scholarly journals A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sudhakar Sengan ◽  
Subramaniyaswamy V ◽  
Rutvij H. Jhaveri ◽  
Vijayakumar Varadarajan ◽  
Roy Setiawan ◽  
...  
Keyword(s):  
Recommendation System ◽  
User Behavior ◽  
Mobile App ◽  
Accelerometer Data ◽  
Constant Input ◽  
User Data ◽  
Standard Solution ◽  
Cryptographic Techniques ◽  
Privacy Issues ◽  
Evaluation Parameters

Advances in Wireless Body Area Networks, where embedded accelerometers, gyroscopes, and other sensors empower users to track real-time health data continuously, have made it easier for users to follow a healthier lifestyle. Various other apps have been intended to choose suitable physical exercise, depending on the current healthcare environment. A Mobile Application (Mobile App) based recommendation system is a technology that allows users to select an apt activity that might suit their preferences. However, most of the current applications require constant input from end-users and struggle to include those who have hectic schedules or are not dedicated and self-motivated. This research introduces a methodology that uses a “Selective Cluster Cube” recommender system to intelligently monitor and classify user behavior by collecting accelerometer data and synchronizing with its calendar. We suggest customized daily workouts based on historical user and related user habits, interests, physical status, and accessibility. Simultaneously, the exposure of customer requirements to the server is also a significant concern. Developing privacy-preserving protocols with basic cryptographic techniques (e.g., protected multi-party computing or HE) is a standard solution to address privacy issues, but in combination with state-of-the-art advising frameworks, it frequently provides far-reaching solutions. This paper proposes a novel framework, a Privacy Protected Recommendation System (PRIPRO), that employs HE for securing private user data. The PRIPRO model is compared for accuracy and robustness using standard evaluation parameters against three datasets.

scholarly journals Development and usability testing of an educational mobile learning app for climate change and health impacts

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Elif Aydoğan ◽  
Ali Derya Atik ◽  
Ergin Şafak Dikmen ◽  
Figen Erkoç
Keyword(s):  
Climate Change ◽  
User Behavior ◽  
Learning Experience ◽  
Mobile App ◽  
Health Impacts ◽  
Health Providers ◽  
Learning Context ◽  
Smart Learning Environment ◽  
Post Test ◽  
Climate Change And Health

Abstract Objective Mobile applications, social media platforms are changing Internet user behavior; creating a new era of education in a connected world. We have previously reported training needs of health providers in the climate change. Aim is to develop and test an Android® Mobile app as an effective smart learning environment for climate change health impacts. Materials and methods The quasi-experimental design method was used in five phases: easy-to-reach, rich content Mobile app design and development for Android® operating system, scale development, finalizing scales to be used, implementation, data collection, analysis. Dependent t-test of pre-test and post-test awareness scores was analyzed. Usability and satisfaction were assessed with two scales; quantitative data with descriptive statistics. Results The developed Mobile app was effective in enhancing students’ learning experience, and well-received in terms of adopting and using such technology for educational purposes. Pre-test and post-test scores different statistically (p<0.05); increasing participants’ awareness level and were satisfied. Conclusion We conclude that our Mobile app, m-learning project, is successfully incorporated into the learning context; when tested, raised awareness about climate change and health effects for the public. To our knowledge, no currently existing tool to provide new mobile application for climate change education and promote awareness exists.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

scholarly journals PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Jiang ◽  
Ruijin Wang ◽  
Zhiyuan Xu ◽  
Yaodong Huang ◽  
Shuo Chang ◽  
...  
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
User Behavior ◽  
Network Services ◽  
Mobile Social Network ◽  
Fine Grained ◽  
Friend Recommendation ◽  
Social Applications ◽  
Browsing Behavior ◽  
Behavioral Information

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

Perbandingan Interpretasi Analisis Data Page View dan Click Related Post Pada Website Edukasi Sebagai Implisit Feedback Dengan Hasil Interpretasi Penggunaan Teknologi EyeTracking

2021 ◽  
Vol 3 (2) ◽  
pp. 66-72
Author(s):  
Riad Taufik Lazwardi ◽  
Khoirul Umam
Keyword(s):  
Decision Process ◽  
Recommendation System ◽  
User Behavior ◽  
Implicit Feedback ◽  
Decision Making Process ◽  
Learning Material ◽  
Relative Preferences ◽  
Insight Into ◽  
Explicit Feedback ◽  
Data Page

