Access control encryption (ACE) is an innovative cryptographic primitive that realizes fine-grained read/write control of data and protects data privacy and security while facilitating the effective flow of information. However, existing ACE schemes face several limitations: 1) Inability to adequately mitigate the risks of a single point of failure in the sanitizer. 2) Lack of an effective accountability mechanism for disputes arising during the sanitization process. To solve these problems, this paper proposes the notion of traceable access control encryption with parallel multiple sanitizers for the first time and designs a specific structure of traceable parallel ACE to prevent the single point of failure, effectively deter abnormal sanitizer behaviors, and optimize system performance. Additionally, computationally intensive operations in the encryption and decryption processes are outsourced to third-party servers, resulting in a significant reduction of computational overhead. Furthermore, theoretical analysis and experimental simulations validate the effectiveness of the proposed scheme. Comprehensive security analysis demonstrates its no-read security under the decisional q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption and its no-write security under the Discrete Logarithm (DL) assumption, ensuring its reliability in practical applications.

Abstract Wireless sensor networks (WSNs) have been widely used in different fields, such as battle field, environment monitoring, and intrusion detection, where amount of nodes are deployed. There are many factors make sensor nodes being more vulnerable to be attacked and compromised. In order to address the issue, many different schemes and methods are proposed. Trust evaluation and trust management play an important role in detecting the malicious nodes. In this paper, we propose a trust management system for clustered WSNs monitoring the sensor nodes' behaviors and evaluating their trust values. This system employs a hash algorithm for generating identify labels for sensor nodes to distinguish external attackers from normal nodes and dynamically manages the trust value of each node to detect the compromised nodes based on the trust evaluating model, which is based on beta density function. The simulation results show that our scheme can detect the malicious nodes quickly, which prevents clustered WSNs from external attacks and internal compromised nodes' attacks. Copyright © 2015 John Wiley & Sons, Ltd.