The rapid evolution of the Industrial Internet of Things (IIoT) has driven unprecedented growth in industrial data volumes. To enhance cost efficiency and data-sharing capabilities, massive amounts of this data are stored in the cloud. Public Key Encryption with Keyword Search (PEKS) technology enables efficient encrypted data retrieval without key management and distribution issues and has been extensively studied for this purpose. However, due to inherent IIoT characteristics—such as heterogeneous data formats, resource-constrained devices, and heightened vulnerability to attacks, existing PEKS schemes face significant limitations: 1) typically restricted to single-keyword searches; 2) prohibitive computational overhead for resource-limited IIoT devices; 3) heightened risks of exploitation by attackers. To address these issues, we propose a lightweight certificateless authenticated encryption with multi-keyword search scheme, named CLAEMKS. It enables efficient multi-keyword search while substantially enhancing computational efficiency by eliminating the intensive bilinear pairing operations. Meanwhile, by leveraging certificateless cryptography, CLAEMKS solves certificate management problems while avoiding key escrow issues. Furthermore, formal security proofs and efficiency analyses are conducted to validate the effectiveness of our proposed scheme. The results demonstrate that CLAEMKS delivers substantial performance and security improvements.

Abstract In wireless networks, the Contention Window (CW) is a crucial parameter for wireless channel sharing among numerous stations, directly influencing overall network performance. With the rapid growth and increasing diversity of traffic in wireless networks, differentiating and serving various types of traffic to enhance network performance has become a pressing issue that needs to be addressed. Deep Reinforcement Learning (DRL) optimizes decision-making through the interaction between an agent and its environment, enabling it to handle complex state spaces and high-dimensional data, making it particularly suitable for dynamically adjusting the contention window and adapting to environmental changes in real time. Access Category (AC) is used to differentiate various types of traffic to support different quality of service (QoS) requirements. Therefore, this paper proposes the integration of DRL techniques into the EDCA mechanism, utilizing AC to differentiate network traffic, while the DRL technology adjusts the contention window to adequately ensure QoS for different types of traffic and enhance overall network performance. This paper proposes a CW back off scheme based on DRL for differentiating ACs. The scheme uses DRL technology to observe the channel collision rate and sense the current network conditions, thereby adaptively adjusting the CW size for ACs. In addition, it dynamically adjusts the back off strategy according to the perceived current network conditions to optimize the data transmission process. In the range of 20–120 stations, the scheme was tested in both single AC and multiple AC traffic scenarios, demonstrating excellent performance. The collision rate consistently remained below 18%, while the normalized throughput was maintained above 76%. Additionally, there is a significant improvement compared to existing deep reinforcement learning-based optimization schemes. Experimental results show that this scheme effectively discriminates between different ACs, resulting in lower latency and higher throughput for high-priority traffic. Furthermore, adaptively adjusting the CW size and improving the back off strategy maintains a low collision rate and stable throughput even under heavy load conditions, significantly improving the overall network performance.

n IEEE 802.11 networks, the Contention Window (CW) is a crucial parameter for wireless channel sharing among numerous stations, directly influencing overall network performance. In order to mitigate the performance degradation caused by the increasing number of stations in the network, we propose a novel adaptive CW backoff scheme, termed the ACWB-DQN algorithm. This algorithm leverages the Deep Q-Leaning Network (DQN) to explore a CW threshold, which is utilized as a boundary to differentiate the network load circumstances and learn the best configurations for different network conditions. When stations transmit data frames, different CW optimization strategies are employed based on station transmission status and the CW threshold. This approach aims to enhance network performance by adjusting the CW to increase transmission efficiency when there are fewer competing stations, and lower collision probabilities when there are more competing stations. Simulation results indicate that this approach can optimize station CW, reduce network collision rates, maintain constant throughput and significantly enhance the performance of Wi-Fi networks by means of adjusting the CW threshold according to real-time network conditions.

The security of secret keys for blockchain-based applications is increasingly important, partly because the theft of secret keys will render a significant financial loss. To guarantee the security of secret keys, many multi-party signature protocols have been proposed. However, few of them are designed for EdDSA-based blockchain that is developing in growth. The folklore and the NIST document for standardizing threshold schemes believe that a distributed hash evaluation is required to design multi-party EdDSA protocols, which leads to a relatively large overhead. In this paper, we present two practical multi-party EdDSA protocols for semi-honest and malicious settings. Our protocols eliminate the distributed hashing by securely maintaining a global state, which is feasible for EdDSA-based blockchain. Furthermore, we extend the malicious protocol to resist DoS attacks by identifying corrupted parties in case of execution aborts. We implemented our EdDSA protocols for different parties using Alibaba cloud servers with all instances of type ecs.t5-c1m2.large. Our protocol in the malicious setting takes 1.51-15.3 ms between 2 parties and 5 parties, and are two orders of magnitude faster than the recent threshold EdDSA protocol. These properties (efficient, identifiable abort, high compatibility) make the two protocols ideal for threshold wallets for EdDSA-based cryptocurrency.