The analysis used in this study uses the help of Google Analytics to understand how the user's behavior on the Calculus learning material educational website page. Are users interested in recommendation articles? The answer to this question provides insight into the user's decision process and suggests how far a click is the result of an informed decision. Based on these results, it is hoped that a strategy to generate feedback from clicks should emerge. To evaluate the extent to which feedback shows relevance, versus implicit feedback to explicit feedback collected manually. The study presented in this study differs in at least two ways from previous work assessing the reliability of implicit feedback. First, this study aims to provide detailed insight into the user decision-making process through the use of a recommendation system with an implicit feedback feature. Second, evaluate the relative preferences that come from user behavior (user behavior). This differs from previous studies which primarily assessed absolute feedback. 

scholarly journals Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

2015 ◽  
Vol 67 (1) ◽  
pp. 99-104 ◽  
Author(s):  
Gabroveanu Mihai
Keyword(s):  
Association Rules ◽  
Management System ◽  
Recommendation System ◽  
Learning Management System ◽  
Distributed Learning ◽  
Learning Management Systems ◽  
Management Systems ◽  
User Data ◽  
E Learning ◽  
Learning Management

Abstract Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

scholarly journals Manipulation Attacks in Local Differential Privacy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Albert Cheu ◽  
Adam Smith ◽  
Jonathan Ullman
Keyword(s):  
Systematic Study ◽  
Differential Privacy ◽  
Domain Size ◽  
User Data ◽  
Large Systems ◽  
Cryptographic Techniques ◽  
Fundamental Limitation ◽  
Theoretical Results ◽  
Different Levels ◽  
Privacy Level

Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems. We initiate a systematic study of a fundamental limitation of locally differentially private protocols: they are highly vulnerable to adversarial manipulation. While any algorithm can be manipulated by adversaries who lie about their inputs, we show that any noninteractive locally differentially private protocol can be manipulated to a much greater extent---when the privacy level is high, or the domain size is large, a small fraction of users in the protocol can completely obscure the distribution of the honest users' input. We also construct protocols that are optimally robust to manipulation for a variety of common tasks in local differential privacy. Finally, we give simple experiments validating our  theoretical results, and demonstrating that protocols that are optimal without manipulation can have dramatically different levels of robustness to manipulation. Our results suggest caution when deploying local differential privacy and reinforce the importance of efficient cryptographic  techniques for the distributed emulation of centrally differentially private mechanisms.

scholarly journals Factors Affecting Users' Behavioral Intention, Behavior and Therapy Outcome of a Mobile-Phone-Delivered Cognitive Behavioral Therapy for Insomnia (Preprint)

2018 ◽  
Author(s):  
Siska Fitrianie ◽  
Corine H. G. Horsch ◽  
Jaap Lancee ◽  
Robbert Jan Beun ◽  
Fiemke Griffioen-Both ◽  
...  
Keyword(s):  
Behavioral Intention ◽  
User Behavior ◽  
Behavioral Therapy ◽  
Mobile App ◽  
Sleep Pattern ◽  
Sleep Restriction ◽  
Therapy Outcomes ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Insomnia Severity

BACKGROUND A mobile app could be a powerful medium for providing individual support for cognitive behavioral therapy (CBT), as well as to facilitate the therapy adherence. Many studies have reported about the efficacy of such apps for insomnia treatment. However, little is known about factors that may explain the acceptance and uptake of such applications. OBJECTIVE This study, therefore examines factors that may explain variation between people's behavioral intention to use a CBT for insomnia (CBT-I) app and their use behavior, and the influence of the behavior on therapy outcomes. METHODS From literature, related factors were identified. Data were gathered from a field trial involving people with relatively mild insomnia using a CBT-I app. Applying the Partial Least Squares-Structural Equation Modeling method, the study examines a three-tier model. The first tier explored seven aspects of behavioral intention: performance expectancy, effort expectancy, social influence, self-efficacy, trust, affect, and anxiety. The second tier investigated the influence of behavioral intention and facilitating condition on user behavior, which was specifically defined as engaging with the app (i.e. sleep diaries and conversations with the app), doing relaxation exercises, and following sleep restriction exercises. Here, the relationship between app engagement and the other two exercise behaviors was also examined. Finally, the third tier tested the influence of the behavior on therapy outcomes: insight into own sleep pattern, general sleep knowledge, and insomnia severity. The latter was measured using Insomnia Severity Index. RESULTS Performance expectancy, effort expectancy, social influence, self-efficacy, and trust all explained part of the variation in behavioral intention, but not beyond the explanation provided by affect, which accounted for R2 = .59 (n = 89). Behavioral intention could explain two behavior factors, i.e. app engagement (R2 = .30, n = 89) and relaxation exercise (R2 = .42, n = 89). Engagement with the app was a determinant for the other behaviors, i.e. relaxation exercise (R2 = .42, n = 89) and sleep restriction (R2 = .54, n = 47). Furthermore, app engagement was the only determinant for one of the therapy outcomes, i.e. understanding own sleep pattern (R2 = .09, n = 72). We did not find an association between user behavior and insomnia severity. CONCLUSIONS We anticipate that the findings will help researchers and developers to focus on: (1) users' positive feelings about the app, as this was an indicator of their acceptance of the mobile app; and on (2) therapeutic activities delivered via the app, as this correlated with their sleep pattern awareness and their involvement in the exercises recommended by the CBT-I app. CLINICALTRIAL Netherlands Trial Register: NTR5560; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5560 (archived by WebCite at http://www.webcitation.org/6noLaUdJ4)

