Extensive research has been done on the security of symmetric encryption algorithms in the black-box attack contexts, where the execution platforms are supposed to be secure. Recent studies intend to secure encryption algorithms in a more challenging but widely adopted scenario, the white-attack context (WBAC), where the adversaries have full visibility of the implementations of cryptosystems and full control over execution platforms. Various of approaches for protecting symmetric encryption algorithms in the WBAC have been proposed. Unfortunately, most existing approaches have been broken. This paper proposes a novel approach for securing symmetric encryption algorithms based on substitution-permutation network (SPN). Our key idea is to incorporate additional secret components into lookup tables to expand and dramatically change the inner states of encryption. Unlike existing approaches, these components do not need to be annihilated in adjacent rounds, and the ciphertext remains essentially unchanged. To recover the plaintext, only simple operations and the standard decryption algorithm are required. Our approach can be applied to protect SPN-based symmetric encryption algorithms such as AES. Security analysis indicates that the approach is expected to be resistant to both existing and unknown attacks. Furthermore, experimental evaluation shows that our approach performs well on various platforms.

Video has become the primary channel for people to access information and engage in leisure and entertainment activities. Video service platforms have emerged to meet these demands and create significant profits. These platforms aggregate diverse videos and offer subscription services, aiming to provide convenient and personalized viewing experiences. To safeguard the copyright of video content against unauthorized access, these platforms employ digital rights management (DRM) technologies. However, the use of DRM faces significant security challenges due to the presence of malware or malicious users, which can compromise the decryption program running on user devices and lead to illegal video content distribution. This paper proposes a high-performance encryption scheme for video DRM protection. We establish the security of our proposed scheme through rigorous mathematical proof and analyze its effectiveness in the context of the video DRM scenario. Furthermore, the experimental results demonstrate that our scheme outperforms other schemes. Overall, our proposed scheme offers a promising solution to the security challenges faced by DRM technologies in protecting video content. Its efficiency, security, and resilience to various attacks make it a viable option for video service platforms seeking to enhance the security of their digital content.

White-box cryptography is widely used in Internet of Things (IoT) devices to ensure data confidentiality. However, the traditional implementations of white-box cryptographic algorithms (WBCAs) are inefficient and impractical for IoT devices that require fast encryption and decryption of large amounts of data. To address this challenge, we propose a framework that can accelerate WBCAs employed by IoT devices with Graphics Processing Units (GPUs). Our framework leverages the inherent multithreading capabilities of GPUs to simultaneously perform a large number of table lookups required in WBCAs. Additionally, we employ pipelined execution strategies to enhance performance further. To demonstrate the effectiveness of our framework, we apply it to two well-known WBCAs and evaluate its performance on two IoT devices. The experimental results show that our framework can improve performance by up to 70 times compared with the execution on a single-threaded CPU.