Use Of Data Collection Research Articles

Overt and covert customer data collection in online personalized advertising: The role of user emotions

Due to its immense popularity amongst marketing practitioners, online personalized advertising is increasingly becoming the subject of academic research. Although advertisers need to collect a large amount of customer information to develop customized online adverts, the effect of how this information is collected on advert effectiveness has been surprisingly understudied. Equally overlooked is the interplay between consumer’s emotions and the process of consumer data collection. Two studies were conducted with the aim of closing these important gaps in the literature. Our findings revealed that overt user data collection techniques produced more favourable cognitive, attitudinal and behavioral responses than covert techniques. Moreover, consistent with the self-validation hypothesis, our data revealed that the effects of these data collection techniques can be enhanced (e.g., via happiness and pride), attenuated (e.g., via sadness), or even eliminated (e.g., via guilt), depending on the emotion experienced by the consumer while viewing an advert.

Privacy Personas for IoT-Based Health Research: A Privacy Calculus Approach.

The reliance on data donation from citizens as a driver for research, known as citizen science, has accelerated during the Sars-Cov-2 pandemic. An important enabler of this is Internet of Things (IoT) devices, such as mobile phones and wearable devices, that allow continuous data collection and convenient sharing. However, potentially sensitive health data raises privacy and security concerns for citizens, which research institutions and industries must consider. In e-commerce or social network studies of citizen science, a privacy calculus related to user perceptions is commonly developed, capturing the information disclosure intent of the participants. In this study, we develop a privacy calculus model adapted for IoT-based health research using citizen science for user engagement and data collection. Based on an online survey with 85 participants, we make use of the privacy calculus to analyse the respondents' perceptions. The emerging privacy personas are clustered and compared with previous research, resulting in three distinct personas which can be used by designers and technologists who are responsible for developing suitable forms of data collection. These are the 1) Citizen Science Optimist, the 2) Selective Data Donor, and the 3) Health Data Controller. Together with our privacy calculus for citizen science based digital health research, the three privacy personas are the main contributions of this study.

Stroke Prediction Analysis using Machine Learning Classifiers and Feature Technique

Stroke is one of the fatal brain diseases that cause death in 3 to 10 hours. However, most stroke mortality can be prevented by identifying the nature of the stroke and reacting to it promptly through smart health systems. In this paper, a machine learning model is approached for predicting the existence of stroke of a patient where the Random forest classifier outperforms the state-of-the-art models, including Logistic Regression, Decision Tree Classifier (DTC), K-NN. We conduct the experiments on datasets which has 5110 observations with 12 attributes. We also applied EDA for preprocessing and feature techniques for balancing the datasets. Finally, a cloud-based mobile app collects user data to analyze and provide the possibility of stroke for alerting the person with the accuracy of precision 96%, recall 96%, and F1-score 96%. This user-friendly system can be a lifesaver as the person gets an essential warning very easily by providing very little information from anywhere with a mobile device.

A Review of the Quality and Impact of Mobile Health Apps.

Mobile health applications (apps) have transformed the possibilities for health promotion and disease self-management; however, their promise is not fully realized owing to their reliance on commercial ecosystems for development and distribution. This review provides an overview of the types of mobile health apps and describes key stakeholders in terms of how apps are used, developed, and regulated. I outline key challenges facing consumers, public health professionals, and policy makers in evaluating the quality of health apps and summarize what is known about the impact of apps on health outcomes and health equity. I suggest that factors within the wider mobile ecosystem largely define the impact of health apps and, most notably, practices around the collection and commercialization of user data. Finally, I suggest that upstream public health strategies, grounded in an understanding of corporate influences on health, are necessary to promote healthy digital environments in which mobile health app innovation can flourish.

