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Collaborative Filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. E-commerce sites use CF systems to suggest products to customers based on like-minded customers' preferences. People use CF systems to cope with information overload. To conduct collaborative filtering, data from customers are needed. However, collecting high quality data from customers is not an easy task because many customers are so concerned about their privacy that they might decide to give false information. CF systems using these data might produce inaccurate recommendations.We propose a randomized perturbation technique to protect users' privacy while still producing accurate recommendations. Although the randomized perturbation techniques add randomness to the original data to prevent the data collector from learning the private user data, our scheme can still provide recommendations with decent accuracy. We conducted several experiments to compare the recommendations on the randomized data with those on the original data. Using these experiment results, we analyzed how different parameters affect the accuracy. Our results show that the CF systems using the randomized perturbation techniques provide accurate recommendations while preserving the users' privacy.

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As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

The aim of this study was to evaluate WhatsApp messenger usage for communication between consulting and emergency physicians. A retrospective, observational study was conducted in the emergency department (ED) of a tertiary care university hospital between January 2014 and June 2014. A total of 614 consultations requested by using the WhatsApp application were evaluated, and 519 eligible consultations were included in the study. The WhatsApp messages that were transferred to consultant physicians consisted of 510 (98.3%) photographic images, 517 (99.6%) text messages, 59 (11.3%) videos, and 10 (1.9%) voice messages. Consultation was most frequently requested from the orthopedics clinic (n = 160, 30.8%). The majority of requested consultations were terminated only by evaluation via WhatsApp messages. (n = 311, 59.9%). Most of the consulting physicians were outside of the hospital or were mobile at the time of the consultation (n = 292, 56.3%). The outside consultation request rate was significantly higher for night shifts than for day shifts (p = .004), and the majority of outside consultation request were concluded by only WhatsApp application (p < .001). WhatsApp is useful a communication tool between physicians, especially for ED consultants who are outside the hospital, because of the ability to transfer large amounts of clinical and radiological data during a short period of time.

Software Defined Networking (SDN) offers several advantages such as manageability, scaling, and improved performance. However, SDN involves specific security problems, especially if its controller is defenseless against Distributed Denial of Service (DDoS) attacks. The process and communication capacity of the controller is overloaded when DDoS attacks occur against the SDN controller. Consequently, as a result of the unnecessary flow produced by the controller for the attack packets, the capacity of the switch flow table becomes full, leading the network performance to decline to a critical threshold. In this study, DDoS attacks in SDN were detected using machine learning-based models. First, specific features were obtained from SDN for the dataset in normal conditions and under DDoS attack traffic. Then, a new dataset was created using feature selection methods on the existing dataset. Feature selection methods were preferred to simplify the models, facilitate their interpretation, and provide a shorter training time. Both datasets, created with and without feature selection methods, were trained and tested with Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN) classification models. The test results showed that the use of the wrapper feature selection with a KNN classifier achieved the highest accuracy rate (98.3%) in DDoS attack detection. The results suggest that machine learning and feature selection algorithms can achieve better results in the detection of DDoS attacks in SDN with promising reductions in processing loads and times.Sustainability 2020, 12, 1035 2 of 16As the control logic has been taken from local devices and become central, SDN is structured from a single spot and dynamically optimized [4]. Although certain security threats are general in computer networks, SDN has brought its own security threats. There are at least seven different threat vectors identified with SDN [5]. The most important threat vector for SDN is Distributed Denial of Service (DDoS) attacks. They can be against the controller in SDN or in the storage capacity of the flow table in the OpenFlow switch.The controller is exposed to DDoS attacks through the communication line between the controller and the data plane. DDoS attacks direct a large amount of traffic to the OpenFlow switch on the data plane. If packets arriving at the OpenFlow switch do not match with the flow input in the flow table (miss flow), packets are taken into the flow buffer. Then, they are transmitted to the controller with the Packet-In message to write a new rule. In this situation, the sources of the controller (memory, processor, bandwidth, etc.) remain incapable and the network becomes inoperative. In addition, the bandwidth of the communication line between the controller that is exposed to attack traffic and the OpenFlow switch is negatively affected. Therefore, network performance severely declines [6].The data plane is exposed to DDoS attacks through the flow table located in the net...

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With increasing need for preserving confidential data while providing recommendations, privacy-preserving collaborative filtering has been receiving increasing attention. To make data owners feel more comfortable while providing predictions, various schemes have been proposed to estimate recommendations without deeply jeopardizing privacy. Such methods eliminate or reduce data owners' privacy, financial, and legal concerns by employing different privacy-preserving techniques. Although there are considerable numbers of studies focusing on privacy-preserving collaborative filtering schemes, there is no comprehensive survey investigating them with respect to different directions. In this survey, we mainly focus on studying various privacy-preserving recommendation methods according to the data partitioning cases and the utilized techniques for preserving confidentiality. We also review privacy in general and examine in collaborative filtering scenarios. We discuss the proposed schemes in terms of their limitations and practical implementation challenges. Moreover, we give an overview of evaluation of such schemes. We finally provide a comprehensive guideline for studying in this area and propose future research directions.