Cloud platforms have become a primary environment for training machine learning models. However, the security of sensitive data could be compromised due to storage and computation in the cloud provided by the third party. Fully Homomorphic Encryption over the Torus (TFHE) offers a promising solution to this issue. However, integrating the wildly used Artificial Neural Networks (ANNs) with TFHE still faces significant challenges, particularly in the efficient implementation of activation functions and arithmetic operations. In this work, we present a parallel implementation of activation functions and arithmetic operations based on TFHE. By leveraging the parallel computing capabilities of multi-core CPUs and GPUs, our approach significantly improves computational performance. Experimental results demonstrate that our method achieves competitive accuracy while delivering a 12x speedup for 32-bit multiplication on GPUs and up to 30x acceleration for ReLU computations across various bit widths.
The Internet of Things (IoT) is fundamentally reshaping various industries by enabling efficient data collection and sharing. However, the prevalence of centralized cloud architectures introduces significant latency and high computational burdens, which are often incompatible with the real-time demands of resource-constrained edge devices. While edge computing provides a viable alternative, critical issues of data privacy and integrity remain, as a single data breach can lead to severe consequences. To address this dual challenge of security and efficiency, we introduce a novel edge-based computing framework. Our approach leverages secret-sharing-based Secure Multi-Party Computation alongside a terminal data source auditing mechanism to achieve secure model inference. We developed highly optimized protocols for Neural Network inference layers, significantly reducing communication overhead by integrating both arithmetic and boolean secret sharing. An innovative secure comparison protocol further minimizes server interactions, thereby boosting the efficiency of nonlinear computations. In addition, our framework incorporates a signature scheme to verify data legitimacy. Experimental evaluations on public datasets confirm that our method surpasses existing solutions in overall performance and security, validating its practical feasibility in modern IoT environments.
Federated learning (FL) enables social networks to utilize distributed user data without direct exposure. However, when FL updates are encrypted to prevent gradient inversion and privacy leakage, observability is significantly reduced and traditional anomaly detection is weakened. This creates opportunities for ciphertext poisoning while cryptographic audits incur prohibitive costs at scale. The key challenge is to design defenses that balance privacy protection, detection capability, and resource constraints. In this paper, we address this challenge by proposing an evolutionary game theoretic defense framework for encrypted FL in social networks. The framework models detection intensity of the server and poisoning propensity of clients as co-evolving strategies. It explicitly incorporates false positive penalties, external incentives for attackers, and practical constraints such as bandwidth and energy budgets. Through equilibrium analysis, we show how detection policies adapt to varying costs and incentives, and we identify stable strategy profiles under different deployment conditions. The framework yields actionable guidance for tuning server-side defenses, offering adaptive and cost-aware protection against ciphertext poisoning in large-scale social networks.
With rapid development of smart grids, how to efficiently aggregate and verify multi-source electricity consumption data while ensuring user privacy has become a key research problem, and a number of privacy-preserving data aggregation schemes have been introduced to alleviate this problem. However, most existing schemes only support simple additive homomorphic aggregation, lacking support for more complex functions. To address this challenge, this paper proposes an efficient and verifiable privacy-preserving data aggregation scheme that integrates functional encryption, Diffie–Hellman key exchange, and Schnorr signature to support linear function queries, resists collusion attacks, and ensures verifiability of aggregation results. Experimental results demonstrate that our proposed scheme achieves higher computational and communication efficiency compared with the existing schemes, e.g., GuardGrid, while maintaining strong security and functional expressiveness. Therefore, it provides a feasible and effective approach for building efficient, scalable, and verifiable privacy-preserving systems in smart grids.
With the growing demand for cross-platform collaboration in blockchain systems, cross-chain interoperability mechanisms have attracted widespread attention. However, the lack of trust among cross-chain entities poses serious challenges to the privacy protection of inter-chain data. Attribute-based access control (ABAC) enables fine-grained and flexible permission management, but existing schemes often disclose policies and attributes in plaintext, making it easier for attackers to exploit public on-chain information to launch attacks. To address this issue, this paper proposes a cross-chain access control scheme that combines ABAC with threshold fully homomorphic encryption (ThFHE). The scheme utilizes Cheon–Kim–Kim–Song (CKKS), a widely-used fully homomorphic encryption (FHE) scheme, to encrypt access policies and user attributes. Benefiting from the properties of FHE, smart contracts can perform permission verification directly in the ciphertext domain without exposing any sensitive information. Furthermore, the scheme achieves decentralized collaborative decryption by aggregating the partial decryption results from multiple relay nodes. Experimental results demonstrate that the scheme achieves secure and efficient cross-chain data access control with reasonable computational overhead.