Doing Google Maps

Peter Gentzel, Jeffrey Wimmer and Ruben Schlagowski focus on the app Google Maps. In structural terms, Google Maps is committed to the production logic of platform or surveillance capitalism, insofar as the collected user data are utilised both to maintain Google Maps as a »cartographic infrastructure« (Plantin 2018) and to predict and manipulate behaviour (Zubo! 2019). On the other hand, Google Maps presents an »image of the world« that, as a product of platform capitalism, also conveys specific notions that we depict by using the concepts of »networked images« or »operational images« (Rubinstein & Sluis 2008; Farocki 2004). First, we traced the development of Google Maps and classified it using cartographic principles and criteria. Building on that, we performed two empirical studies. In a first step, we highlight findings on the everyday usage practices of Google Maps. In a second step, we characterise city maps produced by residents of a medium-sized city in Germany using an app developed by us. The project thus sheds light on the appropriation aspect of Google Maps and, by exploring the microlevel of individual usage practices, knowledge, and skills, provides an empirical contribution that is comparatively rare in the context of platform studies. Developing a map application furthermore enables us to show that the selection of knowledge and its spatial anchoring - the »image of the world«- follows a di!erent logic when certain individuals create a map for specific locations (e.g., multimodal routes to »hidden culture«).

Geographic Information System of Contraceptives Users in Samarinda Seberang Sub-District

Birth Control “Keluarga Berencana” (KB) is a government program designed to balance the needs and population. Birth Control is an attempt to measure the number of children and the desired birth spacing of children. Therefore, the Government launched a program or method to prevent and delay pregnancy. Therefore, the purpose of this study is to create a geographic information system for data collection of contraceptive users based on a website that is useful as a place to store data on contraceptive users and information maps as a benchmark for the achievement of Birth Control instructors in the area of ​​Samarinda opposite. The results of this study that the existence of this geographic information system website, besides being able to assist the public in obtaining information about contraceptives, can also assist the work of Birth Control instructors in the regions, especially in the Samarinda Seberang area in recording Birth Control users in their working areas and as a step to help reduce paper use and modernization from the old system.

PERAN GAWAI MOBILE DALAM MENDUKUNG LAYANAN INFORMASI PERPUSTAKAAN DIGITAL YANG TERPERSONALISASI

Personalized information service is an information service delivered by digital libraries so that users can only get information according to their needs online. This article is a conceptual study that examines the role of mobile devices as technology to support personalized information services from the aspects of user data collection and service delivery. Data obtained through literature review of scientific articles that have relevance to the main concept. The results showed that mobile devices have a role in sending data and accessing library services efficiently. As a two-way communication medium, mobile devices can send various forms of feedback to the library which eventually become data for further analysis. As for service access, mobile devices can support mobile applications so that there are several traditional services that can be accessed online anywhere and anytime. Not only data in the form of text, technological developments have been able to analyze data in other types, one of which is in the form of voice. The development of technology in the 4.0 industrial revolution has also become an aspect that encourages efficiency because it can bring about automation in the aspects of data collection and access to library services.

Applicability of Assessment Indices for Hanok-User Sensory Comfort Based on Visual and Tactile Comfort Evaluation Indicators

This study aimed to present an evaluation system to assess comfort that reflects the unique value of vernacular architecture. For this purpose, the background, purpose, evaluation items, and evaluation methods of Korean Standard-Basic Part: 6300-1, 2 (KS A 6300) were analyzed. As a result, the weakness was found in the KS system that there was no method to evaluate the user’s human sensory comfort. To overcome this problem, the contents of KS A 6300-1, 2 were analyzed, and evaluation indices were categorized to assess the comfort of human sensory experience. A comfort assessment index was then derived to evaluate the visual and tactile comfort of the Korean traditional house by analyzing previous research and systems. To assess the possibility of using the Hanok evaluation system for the derived comfort indices, the actual building environmental and user data were collected for one year. Based on the previous step, Predicted Mean Vote (PMV) and Daylight Glare Probability (DGP) were calculated, and at the same time, a comparative analysis was conducted with the collected user data. As a result, DGP showed a tendency of changing user comfort and an error of 7 to 8% regardless of region, while PMV showed a tendency similar to the change in user discomfort response rate. Accordingly, it was concluded that each evaluation index could be used for the comfort evaluation of Hanok.