Amelioration of Anonymity Modus Operandi for Privacy Preserving Data Publishing

2014 ◽  
pp. 96-107 ◽  
Author(s):  
J. Indumathi
Keyword(s):  
Performance Measures ◽  
Privacy Preserving ◽  
Research Community ◽  
Data Publishing ◽  
Modus Operandi ◽  
Privacy Preserving Data Mining ◽  
Research Problems ◽  
Privacy Preserving Data Publishing ◽  
Cryptographic Techniques ◽  
Privacy Issues

The scientific tumultuous intonation has swept our feet's, of its balance and at the same time wheedled us to reach the take-off arena from where we can march equipped and outfitted into the subsequent century with confidence & self-assurance; by unearthing solutions for all information security related issues (with special emphasis on privacy issues). Examining various outstanding research problems that encompass to be embarked upon for effectively managing and controlling the balance between privacy and utility, the research community is pressurized to propose suitable elucidations. The solution is to engender several Privacy-Preserving Data Publishing (PPDP) techniques like Perturbation, swapping, randomization, cryptographic techniques etc., Amongst the various available techniques k-anonymity is unique in facet of its association with protection techniques that preserve the truthfulness of the data. The principal chip in of this sketch out comprises: 1) Motivation for this exploration for Amelioration Of Anonymity Modus Operandi For Privacy Preserving Data Mining; 2) investigation of well-known research approaches to PPDM; 3) argue solutions to tackle the problems of security threats and attacks in the PPDM in systems; 4) related survey of the various anonymity techniques; 5) exploration of metrics for the diverse anonymity techniques; 6) performance measures for the various anonymity techniques; and 7) contradistinguish the diverse anonymity techniques and algorithms.

Historic Perspective of Log Analysis

2011 ◽  
pp. 18-38 ◽  
Author(s):  
W. David Penniman
Keyword(s):  
World Wide ◽  
User Behavior ◽  
Deep Understanding ◽  
Historical Review ◽  
Early Years ◽  
Log Analysis ◽  
Qualitative Approaches ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Privacy Issues

This historical review of the birth and evolution of transaction log analysis applied to information retrieval systems provides two perspectives. First, a detailed discussion of the early work in this area, and second, how this work has migrated into the evaluation of World Wide Web usage. The author describes the techniques and studies in the early years and makes suggestions for how that knowledge can be applied to current and future studies. A discussion of privacy issues with a framework for addressing the same is presented as well as an overview of the historical “eras” of transaction log analysis. The author concludes with the suggestion that a combination of transaction log analysis of the type used early in its application along with additional more qualitative approaches will be essential for a deep understanding of user behavior (and needs) with respect to current and future retrieval systems and their design.

Data Security and Privacy-Preserving in Edge Computing

2021 ◽  
pp. 188-202
Author(s):  
Manoranjini J. ◽  
Anbuchelian S.
Keyword(s):  
Trust Management ◽  
Edge Computing ◽  
Security And Privacy ◽  
Promising Technology ◽  
Research Areas ◽  
Massive Growth ◽  
Security Models ◽  
Current Scenario ◽  
Cryptographic Techniques ◽  
Privacy Issues

The rapid massive growth of IoT and the explosive increase in the data used and created in the edge networks led to several complications in the cloud technology. Edge computing is an emerging technology which is ensuring itself as a promising technology. The authors mainly focus on the security and privacy issues and their solutions. There are a lot of important features which make edge computing the most promising technology. In this chapter, they emphasize the security and privacy issues. They also discuss various architectures that enable us to ensure safe technologies and also provide an analysis on various designs that enable strong security models. Next, they make a detailed study on different cryptographic techniques and trust management systems. This study helps us to identify the pros and cons that led us to promising implementations of edge computing in the current scenario. At the end of the chapter, the authors discuss on various open research areas which could be the thrust areas for the next era.

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