The Internet of Things (IoT) is gradually stepping out of its infancy into maturity. Its widespread applications cover from tiny wearable devices to large industrial systems. Although many security solutions have been introduced to address data security and privacy problems caused by the unique characteristics of IoT, how to simultaneously achieve data confidentiality, protect the privacy of access control policy, and provide reasonable data source identification has been a challenging problem. Moreover, lacking one of the above properties may result in serious issues (e.g., leakage information and forging identity), and the situation grows steadily worse with the expansion of “things” scale. To address the above issues, we propose a new cryptographic primitive named certificateless matchmaking encryption (CL-ME), which inherits the security properties of certificateless cryptosystem and matchmaking encryption. Meanwhile, we also present two effective concrete constructions with formal security proofs based on the standard hard assumptions. The basic construction is the first instance of CL-ME based on bilinear pairing, and the enhanced construction is a pairing-free lightweight solution. Finally, we implement our proposed schemes using popular cryptography library and compare their performance with existing works. Theoretical analysis and experimental evaluations demonstrate that our proposed schemes are more suitable for IoT environment.

The Industrial Internet of Things (IIoT), as a special form of Internet of Things (IoT), has great potential in realizing intelligent transformation and industrial resource utilization. However, there are security and privacy concerns about industrial data, which is shared on an open channel via sensor devices. To address these issues, many searchable encryption schemes have been presented to provide both data privacy-protection and data searchability. However, due to the use of expensive pairing operations, most previous schemes were inefficient. Recently, a certificateless searchable public-key encryption (CLSPE) scheme was designed by Lu et al. to remove the pairing operation. Unfortunately, we find that Lu et al.’s scheme is vulnerable to user impersonation attacks. To enhance the security, a new pairing-free dual-server CLSPE (DS-CLSPE) scheme for cloud-based IIoT deployment is designed in this paper. In addition, we provide security and efficiency analysis for DS-CLSPE. The analysis results show that DS-CLSPE can resist chosen keyword attacks (CKA) and has better efficiency than other related schemes.

The maturity of cloud computing, the Internet of Things technology, and intelligent transportation system has promoted the rapid development of vehicular ad-hoc networks (VANETs). To keep pace with real-world demands (mobility, low latency, etc.) in a practical VANETs deployment, there have been attempts to integrate fog computing with VANETs. To facilitate secure interaction in fog-based VANETs, we design a new authenticated key agreement protocol without bilinear pairing. This protocol achieves mutual authentication, generates a securely agreed session key for secret communication, and supports privacy protection. We also give a strict formal security proof and demonstrate how the proposed protocol meets the security requirements in the fog-based VANETs. We then evaluate the efficiency of the proposed protocol, and it shows the practicality of the protocol.

Industrial Internet of Things (IIoT) integrates various types of intelligent terminals, mobile devices, and communication technologies to enable the upgrade of traditional industries to intelligent industries. IIoT relies on the powerful data processing capabilities of cloud computing to reduce the cost of various on-demand services as per the requirements of users. However, the privacy and confidentiality of the outsourced data should be protected in this environment because the data are typically “handled” by a third-party service provider. An encryption technique can guarantee the confidentiality of the data but it limits data retrieval due to its innate “all-or-nothing” decryption feature. To apply encryption to privacy-preserving data retrieval, many public key encryption techniques with keyword search systems have been proposed in the literature. However, most of the existing schemes are vulnerable to inside keyword guessing attack (IKGA), which is caused by a small keyword space. To address this problem, we propose a certificateless public key authenticated encryption with keyword search scheme, which is provably secure against IKGA. A performance analysis of the proposed scheme demonstrates that it is more secure and effective compared with other certificateless public key encryption with keyword search schemes.

With the widespread adoption of Internet of Things and cloud computing in different industry sectors, an increasing number of individuals or organizations are outsourcing their Industrial Internet of Things (IIoT) data in the cloud server to achieve cost saving and collaboration (e.g., data sharing). However, in this environment, preserving the privacy of data remains a key challenge and inhibiting factor to an even wider adoption of IIoT in the cloud environment. To mitigate these issues, in this paper, we design a new secure channel-free certificateless searchable public key encryption with multiple keywords scheme for IIoT deployment. We then demonstrate the security of the scheme in the random oracle model against two types of adversaries, where one adversary is given the power to choose a random public key instead of any user's public key and another adversary is allowed to learn the system master key. In the presence of these types of adversaries, we evaluate the performance of the proposed scheme and demonstrate that it achieves (computational) efficiency with low communication cost.