Federated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data. FL-based systems can achieve much stronger privacy preservation since the distributed devices deliver only local model parameters trained with local data to a centralized server. However, there exists a possibility that a centralized server or attackers infer/extract sensitive private information using the structure and parameters of local learning models. We propose employing homomorphic encryption (HE) scheme that can directly perform arithmetic operations on ciphertexts without decryption to protect the model parameters. Using the HE scheme, the proposed privacy-preserving federated learning (PPFL) algorithm enables the centralized server to aggregate encrypted local model parameters without decryption. Furthermore, the proposed algorithm allows each node to use a different HE private key in the same FL-based system using a distributed cryptosystem. The performance analysis and evaluation of the proposed PPFL algorithm are conducted in various cloud computing-based FL service scenarios.

Reinforcement learning (RL) is a learning technique that enables state-dependent learning through feedback from an environment and makes an action decision for maximizing a reward without prior knowledge of the environment. If these RL techniques are used for data-centric services running on cloud computing, serious data privacy issues may occur because it is required to exchange privacy-related user data for RL-based services between the users and the cloud computing platform. We consider using homomorphic encryption (HE) scheme, which enables cloud computing platforms to perform arithmetic operations without decrypting ciphertexts. Using the HE scheme, users are allowed to deliver only ciphertexts to the cloud computing platform for using RL-based services. We propose a privacy-preserving reinforcement learning (PPRL) framework for the cloud computing platform. The proposed framework exploits a cryptosystem based on learning with errors (LWE) for fully homomorphic encryption (FHE). Performance analysis and evaluation for the proposed PPRL framework are conducted in a variety of cloud computing-based intelligent service scenarios.

Federated learning (FL) technology has emerged for efficient data collection, data privacy protection, and efficient utilization of computing resources. In FL-based systems, data owners only send intermediate model trained with their local data to a central FL server, and then the central server combines intermediate models to generate globally trained model instead of the central server's collecting raw data from the data owners. However, even if only the trained intermediate model is transmitted to the central server, there are still several instances of privacy infringements using intermediate model information. To prevent data privacy infringement from intermediate data, homomorphic encryption (HE) schemes that enable numerical operations with ciphertexts can be used. The HE schemes have a limitation that data can only be calculated when it is encrypted with the same encryption key. The proposed algorithm improves data security by developing a technique that enables homomorphic operations even if data is encrypted with different encryption keys.

In wireless local area networks (WLANs), the effect of interference signals between neighboring nodes increases as the number of wireless nodes using limited radio frequency resources in a limited space increases, which can significantly degrade the reliability of data transmission. In high-density WLANs, there can be several neighboring access points (APs) that can receive uplink transmission from a station. In conventional medium access control (MAC) protocols, uplink data frames containing errors or transmitted from a non-associated station are discarded at APs. Alternatively, we propose a MAC protocol using redundant wireless links between neighboring APs and the non-associated stations. In the proposed MAC protocol, we consider a centralized WLAN with a control node that performs error corrections of erroneous uplink data frames via a majority voting algorithm-based link-layer diversity scheme using uplink data received from multiple APs to increase the reliability of data transmission. In addition, we propose an adaptive carrier sensing ranging mechanism to improve the uplink network throughput in the proposed centralized WLAN system. Further, we conduct simulation studies and software-defined radio-based experiments to evaluate the performance of the proposed MAC protocol in various WLAN scenarios.