The Internet of Things (IoT) has been widely adopted for its efficient data processing capabilities, but it also poses significant data security challenges. Attribute-Based Encryption (ABE) has attracted attention for enabling fine-grained, attribute-based access control. However, traditional ABE schemes fall short in revoking decryption privileges and protecting policy privacy, especially in scenarios where users’ private keys are exposed over a long period or sensitive access policies are at risk of leakage. To address these issues, this paper proposes an attribute-based encryption scheme that integrates puncturable encryption with policy hiding. In the proposed scheme, users can independently update their private keys without relying on the key distributor, and can revoke decryption capabilities of old keys by puncturing tags embedded in the ciphertext. This mechanism not only significantly reduces communication overhead between users and IoT devices but also ensures the security of previously generated data even if private keys are compromised. It simplifies key management and provides flexible forward security. Moreover, the scheme achieves full access policy hiding through attribute-mapping obfuscation and leverages the immutability of blockchain to ensure that users can verify the integrity of the policy before decryption, thereby further enhancing data privacy and system trustworthiness. We formally prove the scheme's security under the decisional bilinear Diffie-Hellman assumption. Finally, experimental results demonstrate that our scheme offers advantages in both storage overhead and computational efficiency.

Cloud-assisted Internet of Things (IoT) provides resource-constrained IoT devices with powerful storage capabilities while also raising significant data privacy and security issues. Ciphertext-policy attribute-based encryption (CP-ABE) is widely used to protect the privacy and security of IoT data. However, the leakage of users’ long-term keys compromises the confidentiality of previously generated IoT data. In this paper, we propose a novel puncturable CP-ABE (Pt-CP-ABE) scheme based on the Learning with Errors (LWE) assumption. Pt-CP-ABE enables data owners to define access policies, thereby achieving fine-grained ciphertext access control. Furthermore, after decrypting the ciphertext, data users can update their private keys by themselves and revoke the decryption privileges of existing ciphertexts by puncturing the tags within the ciphertext. This ensures that even if the private key is compromised, the previously generated IoT data remains confidential, thereby achieving flexible forward security. We formalize the security definition for Pt-CP-ABE and prove the security of the Pt-CP-ABE scheme in the standard model. To the best of our knowledge, our Pt-CP-ABE scheme is the first CP-ABE scheme that supports flexible puncturing, forward security, and resistance to quantum computer attacks. Furthermore, by incorporating a revocation mechanism into our Pt-CP-ABE, we propose another Pt-CP-ABE scheme with user revocation functionality. Notably, this is the first ABE scheme to simultaneously achieve dual security guarantees of forward security and quantum resistance, along with user revocation.

The massive amount of data generated by the Internet of Things (IoT) and the need to store that data presents a huge challenge for storage. However, meeting this challenge has also driven the development of storage technologies, especially those related to cloud storage. Although attribute-based encryption (ABE) schemes are commonly used to achieve data confidentiality and fine-grained access control in cloud storage, there is still an inherent problem with ABE schemes, namely the key escrow problem. In this paper, we propose a revocable blockchain-aided ABE with escrow-free (BC-ABE-EF) system that resolves the key escrow problem by replacing the traditional key authority with a consortium blockchain. The keys are generated between the blockchain and the data user through a secure key issuing protocol, and the blockchain cannot obtain the user's full key alone. Furthermore, utilize the decryption cloud server to schedule pre-decryption operations in cloud and introduce a group manager to update the group keys of unrecovered users and generate re-encryption keys. The security analysis shows that our scheme is secure under the Decisional Computation Diffie Hellman (DCDH) assumption. The effectiveness of the scheme is demonstrated by simulating the BC-ABE-EF scheme and comparing it based on performance analysis.

Attribute-based encryption (ABE) is a preferred technology used to access control the data stored in the cloud servers. However, in many cases, the authorized decryption user may be unable to decrypt the ciphertext in time for some reason. To be on the safe side, several alternate users are delegated to cooperate to decrypt the ciphertext, instead of one user doing that. We provide a ciphertext-policy ABE scheme with shared decryption in this article. An authorized user can recover the messages independently. At the same time, these alternate users (semi-authorized users) can work together to get the messages. We also improve the basic scheme to ensure that the semi-authorized users perform the decryption tasks honestly. An integrated access tree is used to improve the efficiency for our scheme. The new scheme is proved CPA-secure in the standard model. The experimental result shows that our scheme is very efficient on both computational overhead and storage cost.

