72 research outputs found
Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learning
Split learning (SL) aims to protect user data privacy by distributing deep
models between client-server and keeping private data locally. Only processed
or `smashed' data can be transmitted from the clients to the server during the
SL process. However, recently proposed model inversion attacks can recover the
original data from the smashed data. In order to enhance privacy protection
against such attacks, a strategy is to adopt differential privacy (DP), which
involves safeguarding the smashed data at the expense of some accuracy loss.
This paper presents the first investigation into the impact on accuracy when
training multiple clients in SL with various privacy requirements.
Subsequently, we propose an approach that reviews the DP noise distributions of
other clients during client training to address the identified accuracy
degradation. We also examine the application of DP to the local model of SL to
gain insights into the trade-off between accuracy and privacy. Specifically,
findings reveal that introducing noise in the later local layers offers the
most favorable balance between accuracy and privacy. Drawing from our insights
in the shallower layers, we propose an approach to reduce the size of smashed
data to minimize data leakage while maintaining higher accuracy, optimizing the
accuracy-privacy trade-off. Additionally, a smaller size of smashed data
reduces communication overhead on the client side, mitigating one of the
notable drawbacks of SL. Experiments with popular datasets demonstrate that our
proposed approaches provide an optimal trade-off for incorporating DP into SL,
ultimately enhancing training accuracy for multi-client SL with varying privacy
requirements
AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data
Educational systems have advanced with the use of electronic learning (e-learning), which is a promising solution for long-distance learners. Students who engage in e-learning can access tests and exams online, making education more flexible and accessible. This work reports on the design of an e-learning system that makes recommendations to students to improve their learning. This artificial intelligence-based student assessment and recommendation (AISAR) system consists of score estimation, clustering, performance prediction, and recommendation. In addition, the importance of student authentication is recognised in situations in which students must authenticate themselves prior to using the e-learning system using their identity, password, and personal identification number. Individual scores are determined using a recurrent neural network (RNN) based on student engagement and examination scores. Then, a density-based spatial clustering algorithm (DBSCAN) using Mahalanobis distance clustering is implemented to group students based on their obtained score values. The constructed clusters are validated by estimating purity and entropy. Student performance is predicted using a threshold-based MapReduce (TMR) procedure from the score-based cluster. When predicting student performance, students are classified into two groups: average and poor, with the former being divided into below- and above-average students and the latter into poor and very poor students. This categorisation aims to provide useful recommendations for learning. A recommendation reinforcement learning algorithm, the rule-based state–action–reward–state–action (R-SARSA) algorithm, is incorporated for evaluation. Students were required to work on their subjects according to the provided recommendations. This e-learning recommendation system achieves better performance in terms of true-positives, false-positives, true-negatives, false-negatives, precision, recall, and accuracy
AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data
Educational systems have advanced with the use of electronic learning (e-learning), which is a promising solution for long-distance learners. Students who engage in e-learning can access tests and exams online, making education more flexible and accessible. This work reports on the design of an e-learning system that makes recommendations to students to improve their learning. This artificial intelligence-based student assessment and recommendation (AISAR) system consists of score estimation, clustering, performance prediction, and recommendation. In addition, the importance of student authentication is recognised in situations in which students must authenticate themselves prior to using the e-learning system using their identity, password, and personal identification number. Individual scores are determined using a recurrent neural network (RNN) based on student engagement and examination scores. Then, a density-based spatial clustering algorithm (DBSCAN) using Mahalanobis distance clustering is implemented to group students based on their obtained score values. The constructed clusters are validated by estimating purity and entropy. Student performance is predicted using a threshold-based MapReduce (TMR) procedure from the score-based cluster. When predicting student performance, students are classified into two groups: average and poor, with the former being divided into below- and above-average students and the latter into poor and very poor students. This categorisation aims to provide useful recommendations for learning. A recommendation reinforcement learning algorithm, the rule-based state–action–reward–state–action (R-SARSA) algorithm, is incorporated for evaluation. Students were required to work on their subjects according to the provided recommendations. This e-learning recommendation system achieves better performance in terms of true-positives, false-positives, true-negatives, false-negatives, precision, recall, and accuracy
Green Networking for Major Components of Information Communication Technology Systems
<p>Abstract</p> <p>Green Networking can be the way to help reduce carbon emissions by the Information and Communications Technology (ICT) Industry. This paper presents some of the major components of Green Networking and discusses how the carbon footprint of these components can be reduced.</p
Green Networking for Major Components of Information Communication Technology Systems
Green Networking can be the way to help reduce carbon emissions by the Information and Communications Technology (ICT) Industry. This paper presents some of the major components of Green Networking and discusses how the carbon footprint of these components can be reduced
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