Cryptography and Security (cs.CR)

Abstract: Rapidly developing intelligent healthcare systems are underpinned by Sixth Generation (6G) connectivity, ubiquitous Internet of Things (IoT), and Deep Learning (DL) techniques. This portends a future where 6G powers the Internet of Medical Things (IoMT) with seamless, large-scale, and real-time connectivity amongst entities. This article proposes a Convolutional Neural Network (CNN) based Federated Learning framework that combines Secure Multi-Party Computation (SMPC) based aggregation and Encrypted Inference methods, all within the context of 6G and IoMT. We consider multiple hospitals with clusters of mixed IoMT and edge devices that encrypt locally trained models. Subsequently, each hospital sends the encrypted local models for SMPC-based encrypted aggregation in the cloud, which generates the encrypted global model. Ultimately, the encrypted global model is returned to each edge server for more localized training, further improving model accuracy. Moreover, hospitals can perform encrypted inference on their edge servers or the cloud while maintaining data and model privacy. Multiple experiments were conducted with varying CNN models and datasets to evaluate the proposed framework's performance.

Abstract: Due to the rising awareness of privacy and security in machine learning applications, federated learning (FL) has received widespread attention and applied to several areas, e.g., intelligence healthcare systems, IoT-based industries, and smart cities. FL enables clients to train a global model collaboratively without accessing their local training data. However, the current FL schemes are vulnerable to adversarial attacks. Its architecture makes detecting and defending against malicious model updates difficult. In addition, most recent studies to detect FL from malicious updates while maintaining the model's privacy have not been sufficiently explored. This paper proposed blockchain-based federated learning with SMPC model verification against poisoning attacks for healthcare systems. First, we check the machine learning model from the FL participants through an encrypted inference process and remove the compromised model. Once the participants' local models have been verified, the models are sent to the blockchain node to be securely aggregated. We conducted several experiments with different medical datasets to evaluate our proposed framework.

Abstract: Smart manufacturing systems involve a large number of interconnected devices resulting in massive data generation. Cloud computing technology has recently gained increasing attention in smart manufacturing systems for facilitating cost-effective service provisioning and massive data management. In a cloud-based manufacturing system, ensuring authorized access to the data is crucial. A cloud platform is operated under a single authority. Hence, a cloud platform is prone to a single point of failure and vulnerable to adversaries. An internal or external adversary can easily modify users' access to allow unauthorized users to access the data. This paper proposes a role-based access control to prevent modification attacks by leveraging blockchain and smart contracts in a cloud-based smart manufacturing system. The role-based access control is developed to determine users' roles and rights in smart contracts. The smart contracts are then deployed to the private blockchain network. We evaluate our solution by utilizing Ethereum private blockchain network to deploy the smart contract. The experimental results demonstrate the feasibility and evaluation of the proposed framework's performance.

Abstract: We design a model of Post Quantum Cryptography (PQC) Quantum Federated Learning (QFL). We develop a framework with a dynamic server selection and study convergence and security conditions. The implementation and results are publicly available1.

Abstract: Nowadays, embedded devices are increasingly present in everyday life, often controlling and processing critical information. For this reason, these devices make use of cryptographic protocols. However, embedded devices are particularly vulnerable to attackers seeking to hijack their operation and extract sensitive information. Code-Reuse Attacks (CRAs) can steer the execution of a program to malicious outcomes, leveraging existing on-board code without direct access to the device memory. Moreover, Side-Channel Attacks (SCAs) may reveal secret information to the attacker based on mere observation of the device. In this paper, we are particularly concerned with thwarting CRAs and SCAs against embedded devices, while taking into account their resource limitations. Fine-grained code diversification can hinder CRAs by introducing uncertainty to the binary code; while software mechanisms can thwart timing or power SCAs. The resilience to either attack may come at the price of the overall efficiency. Moreover, a unified approach that preserves these mitigations against both CRAs and SCAs is not available. This is the main novelty of our approach, Secure Diversity by Construction (SecDivCon); a combinatorial compiler-based approach that combines software diversification against CRAs with software mitigations against SCAs. SecDivCon restricts the performance overhead in the generated code, offering a secure-by-design control on the performance-security trade-off. Our experiments show that SCA-aware diversification is effective against CRAs, while preserving SCA mitigation properties at a low, controllable overhead. Given the combinatorial nature of our approach, SecDivCon is suitable for small, performance-critical functions that are sensitive to SCAs. SecDivCon may be used as a building block to whole-program code diversification or in a re-randomization scheme of cryptographic code.