Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems. MI tasks are performed by imagining doing a specific task and classifying MI through EEG signal processing. However, it is a challenging task to classify EEG signals accurately. In this study, we propose a LSTM-based classification framework to enhance classification accuracy of four-class MI signals. To obtain time-varying data of EEG signals, a sliding window technique is used, and an overlapping-band-based FBCSP is applied to extract the subject-specific spatial features. Experimental results on BCI competition IV dataset 2a showed an average accuracy of 97% and kappa value of 0.95 in all subjects. It is demonstrated that the proposed method outperforms the existing algorithms for classifying the four-class MI EEG, and it also illustrates the robustness on the variability of inter-trial and inter-session of MI data. Furthermore, the extended experimental results for channel selection showed the best performance of classification accuracy when using all twenty-two channels by the proposed method, but an average kappa value of 0.93 was achieved with only seven channels.

In order to protect each node’s local learning parameters from model inversion attacks, secure aggregation has become the essential technique for federated learning so that the federated learning server knows only the combined result of all local parameters. In this paper, we introduced a novel cluster-based secure aggregation model that effectively deals with dropout nodes while reducing communicational and computational overheads. Specifically, we considered a federated learning environment with heterogeneous devices deployed across the country. The computing power of each node and the amount of training data can be heterogeneous. Because of this, each node had a different local processing time, and the response time to the server is also different. To clearly determine the dropout nodes in this environment, our model clusters nodes with similar response times based on each node’s local processing time and location and then performs the aggregation on a pre-cluster basis. In addition, we propose a new practical additive sharing-based masking protocol to hide the actual local updates of nodes during aggregation. The new masking protocol makes it easy to remove the share of dropout nodes from the aggregation without using a (t, n) threshold scheme, and updates from dropout nodes are still secure even if they are delivered to the server after the dropout shares have been revealed. In addition, our model provides mask verification for reliable aggregation. Nodes can publicly verify the correctness and integrity of the masks received from others using a discrete logarithm problem before the aggregation. As a result, the proposed aggregation model is robust to dropout nodes and ensures the privacy of local updates if at least three honest nodes are alive in each cluster. Since the masking process is performed on a cluster basis, our model effectively reduces the overhead of generating and sharing the masking value. For an average cluster size $\mathcal{C}$ and a total number of nodes N, the computation and communication cost of each node is O($\mathcal{C}$), the computation cost of the server is O($N$), and the communication cost is O($N\mathcal{C}$). We analyzed the security and efficiency of our protocol by simulating diverse dropout scenarios. The simulated results showed that our cluster-based secure aggregation outputs about a 91% learning accuracy regardless of dropout rate with four clusters for one hundred nodes.

As eBook readers have expanded on the market, various online eBook markets have arisen as well. Currently, the online eBook market consists of at least publishers and online platform providers and authors, and these actors inevitably incur intermediate costs between them. In this paper, we introduce a blockchain-based eBook market system that enables self-published eBook trading and direct payments from readers to authors without any trusted party; because authors publish themselves and readers purchase directly from authors, neither actor incurs any intermediate costs. However, because of this trustless environment, the validity, ownership and intellectual property of digital contents cannot be verified and protected, and the safety of purchase transactions cannot be ensured. To address these shortcomings, we propose a secure and reliable eBook transaction system that satisfies the following security requirements: (1) verification of the ownership of each eBook, (2) confidentiality of eBook contents, (3) authorization of a right to read a book, (4) authentication of a legitimate purchaser, (5) verification of the validity and integrity of eBook contents, (6) safety of direct purchase transactions, and (7) preventing eBook piracy and illegal distribution. We provide practical cryptographic protocols for the proposed system and analyze the security and simulated performance of the proposed schemes.

Secure aggregation of local learning model parameters is crucial for achieving privacy-preserving federated learning. This paper presents a novel and practical aggregation method that effectively combines the advantages of masking-based aggregation with those of homomorphic encryption-based techniques. Each node conceals its local parameters using a randomly selected mask, independently chosen, thereby eliminating the need for additional computations to generate or exchange mask values with other nodes. Instead, each node homomorphically encrypts its random mask using its own encryption key. During each federated learning round, nodes send their masked parameters and the homomorphically encrypted mask to the federated learning server. The server then aggregates these updates in an encrypted state, directly calculating the average of actual local parameters across all nodes without the necessity to decrypt the aggregated result separately. To facilitate this, we introduce a new multi-key homomorphic encryption technique tailored for secure aggregation in federated learning environments. Each node uses a different encryption key to encrypt its mask value. Importantly, the ciphertext of each mask includes a partial decryption component from the node, allowing the collective sum of encrypted masks to be automatically decrypted once all are aggregated. Consequently, the server computes the average of the actual local parameters by simply subtracting the decrypted total sum of mask values from the cumulative sum of the masked local parameters. Our approach effectively eliminates the need for interactions between nodes and the server for mask generation and sharing, while addressing the limitation of a single key homomorphic encryption. Moreover, the proposed aggregation process completes the global model update in just two interactions (in the absence of dropouts), significantly simplifying the aggregation procedure. Utilizing the CKKS (Cheon-Kim-Kim-Song) homomorphic encryption scheme, our method ensures efficient aggregation without compromising security or accuracy. We demonstrate the accuracy and efficiency of the proposed method through varied experiments on MNIST data.

Automatically recognizing dangerous situations for a vehicle and quickly sharing this information with nearby vehicles is the most essential technology for road safety. In this paper, we propose a real-time deceleration pattern-based traffic risk detection system using smart mobile devices. Our system detects a dangerous situation through machine learning on the deceleration patterns of a driver by considering the vehicle’s headway distance. In order to estimate the vehicle’s headway distance, we introduce a practical vehicle detection method that exploits the shadows on the road and the taillights of the vehicle. For deceleration pattern analysis, the proposed system leverages three machine learning models: neural network, random forest, and clustering. Based on these learning models, we propose two types of decision models to make the final decisions on dangerous situations, and suggest three types of improvements to continuously enhance the traffic risk detection model. Finally, we analyze the accuracy of the proposed model based on actual driving data collected by driving on Seoul city roadways and the Gyeongbu expressway. We also propose an optimal solution for traffic risk detection by analyzing the performance between the proposed decision models and the improvement techniques.