Pramod Gaur

Advancements in the Connected and Autonomous Vehicles (CAVs) industry are revolutionizing modern transportation through advanced automation levels and connectivity capabilities. While autonomous vehicles can operate using onboard sensors alone, the integration of Vehicle-to-Everything (V2X) communication is vital for enabling seamless connectivity and cooperative decision-making. However, the increasing exchange of traffic and sensor data introduces critical privacy challenges, necessitating robust and scalable privacy-preserving mechanisms to ensure user trust and compliance with data protection regulations. The inherently dynamic nature of CAV environments, characterized by high mobility, short-duration connections, and frequent handovers, further complicates the design of effective privacy models. In this context, this paper investigates the evolving data privacy risks associated with CAV systems. It critically reviews existing privacy-preserving approaches and identifies their limitations in dynamic vehicular contexts. In particular, the paper explores the role of Federated Learning, permissioned blockchain and Software-Defined Networking (SDN) as enabling technologies for privacy preservation in CAVs. The analysis concludes with targeted recommendations for optimizing these frameworks to enhance privacy resilience in next-generation intelligent transportation systems.

The need for repeated calibration and accounting for inter subject variability is a major challenge for the practical applications of a brain-computer interface. The problem becomes more severe when it is applied for the neurorehabilitation of stroke patients where the brain-activation pattern may differ quite a bit as compared to healthy individuals due to the altered neurodynamics because of a lesion. There were several approaches to handle this problem in the past that depend on creating customized features that can be generalized among the individual subjects. Recently, several deep learning architectures came into the picture although they often failed to produce superior accuracy as compared to the traditional approaches and mostly do not follow an end-to-end architecture as they depend on custom features. However, a few of them have the promising ability to create more generalizable features in an end-to-end fashion such as the popular EEGNet architecture. Although EEGNet was applied for decoding stroke patient’s motor imagery (MI) data with limited success it failed to achieve superior performance over the traditional methods. In this study, we have augmented the EEGNet based decoding by introducing a post-processing step called the longest consecutive repetition (LCR) in a sliding window-based approach and named it EEGNet+LCR. The proposed approach was tested on a dataset of 10 hemiparetic stroke patients’ MI data set yielding superior performance against the only EEGNet and a more traditional approach such as common spatial pattern (CSP)+support vector machine (SVM) for both within- and cross-subject decoding of MI signals. We also observed comparable and satisfactory performance of the EEGNet+LCR in both the within- and cross-subject categories which are rarely found in literature making it a promising candidate to realize practically feasible BCI for stroke rehabilitation.

The increase in popularity and users of the Android platform in recent years has led to a lot of innovative and smart Android applications (apps). Many of these apps are highly interactive, customizable, and require user data to provide services. While being convenient, user privacy is the primary concern. It is not guaranteed that these apps are not storing user data for their need or scrapping algorithms through them. Android uses the system of permissions to provide security and protect user data. The user can grant permission for requested resources either at runtime or during the installation process. However, this system is often misused in practice by demanding extra permissions that are not required to provide services. These kinds of apps stop functioning if all permissions are not granted to them. Therefore, in this paper, a privacy preserved secure framework is proposed to prevent an app from stealing user data by restricting all unnecessary permissions. Unnecessary permissions are recognized by predicting the permissions required by a given app by using collaborative filtering and frequent permission set mining algorithms. Thus, the proposed model interacts with the target application and modifies the permission data inside. Experimental results reveal that the proposed model not only protects the user data but also ensures the proper functioning of the given application.

Brain-computer interface (BCI) performance is often impacted due to the inherent non-stationarity in the recorded EEG signals coupled with a high variability across subjects. This study proposes a novel method using Logistic Regression with Tangent Space-based Transfer Learning (LR-TSTL) for motor imagery (MI)-based BCI classification problems. The single-trial covariance matrix (CM) features computed from the EEG signals are transformed into a Riemannian geometry frame and tangent space features are computed by considering the lower triangular matrix. These are then further classified using the logistic regression model to improve classification accuracy. The performance of LR-TSTL is tested on healthy subjects’ dataset as well as on stroke patients’ dataset. As compared to existing within-subject learning approaches the proposed method gave an equivalent or better performance in terms of average classification accuracy (78.95±11.68%), while applied as leave-one-out cross-subject learning for healthy subjects. Interestingly, for the patient dataset LR-TSTL significantly (p<0.05) outperformed the current benchmark performance by achieving an average classification accuracy of 81.75±6.88%. The results show that the proposed method for cross-subject learning has the potential to realize the next generation of calibration-free BCI technologies with enhanced practical usability especially in the case of neurorehabilitative BCI designs for stroke patients.B.

Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain-computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance the binary classification of MI. The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows and is named SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a data set of healthy individuals and on a stroke patients' data set. Compared with the existing state of the art, the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients' data set for left- versus right-hand MI with lower standard deviation. For both the data sets, the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window-based prediction of MI using SW-LCR and SW-Mode is robust against intertrial and intersession inconsistencies in the time of activation within a trial and thus can lead to a reliable performance in a neurorehabilitative BCI setting.