7 research outputs found
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks
The applications concerning vehicular networks benefit from the vision of
beyond 5G and 6G technologies such as ultra-dense network topologies, low
latency, and high data rates. Vehicular networks have always faced data privacy
preservation concerns, which lead to the advent of distributed learning
techniques such as federated learning. Although federated learning has solved
data privacy preservation issues to some extent, the technique is quite
vulnerable to model inversion and model poisoning attacks. We assume that the
design of defense mechanism and attacks are two sides of the same coin.
Designing a method to reduce vulnerability requires the attack to be effective
and challenging with real-world implications. In this work, we propose
simulated poisoning and inversion network (SPIN) that leverages the
optimization approach for reconstructing data from a differential model trained
by a vehicular node and intercepted when transmitted to roadside unit (RSU). We
then train a generative adversarial network (GAN) to improve the generation of
data with each passing round and global update from the RSU, accordingly.
Evaluation results show the qualitative and quantitative effectiveness of the
proposed approach. The attack initiated by SPIN can reduce up to 22% accuracy
on publicly available datasets while just using a single attacker. We assume
that revealing the simulation of such attacks would help us find its defense
mechanism in an effective manner.Comment: 6 pages, 4 figure
ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review
ChatGPT is another large language model (LLM) inline but due to its
performance and ability to converse effectively, it has gained a huge
popularity amongst research as well as industrial community. Recently, many
studies have been published to show the effectiveness, efficiency, integration,
and sentiments of chatGPT and other LLMs. In contrast, this study focuses on
the important aspects that are mostly overlooked, i.e. sustainability, privacy,
digital divide, and ethics and suggests that not only chatGPT but every
subsequent entry in the category of conversational bots should undergo
Sustainability, PrivAcy, Digital divide, and Ethics (SPADE) evaluation. This
paper discusses in detail about the issues and concerns raised over chatGPT in
line with aforementioned characteristics. We support our hypothesis by some
preliminary data collection and visualizations along with hypothesized facts.
We also suggest mitigations and recommendations for each of the concerns.
Furthermore, we also suggest some policies and recommendations for AI policy
act, if designed by the governments.Comment: 15 pages, 5 figures, 4 table
VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors
After affecting the world in unexpected ways, the virus has started mutating which is evident with the insurgence of its new variants. The governments, hospitals, schools, industries, and humans, in general, are looking for a potential solution in the vaccine which will eventually be available, but its timeline for eradicating the virus is yet unknown. Several researchers have encouraged and recommended the use of good practices such as physical healthcare monitoring, immunity boosting, personal hygiene, mental healthcare, and contact tracing for slowing down the spread of the virus. In this article, we propose the use of smart sensors integrated with the Internet of Medical Things to cover the spectrum of good practices in an automated manner. We present hypothetical frameworks for each of the good practice modules and propose the VIrus Resistance Framework using the Internet of Medical Things (VIRFIM) to tie all the individual modules in a unified architecture. Furthermore, we validate the realization of VIRFIM framework with two case studies related to physical activity monitoring and stress detection services. We envision that VIRFIM would be influential in assisting people with the new normal for current and future pandemics as well as instrumental in halting the economic losses, respectively. We also provide potential challenges and their probable solutions in compliance with the proposed VIRFIM
Classification of Broken Rice Kernels using 12D Features
Integrating the technological aspect for assessment of rice quality is very much needed for the Asian
markets where rice is one of the major exports. Methods based on image analysis has been proposed for
automated quality assessment by taking into account some of the textural features. These features are
good at classifying when rice grains are scanned in controlled environment but it is not suitable for
practical implementation. Rice grains are placed randomly on the scanner which neither maintains the
uniformity in intensity regions nor the placement strategy is kept ideal thus resulting in false
classification of grains. The aim of this research is to propose a method for extracting set of features
which can overcome the said issues. This paper uses morphological features along-with gray level and
Hough transform based features to overcome the false classification in the existing methods. RBF
(Radial Basis function) is used as a classification mechanism to classify between complete grains and
broken grains. Furthermore the broken grains are classified into two classes? i.e. acceptable grains and
non-acceptable grains. This research also uses image enhancement technique prior to the feature extraction and classification process based on top-hat transformation. The proposed method has been simulated in MATLAB to visually analyze and validate the results
PROMPT: PROcess Mining and Paravector Tensor based Physical Health Monitoring Framework
The provision of physical healthcare services during the isolation phase is one of the major challenges associated with the current COVID-19 pandemic. Smart healthcare services face a major challenge in the form of human behavior, which is based on human activities, complex patterns, and subjective nature. Although the advancement in portable sensors and artificial intelligence has led to unobtrusive activity recognition systems but very few studies deal with behavior tracking for addressing the problem of variability and behavior dynamics. In this regard, we propose the fusion of PRocess mining and Paravector Tensor (PROMPT) based physical health monitoring framework that not only tracks subjective human behavior, but also deals with the intensity variations associated with inertial measurement units. Our experimental analysis on a publicly available dataset shows that the proposed method achieves 14.56% better accuracy in comparison to existing works. We also propose a generalized framework for healthcare applications using wearable sensors and the PROMPT method for its triage with physical health monitoring systems in the real world
A Secure Data Sharing Scheme in Community Segmented Vehicular Social Networks for 6G
peer reviewedThe use of aerial base stations, AI cloud, and satellite storage can help manage location, traffic, and specific application-based services for vehicular social networks. However, sharing of such data makes the vehicular network vulnerable to data and privacy leakage. In this regard, this article proposes an efficient and secure data sharing scheme using community segmentation and a blockchain-based framework for vehicular social networks. The proposed work considers similarity matrices that employ the dynamics of structural similarity, modularity matrix, and data compatibility. These similarity matrices are then passed through stacked autoencoders that are trained to extract encoded embedding. A density-based clustering approach is then employed to find the community segments from the information distances between the encoded embeddings. A blockchain network based on the Hyperledger Fabric platform is also adopted to ensure data sharing security. Extensive experiments have been carried out to evaluate the proposed data-sharing framework in terms of the sum of squared error, sharing degree, time cost, computational complexity, throughput, and CPU utilization for proving its efficacy and applicability. The results show that the CSB framework achieves a higher degree of SD, lower computational complexity, and higher throughput