AES-Route Server Model for Location based Services in Road Networks

Alrahhal, Mohamad Shady; Ashraf, Muhammad Usman; Abesen, Adnan; Arif, Sabah

doi:10.14569/ijacsa.2017.080847

Cited by 6 publications

(3 citation statements)

References 18 publications

(25 reference statements)

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“…The advantages of profundity are seen for all matrices in Table 3 and 4. And at the end of the day we've recommended a new ResNet 200 deeper layered model [72][73][74][75]. The other suggestion we've imposed to improve the performance and the accuracy of ResNet is by tuning the batch size and learning rate.…”

Section: Discussion and Recommendationmentioning

confidence: 99%

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

Aljahdali¹

2023

IJETAE

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As we know that how rapidly is corona virus spreading, starting from China, to all over the world. There are more than 5 million suspected cases and almost 0.33 million deaths according to the statistics of world meterinfo, and there are limited test kits available in hospitals because of cases are increasing rapidly on the daily bases. So, it is compulsory to build an authentic automatic detection system which gives maximum high performance to check whether the patient is Covid-19 suspect or not as an alternative to slow down the spread of coronavirus among people.In this research, we have used deep learning’s (DL) Convolutional neural network (CNN) and ResNet models. With a critical analysis, we conclude that every ResNet layered model has the high performance with error rate less than 3% on approximately all kinds of datasets of chest Xray images whether it includes rib shadow & clivade or after segmentation. We have proposed a new solution for existing model and to enhance the ResNet model by applying layered architecture style by adding more layers to our ResNet which will help to minimize the error rate. Further, to boost the performance of ResNet by tune up the batch size and learning rate, we achieve the learning rate 0.00001 that has higher accuracy as compared to the other learning rates 0.1, 0.01, 0.001 and 0.0001. The proposed study is promising framework for the covid detection that assist us to deal the COVID decease

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Section: Discussion and Recommendationmentioning

confidence: 99%

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

Aljahdali¹

2023

IJETAE

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“…When system's this silent mode is terminated location is upgraded to novel one but downside of this approach is, if vehicle's speed is lower than desired then silent period will go long and location will not be upgraded and will keep former which is vulnerable to attack. In the swing and swap method [40], a node can interchange its Identities with imminent and corresponding vehicle to obfuscate the attacker about vehicles but where user is on the less-crowded road or on motorway then this approach is not efficient. In another coordinate transformation technique [41], [42], the user executes some geometric options such as shifting and rotating their location before getting a service from the LBS.…”

Section: B Related Workmentioning

confidence: 99%

HBLP: A Privacy Protection Framework for TIP Attributes in NTTP-Based LBS Systems

2020

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Nowadays, location-based services are being widely popularized due to their massive usage in current and emerging technologies. These services are based on searching out areas of interest which are likely to be accessed by users. Despite helping users worldwide, Location Based Services (LBSs) Systems endanger users' privacy because a user must provide personal information in order to use the services. Users thus become easy prey for assailants to access their social and personal lives. This problem is a giant issue for contemporary technologies because they are increasingly being used with the passage of time. Many existing solutions have attempted to resolve the challenges, but they face some serious dilemmas regarding the preservation of privacy. In order to address the privacy challenges in LBS systems, in this paper we have introduced a new Hierarchy Based Location Privacy (HBLP) model that protects the user's privacy, including the user's query time and identity and location information. The proposed model protects the user's privacy by using pseudo identity exchange, an aggregation protocol, and the concepts of Forest User (FU), Tree User (TU), and Child Users (CU) with k-anonymity and t-closeness, which is a reasonable combination for privacy provision for a user's query time, identity, and location. In order to evaluate the privacy protection level, we implemented the HBLP model in a Riverbed (Opnet) simulation and compared the results with existing state-of-the-art privacy-provisioning methods. The results showed that HBLP protected all the privacy attributes when a user interacts with an LBS system. INDEX TERMS Location based services, user privacy, NTTP, point of interest.

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“…Moreover, these approaches are reliant on the third trusted party (TTP), which can’t be taken for granted [24]. The SP or TTP can indeed be the internal attackers or partner to a host of external attackers [25]. Furthermore, the users are forced to compromise on the accuracy of their retrieved results [26].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Method for Preserving Privacy in Internet of Things

Yamin

Alsaawy

Alkhodre

et al. 2019

Sensors

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Preservation of privacy of users’ personal data has always been a critical issue to deal with. This issue in the Internet of Things (IoT), which facilitates millions of applications, has become even more challenging. Currently, several approaches and methods are available to safeguard privacy but each of them suffers from one or more anomalies. In particular, Trusted Third-Party approach relies on the trust of a third-party server, Cooperation needs the trust of other peers, Obfuscation is known to return inaccurate results, and Dummy generates too much overhead. Moreover, these and most of the other well-known approaches deal only with specific types of applications linked to the location-based services. In this paper, we present two new methods, namely: Blind Third Party (BTP) and Blind Peers ( B L P ), and combine them to form a new one to be known as the Blind Approach ( B L A ). With the help of simulation results we shall demonstrate the effectiveness and superiority of B L A over the other available methods. The simulation results also exhibit that B L A is free from all the existing problems of the other approaches. However, B L A causes a slight increase in the average (response) time, which we consider to be a minor issue. We shall also discuss the capability and superiority of the Blind Approach in the cases of E-health, Smart Transportation, and Smart Home systems.

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AES-Route Server Model for Location based Services in Road Networks

Cited by 6 publications

References 18 publications

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

HBLP: A Privacy Protection Framework for TIP Attributes in NTTP-Based LBS Systems

An Innovative Method for Preserving Privacy in Internet of Things

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