A privacy‐preserving mobile location‐based advertising system for small businesses

Ahmed, Ahmed Abdelmoamen

doi:10.1002/eng2.12416

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…The context-aware privacy-preserving algorithm (dubbed CAPP) was developed and compared with currently present algorithms of privacies such as EfficientFake [8] and MaskSensitive, MaskIt (using the hybrid check) [7]. Basic method's MaskSensitive, which hides or suppresses all sensitive circumstances during the release of a non-sensitive one.…”

Section: Resultsmentioning

confidence: 99%

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An Adaptive Approach for Preserving Privacy in Context Aware Applications for Smartphones in Cloud Computing Platform

Gadiyar¹,

S²,

Goudar³

2022

IJACSA

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With the widespread use of mobile phones and smartphone applications, protecting one's privacy has become a major concern. Because active defensive strategies and temporal connections between situations relevant to users are not taken into account, present privacy preservation systems for cell phones are often ineffective. This work defines secrecy maintenance issues similar to optimizing tasks, thereby verifying their accuracy and optimization capabilities through a hypothetical study. Many optimal issues arise while preserving one's privacy and these optimal issues are to be addressed as linear programming issues. By addressing linear programming issues, an effective context-aware privacy-preserving algorithm (CAPP) was created that uses an active defence strategy to determine how to release a user's current context to enhance the quality of service (QoS) regarding context-aware applications while maintaining secrecy. CAPP outperforms other standard methodologies in lengthy simulations of actual data. Additionally, the minimax learning algorithm (MLA) optimizes the policy users and improves the satisfaction threshold of the contextaware applications. Moreover, a cloud-based approach is introduced in the work to protect the user's privacy from third parties. The obtained performance measures are compared with existing approaches in terms of privacy policy breaches, context sensitivity, satisfaction threshold, adversary power, and convergence speed for online and offline attacks.

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Section: Resultsmentioning

confidence: 99%

“…An increased number of actual contexts lead to greater service quality for consumers with such a deception strategy. As a result, it is not suitable for mobile devices [8]. In recent years, many researchers have been fascinated with cloud computing [9,10] due to the efficient outcome for protecting privacy from the third party.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Approach for Preserving Privacy in Context Aware Applications for Smartphones in Cloud Computing Platform

Gadiyar¹,

S²,

Goudar³

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Also, this will transform this system to be generic, making it applicable to different fields requiring real-time processing and using cameras such as in the transportation field. It will be useful for supporting the sensing needs of a wide range of researches [24][25][26][27][28][29][30][31][32][33] and applications [34][35][36][37][38][39][40][41][42]. Finally, experiments with more massive datasets are needed to study the robustness of our system at a large scale, and improve the prediction accuracy of the less performing disease classes.…”

Section: Discussionmentioning

confidence: 99%

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning

Ahmed

Reddy

2021

AgriEngineering

Self Cite

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Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species.

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“…We are also working on using the Actor model of concurrency [19], leveraging multi-threaded computation for massive live traffic streams. It will be useful for supporting the sensing needs of a wide range of researches [20][21][22][23][24][25][26][27][28][29][30] and applications [31][32][33][34][35][36][37][38][39][40]. Finally, experiments with more massive datasets are needed to study the robustness of our system at a large scale, and improve the prediction accuracy of the less performing application classes.…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning

Ahmed

Agunsoye

2021

Algorithms

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The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported over secure application-layer protocols (e.g., HTTPS, SSL, and SSH). This makes it a challenging task for network administrators to identify online applications using traditional port-based approaches. One way for classifying the modern network traffic is to use machine learning (ML) to distinguish between the different traffic attributes such as packet count and size, packet inter-arrival time, packet send–receive ratio, etc. This paper presents the design and implementation of NetScrapper, a flow-based network traffic classifier for online applications. NetScrapper uses three ML models, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Network (ANN), for classifying the most popular 53 online applications, including Amazon, Youtube, Google, Twitter, and many others. We collected a network traffic dataset containing 3,577,296 packet flows with different 87 features for training, validating, and testing the ML models. A web-based user-friendly interface is developed to enable users to either upload a snapshot of their network traffic to NetScrapper or sniff the network traffic directly from the network interface card in real time. Additionally, we created a middleware pipeline for interfacing the three models with the Flask GUI. Finally, we evaluated NetScrapper using various performance metrics such as classification accuracy and prediction time. Most notably, we found that our ANN model achieves an overall classification accuracy of 99.86% in recognizing the online applications in our dataset.

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A privacy‐preserving mobile location‐based advertising system for small businesses

Cited by 12 publications

References 28 publications

An Adaptive Approach for Preserving Privacy in Context Aware Applications for Smartphones in Cloud Computing Platform

An Adaptive Approach for Preserving Privacy in Context Aware Applications for Smartphones in Cloud Computing Platform

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning

A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning

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