PRIVACY IN ADVANCED CRYPTOGRAPHIC PROTOCOLS: PROTOTYPICAL EXAMPLES

Cryptography is the fundamental cornerstone of cybersecurity employed for achieving data confidentiality, integrity, and authenticity. However, when cryptographic protocols are deployed for emerging applications such as cloud services or big data, the demand for security grows beyond these basic requirements. Data nowadays are being extensively stored in the cloud, users also need to trust the cloud servers/authorities that run powerful applications. Collecting user data, combined with powerful machine learning tools, can come with a huge risk of mass surveillance or undesirable data-driven strategies for making profits rather than for serving the user. Privacy, therefore, becomes more and more important, and new techniques should be developed to protect personal information and to reduce trust requirements on the authorities or the Big Tech providers. In a general sense, privacy is ``the right to be left alone'' and privacy protection allows individuals to have control over how their personal information is collected and used. In this survey, we discuss the privacy protection methods of various cryptographic protocols, in particular we review: - Privacy in electronic voting systems. This may be, perhaps, the most important real-world application where privacy plays a fundamental role. %classical authentication with group, ring signatures, anonymous credentials. - Private computation. This may be the widest domain in the new era of modern technologies with cloud computing and big data, where users delegate the storage of their data and the computation to the cloud. In such a situation, ``how can we preserve privacy?'' is one of the most important questions in cryptography nowadays. - Privacy in contact tracing. This is a typical example of a concrete study on a contemporary scenario where one should deal with the unexpected social problem but needs not pay the cost of weakening the privacy of users. Finally, we will discuss some notions which aim at reinforcing privacy by masking the type of protocol that we execute, we call it the covert cryptographic primitives and protocols.

Design and implementation of data analysis platform of infectious disease syndromes and pathogen surveillance based on cloud computing technology

Objective During the 11th national five-year plan period, the information management system for infectious disease surveillance platform was established to provide information support for infectious disease surveillance. However, the system can not meet the new needs of information management after the operation for years, facing a series of challenges, such as the high sensitivity of data collection and complexity of users. Methods This paper expounds the design and implementation of infectious disease syndromes and pathogen surveillance information analysis platform from the aspects of business requirements, structure design, system deployment, etc. and evaluate the application of the platform. Results China CDC has established the cloud platform of infectious disease syndromes and pathogen surveillance information for the integration of existing system, newly established infectious disease syndromes and pathogen surveillance information analysis platform and pathogen genomics information platform to provide visualized statistical analysis results, which can improve the application value of the data. Conclusion The platform can control the access to resources effectively, ensure the authenticity and credibility of user's identity and improve the integration of resources to support scientific decision making.

An Ensemble Approach to Control Network Traffic on IoT

Cyber security is most widely seen in many domains. From various domains these attacks plays the major role in damaging the servers and making websites unavailable. Many challenges are facing with the various cyber attacks.Internet of Things (IoT) is most widely used to define as a pervasive network of a (broad) range of connected smart nodes that offer diverse digital services, including the collection of environmental and user data. Detection of cyber attacks is difficult task and this may cause the loss of data packets and may interrupt the server. In this paper, the Enhancement is developed to handle the attack packets effectively. This is the window based application i,e based on the window size the data is processed. The Enhancement is called as an Amplified and Forward for Bi- Directional traffic from Attacks. This is very significant model to detect the several types of attacks that occur in IoT. Results show the performance of proposed system.

Privacy-Preserving federated learning in medical diagnosis with homomorphic re-Encryption

Unlike traditional centralized machine learning, distributed machine learning provides more efficient and useful application scenarios. However, distributed learning may not meet some security requirements. For example, in medical treatment and diagnosis, an increasing number of people are using IoT devices to record their personal data, when training medical data, the users are not willing to reveal their private data to the training party. How to collect and train the data securely has become the main problem to be resolved. Federated learning can combine a large amount of scattered data for training, and protect user data. Compared with general distributed learning, federated learning is more suitable for training on scattered data. In this paper, we propose a privacy-preserving federated learning scheme that is based on the cryptographic primitive of homomorphic re-encryption, which can protect user data through homomorphic re-encryption and trains user data through batch gradient descent (BGD). In our scheme, we use the IoT device to encrypt and upload user data, the fog node to collect user data, and the server to complete data aggregation and re-encrypting. Besides, the security analysis and experimental results show that our scheme can complete model training while preserving user data and local models.

Delivering Resources for Augmented Reality by UAVs: a Reinforcement Learning Approach

Unmanned aerial vehicles (UAVs) are supposed to be used to provide different services from video surveillance to communication facilities during critical and high-demanding scenarios. Augmented reality streaming services are especially demanding in terms of required throughput, computing resources at the user device, as well as user data collection for advanced applications, for example, location-based or interactive ones. This work is focused on the experimental utilization of a framework adopting reinforcement learning (RL) approaches to define the paths crossed by UAVs in delivering resources for augmented reality services. We develop an OpenAI Gym-based simulator that is tuned and tested to study the behavior of UAVs trained with RL to fly around a given area and serve augmented reality users. We provide abstractions for the environment, the UAVs, the users, and their requests. A reward function is then defined to encompass several quality-of-experience parameters. We train our agents and observe how they behave as a function of the number of UAVs and users at different hours of the day.

