption algorithm honestlycorrectly. We argue that if a DB finds the decryption request comes from the tracing algorithm, it may intentionally output incorrect decryption results to counter the tracing. Thus, we present the structural defect of the tracing algorithm applied to all known traceable CP-ABE schemes. We describe the construction of an alert decryption black box (ADB) that is capable of distinguishing a tracing ciphertext from a normal one. We also show how an ADB frustrates the tracing algorithm, and furthermore, malicious users can even apply an ADB to frame innocent users.

As an asymmetric encryption method capable of performing one-to-many encryption, the ciphertext-policy attribute-based encryption (CP-ABE) is widely recognized as an ideal cryptographic tool for cloud-based applications. It can empower data owners to independently and flexibly define and enforce access control policies for cloud-stored data. However, the practical implementation of CP-ABE-based cryptographic access control remains hindered by critical challenges. Firstly, malicious users may engage in key abuse by delegating attribute keys to unauthorized parties or exploiting their keys to construct decryption black-boxes for providing illegal decryption services. Consequently, a secure CP-ABE scheme must incorporate the capability to trace such malicious users who misuse their privileges. Secondly, for resource-constrained IoT devices, the substantial computational overhead of CP-ABE becomes prohibitive, making its deployment in scenarios like IoT-cloud services particularly challenging. In this paper, we propose a new CP-ABE scheme with black-box traceability and computational outsourcing capabilities. Our scheme can improve the tracing efficiency from $O\left(N^3\right)$ or $O\left(rlogN\right)$ (as seen in traditional schemes) to $O\left(1\right)$, where N is the number of system users. Furthermore, the proposed scheme features compulsory traceability and maintains outstanding performance in the aspects of encryption, decryption, and tracing operations.