This article proposes a neural terminal sliding mode controller (TSMC) with finite-time (FT) convergence for the uncertain MEMS gyroscope dynamics. To address the uncertainty, considering the periodic tracking property of MEMS gyroscopes, a composite learning mechanism driven by the learning performance evaluation signal is applied to learn the system dynamics. By selecting the terminal sliding mode surface, the TSMC is constructed with error feedback and feedforward compensation. Under the TSMC with the composite learning, the system tracking can be guaranteed to be with FT convergence, while the weights of the learning system will converge in FT. The stability of the closed-loop system is analyzed by the Lyapunov approach. The highlight is that the design can obtain the learning knowledge while the information can be directly reused in repeated tasks with no need of online update. The effectiveness of the proposed method is verified via reference signal tracking of MEMS gyroscopes, while the controller using the stored knowledge achieves better tracking performance of faster convergence and higher tracking accuracy with no need of weight update.

The security for many certificate-based encryption schemes was considered under the ideal condition, where the attackers rarely have the secret state for the solutions. However, with a side-channel attack, attackers can obtain partial secret values of the schemes. In order to make the scheme more practical, the security model for the certificate-based encryption which is resilient to continual leakage is first formalized. The attackers in the security model are permitted to get some secret information continuously through the side-channel attack. Based on the certificate-based key encapsulation scheme, a novel certificate-based encryption scheme is proposed, which is resilient to the continual leakage. In the standard model, the new scheme we propose is proved to be secure under the decisional truncated q-augmented bilinear Diffie–Hellman exponent hard problem and the decisional 1-bilinear Diffie–Hellman inversion hard problem. Additionally, the new scheme can resist the chosen-ciphertext attack. Moreover, a comparison is performed with other related schemes, where the proposed solution further considers the continual leakage-resilient property and exhibits less computation cost.

This paper investigates the target tracking mission of an Unmanned Aerial Vehicle (UAV) equipped with a camera and a laser module. Firstly, utilizing Deep Neural Network (DNN) and Kernelized Correlation Filters (KCF), target recognition and location in the pixel coordinate system is achieved based on vision. Furthermore, by combining the laser ranging information and the distance estimation algorithm based on image, the distance between the UAV and the target is well estimated. To ensure the target tracking, a PID controller based on the distance error is applied to the UAV. The effectiveness of the system is verified on an actual UAV target tracking scenario.

In traditional airplane monitoring system (AMS), data sensed from strain, vibration, ultrasound of structures or temperature, and humidity in cabin environment are transmitted to central data repository via wires. However, drawbacks still exist in wired AMS such as expensive installation and maintenance, and complicated wired connections. In recent years, accumulating interest has been drawn to performing AMS via airborne wireless sensor network (AWSN) system with the advantages of flexibility, low cost, and easy deployment. In this review, we present an overview of AMS and AWSN and demonstrate the requirements of AWSN for AMS particularly. Furthermore, existing wireless hardware prototypes and network communication schemes of AWSN are investigated according to these requirements. This paper will improve the understanding of how the AWSN design under AMS acquires sensor data accurately and carries out network communication efficiently, providing insights into prognostics and health management (PHM) for AMS in future.

This paper addresses a terminal sliding mode strategy to control the attitude of quadrotor while achieving the finite time convergence. To deal with system uncertainty and time-varying disturbance, a hybrid controller using single-hidden layer feedforward network (SLFN) and disturbance observer (DOB) is proposed. Fast terminal sliding mode surface is designed to construct the sliding mode control. To improve learning speed, the updating law of SLFN weight utilizes the information of the fast terminal sliding mode. The effectiveness of the proposed controller is demonstrated with simulation example.

Abstract The continuous auxiliary inputs leakage is more strong side‐channel attacks. In this article, we first propose a continuous auxiliary inputs leakage model for the hierarchical attribute‐based encryption scheme. Under the security model, an adversary has ability to gain partial updated master keys and updated secret keys continually by certain leakage attacks. Moreover, a resilient‐leakage hierarchical attribute‐based encryption scheme is constructed. The security proof for this scheme is provided under the standard model. Furthermore, we give the performance comparison between our scheme and relevant scheme. Copyright © 2016 John Wiley & Sons, Ltd.

The situation for post-challenge continuous auxiliary input leakage has not been considered in the cryptography schemes for previous literature. We present a semantic-security model with post-challenge continuous auxiliary inputs for identity-based encryption. In this model, the adversary is permitted to obtain some information of the private keys constantly and to query more information after seeing the challenge ciphertext through the side-channel attacks. Furthermore, we present an identity-based encryption scheme resilient to leakage under composite order groups. Our scheme is secure against post-challenge continuous auxiliary input, adaptive chosen-identity, and adaptive chosen plaintext attacks under three static assumptions in the standard model. Compared with existing identity-based encryption schemes under security properties and performance, our scheme is practical. Copyright © 2015 John Wiley & Sons, Ltd.