A Survey on Statistical, Information, and Estimation—Theoretic Views on Privacy

Privacy has become an emerging challenge in both information theory and computer science due to massive (centralized) collection of user data. In this article, we overview privacy-preserving mechanisms and metrics from the lenses of information theory, and unify different privacy metrics, including f-divergences, Rényi divergences, and differential privacy (DP), in terms of the probability likelihood ratio (and its logarithm). We review recent progress on the design of privacy-preserving mechanisms according to the privacy metrics in computer science, where DP is the standard privacy notion which controls the output shift given small input perturbation, and information theory, where the privacy is guaranteed by minimizing information leakage. In particular, for DP, we include its important variants (e.g., Rényi DP, Pufferfish privacy) and properties, discuss its connections with information-theoretic quantities, and provide the operational interpretations of its additive noise mechanisms. For information-theoretic privacy, we cover notable frameworks, including the privacy funnel, originated from rate-distortion theory and information bottleneck, to privacy guarantee against statistical inference/guessing, and information obfuscation on samples and features. Finally, we discuss the implementations of these privacy-preserving mechanisms in current data-driven machine learning scenarios, including deep learning, information obfuscation, federated learning, and dataset sharing.

Preserving User Privacy in Personalized Networks

The advances in efficient computation and data processing technologies have ushered in an entirely new era in which network connectivity applications are being revolutionized by Artificial Intelligence (AI). One of the emerging AI applications in wireless networks is network personalization. Personalized wireless networks are designed to track user satisfaction levels using Machine Learning (ML) models that are trained and updated using context data collected from users. Nonetheless, user data collection and transmission make sensitive user information prone to various privacy attacks. Therefore, in this letter, we propose a privacy-preserving framework for personalized wireless networks. The proposed framework is enabled by Differential Privacy (DP), which is an influential privacy technology that offers a rigorous and provable privacy guarantee. Besides, the proposed framework is supported by a multi-stage prediction process, which adds a second layer of privacy protection to personal user data. Lastly, we investigate the impact of privacy budget and data size on the quality of user satisfaction predictors in personalized wireless networks.

Democratic learning: hardware/software co-design for lightweight blockchain-secured on-device machine learning

Recently, the trending 5G technology encourages extensive applications of on-device machine learning, which collects user data for model training. This requires cost-effective techniques to preserve the privacy and the security of model training within the resource-constrained environment. Traditional learning methods rely on the trust among the system for privacy and security. However, with the increase of the learning scale, maintaining every edge device’s trustworthiness could be expensive. To cost-effectively establish trust in a trustless environment, this paper proposes democratic learning (DemL), which makes the first step to explore hardware/software co-design for blockchain-secured decentralized on-device learning. By utilizing blockchain’s decentralization and tamper-proofing, our design secures AI learning in a trustless environment. To tackle the extra overhead introduced by blockchain, we propose PoMC (an algorithm and architecture co-design) as a novel blockchain consensus mechanism, which first exploits cross-domain reuse (AI learning and blockchain consensus) in AI learning architecture. Evaluation results show our DemL can protect AI learning from privacy leakage and model pollution, and demonstrated that privacy and security come with trivial hardware overhead and power consumption (2%). We believe that our work will open the door of synergizing blockchain and on-device learning for security and privacy.

BIG DATA AND DIGITAL MARKETS CONTESTABILITY: THEORY OF HARM AND DATA ACCESS REMEDIES

Abstract This article analyses the crucial role of user data for digital markets contestability and presents policy proposals devised to address growing concerns about the dominance of data-rich incumbents in digital markets. To this end, we discuss a data-driven theory of harm that would warrant ex-ante data access regulation and highlight that niche entry and growth should be the primary economic policy objective in digital markets characterized by strong data-driven network effects. We then evaluate regulatory data access remedies with respect to the involved economic trade-offs and their effectiveness for promoting niche entry and growth. Firstly, we analyse remedies that would limit the collection of user data by data-rich incumbents such as data silos and line of business restrictions. Secondly, we consider remedies that facilitate sharing of (user) data by opening up access to raw behavioural user data collected by data-rich incumbents. In particular, we propose a dual approach with two complementary types of data access remedies: bulk sharing of broad anonymized raw user data and continuous, real-time data portability of deep raw data that contain personally identifiable information. Finally, we comment on the recent proposal for a Digital Markets Act by the European Commission with respect to our findings.

