Secure Outsourced Computation on Encrypted Data

Homomorphic encryption (HE) is a promising cryptographic technique that supports computations on encrypted data without requiring decryption first. This ability allows sensitive data, such as genomic, financial, or location data, to be outsourced for evaluation in a resourceful third-party such as the cloud without compromising data privacy. Basic homomorphic primitives support addition and multiplication on ciphertexts. These primitives can be utilized to represent essential computations, such as logic gates, which subsequently can support more complex functions. We propose the construction of efficient cryptographic protocols as building blocks (e.g., equality, comparison, and counting) that are commonly used in data analytics and machine learning. We explore the use of these building blocks in two privacy-preserving applications. One application leverages our secure prefix matching algorithm, which builds on top of the equality operation, to process geospatial queries on encrypted locations. The other applies our secure comparison protocol to perform conditional branching in private evaluation of decision trees. There are many outsourced computations that require joint evaluation on private data owned by multiple parties. For example, Genome-Wide Association Study (GWAS) is becoming feasible because of the recent advances of genome sequencing technology. Due to the sensitivity of genomic data, this data is encrypted using different keys possessed by different data owners. Computing on ciphertexts encrypted with multiple keys is a non-trivial task. Current solutions often require a joint key setup before any computation such as in threshold HE or incur large ciphertext size (at best, grows linearly in the number of involved keys) such as in multi-key HE. We propose a hybrid approach that combines the advantages of threshold and multi-key HE to support computations on ciphertexts encrypted with different keys while vastly reducing ciphertext size. Moreover, we propose the SparkFHE framework to support large-scale secure data analytics in the Cloud. SparkFHE integrates Apache Spark with Fully HE to support secure distributed data analytics and machine learning and make two novel contributions: (1) enabling Spark to perform efficient computation on large datasets while preserving user privacy, and (2) accelerating intensive homomorphic computation through parallelization of tasks across clusters of computing nodes. To our best knowledge, SparkFHE is the first addressing these two needs simultaneously

A Cognitive Theory-based Approach for the Evaluation and Enhancement of Internet Security Awareness among Children Aged 3-12 Years

In the age of technology, the Internet has spread widely and used for multiple purposes by users of all ages, especially children who start using it frequently to play in their spare time. With the use of the Internet, children must have a sufficient security awareness to avoid security risks found online. This study takes us through the journey of evaluating and enhancing the level of the Internet security awareness among a group of Saudi children aged 3-12 years. The developed evaluation survey shows that there is some awareness among the Saudi Children; however, they still need more concrete ways of ensuring secure practices as they showed a poor knowledge of proper Internet security practices in areas such as interacting with anonymous advertisements as well as understanding some of the Internet Security symbols. The study also presents a suggested Awareness Enhancement solution to raise the security awareness among children. The solution’s design takes into consideration the Piaget’s theory of children’s cognitive development, which states that children in different age groups have different perceptual and learning abilities. The test of the suggested solution shows a significant increase in the sample’s Internet security level. The work of this study emphasizes on the importance of targeting the Saudi children with interactive training sessions to raise their Internet security awareness level

Efficacy of metformin monotherapy in newly diagnosed type 2 diabetes mellitus patients treated at Prince Mohammed Bin Abdulaziz hospital, Riyadh, Saudi Arabia

Background: Metformin monotherapy is the primary therapeutic approach in most cases of newly diagnosed type 2 diabetes mellitus (T2DM) as it is safe, efficient, and is known to lower risks like vascular complications in patients. Previous studies have shown that glycemic control provided by metformin monotherapy is not consistent and needs to be monitored in association with other factors. Due to the complex nature of the disease and other factors like genetic predisposition, ethnicity, and geographic distribution, it is crucial to investigate its effect on the Saudi population. This study aimed to evaluate the glycemic response of metformin monotherapy in individuals with newly diagnosed T2DM who had not previously taken any other medications. Methods: A retrospective study model was followed to determine metformin monotherapy in newly diagnosed type 2 diabetes patients. The efficacy of the metformin monotherapy was evaluated in the patients who were drug naive and had undergone treatment for six months. Results: HbA1c levels for our study population (n=136) before and after metformin monotherapy for a period of six months was collected from patient records. The study cohort included both male (n=71) and female (n=67) patients. There was a significant difference in the HbA1c levels of all diabetes patients before (Mean=9.1, SD=2.84) and after (Mean=7.13, SD=1.51) medication; 2-tailed significance p<0.001. Conclusions: Metformin monotherapy was effective in reducing the HbA1c levels across both genders and all age groups in the present study. These results suggest that metformin monotherapy could be the first line of therapy for newly diagnosed T2DM individuals

Detecting Credit Card Fraud using Machine Learning

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models