Fog-enabled secure multiparty computation based aggregation scheme in smart grid

The Smart Grid (SG) is a modern power grid infrastructure for improved efficiency and reliability using modern telecommunications infrastructure, sensing, and measuring technologies. It provides sustainable electricity to customers and enables them to monitor their electricity use. The Fog Nodes (FNs) are deployed between the Cloud Control Center (CCC) and Smart Meters (SMs) to improve efficiency and reliability. The CCC is collecting usage data through FNs at regular intervals from SMs. As SM data contains private customer data, a secure scheme is required to ensure customer privacy during data collection. The usage view behavior of consumers is only possible if internal or external malicious entities collude together. In this paper, we present the Fog-Enabled Secure Multiparty Computation (FESMPC) based aggregation scheme for SG. This paper’s key contributions to the literature on the topic of secure privacy-aware aggregation schemes in SGs are threefold. Firstly, a secure aggregation scheme that preserves data privacy and ensures fault-tolerance is presented. The scheme is robust against the attacks of collusive and False Data Injection (FDI). A collusive attack is managed through enhanced Shamir’s secret scheme. At the same time, privacy during data aggregation is achieved through a Paillier homomorphic encryption scheme. Secondly, detailed security analyses are provided to show that the proposed scheme supports data privacy, source authentication, fault tolerance, and prevents FDI attacks. Thirdly, the paper presents extensive performance evaluations demonstrating that the proposed program scheme is efficient in encryption, aggregation, decryption, and less communication costs than the three existing state-of-the-art programs.

Data Management Tools to Measure the Impact of Core Facilities.

The Biomolecular Research Center at Boise State University is a research core facility that supports the study of biomolecules with an emphasis on protein structure and function, molecular interactions, and imaging. The mission of the core is to facilitate access to instrumentation that might otherwise be unavailable because of the cost, training for new users, and scientific staff with specialized skills to support early-stage investigators, as well as more established senior investigators. Data collection and management of users and their research output is essential to understand the impact of the center on the research environment and research productivity. However, challenges are often encountered when trying to fully quantify the impact of a core facility on the institution, as well as on the career success of individual investigators. This challenge can be exacerbated under the conditions of unprecedented growth in biomedical research and shared core facility use that has been experienced at Boise State University, an institution of emerging research excellence. Responding to these challenges required new approaches to information management, reporting, assessment, and evaluation. Our specific data management, evaluation, and assessment challenges included 1) collection and management of annual reporting information from investigators, staff, and students in a streamlined manner that did not lead to reporting fatigue; 2) application of software for analyzing synergy among programs' management strategy and investigator success; and 3) consolidation of core facility management, billing, and reporting capabilities into 1 cohesive system. The data management tools adopted had a beneficial effect by saving time, reducing administrative burden, and streamlining reporting. Practices implemented for data management have facilitated effective evaluation and future program planning. The substantial burden of assessment requirements necessitates early consideration of a strategy for data management to allow assessment of impact.

Improvement in credit card fraud detection using ensemble classification technique and user data

Financial fraud a serious problem in banking system. Credit card fraud growing with increasing Internet usage, as it becomes very simple to collect user data and do fraud transaction. Fortunately, all records including fraud and legit transactions are present in the financial record. Improved data mining techniques are now capable to find solutions for such outlier detections. Financial data freely available in many sources, but this data has some challenges like,1) the profile of legit and fraudulent behavior changes constantly, 2) there a class imbalance problem in dataset, because less than 3% transaction are fraud, 3) transaction verification latency also one more problem. All this data issues are handled using pre-processing techniques like cleaning and reduction. Main aim of this research to find out, output attribute is Fraud, with better time complexity. To this end, K-means, Random Forest and J48 algorithm used, and its accuracy rates are compared to find best fit pre-processing and machine learning algorithm. It observed that accuracy rate of Random Forest 93.8% when both global and local dataset used.