Level Of Privacy Research Articles

The impact of using WhatsApp on the team’s communication, employee performance and data confidentiality

This research aims to explore the ways in which the use of WhatsApp for diagonal and lateral communication can improve the achievement of tasks, to what extent it can keep data and information trustworthy and confidential, and in what ways WhatsApp improves the communication of suggestions, instructions, and complaints. The study uses a quantitative research strategy with one independent variable, which is WhatsApp usage in the workplace, and three dependent variables, which are team member communication, employee performance, and confidentiality. To test the proposed research model, the authors conduct an online questionnaire in the United Arab Emirates. Descriptive statistics are used to analyze the quantitative data collected through the questionnaires. The study shows that the use of WhatsApp for communication is positively associated with leader-member exchange (LMX) and team-member exchange (TMX). Both LMX and TMX have a positive correlation with employee performance. WhatsApp is a trusted method to transfer information between team members and between managers and employees. The study also asserts that the use of WhatsApp is an effective tool to improve productivity and performance, and it makes task completion faster. It appears that the study has a limited literature review and lacks previous research on the variables related to data confidentiality and improving team performance. In this case, the study seems to be lacking a thorough examination of prior research related to data confidentiality and its impact on team performance. WhatsApp is a widely used messaging application that offers end-to-end encryption to its users, and this encryption provides a certain level of security and privacy. WhatsApp usage has a positive impact on team performance and productivity. The study presents a concrete understanding of how vertical and horizontal relationships connect the impact of WhatsApp communication on employee performance. The study recommends the use of WhatsApp in the workplace as a safe tool to boost performance and improve productivity and satisfaction.

МЕТОД БАГАТОСТОРОННІХ ОБЧИСЛЕНЬ КЕРУВАННЯ КРИПТОАКТИВАМИ НА ОСНОВІ ТЕХНОЛОГІЇ MULTI-PARTY COMPUTATION

The task of building an efficient and secure crypto-currency wallet based on multi-party computing technology, which can provide reliable and secure use in various industries working with cryptocurrencies, is considered. Modern algorithms of multilateral calculations, such as those of Gennaro and Goldfeder, Lindella, Doerner, offer significant advantages in data protection, but they have certain limitations. The main ones are insufficient efficiency in the number of signature rounds and lack of cold storage support. This highlights the need for further research and development in the field of Multi-Party Computation technology to achieve higher levels of operational efficiency and flexibility. The method of ensuring the confidentiality and security of cryptoassets on the dream technology of multilateral computing for the Ethereum network consists in: ensuring a high level of security for the storage of private keys and the process of signing the transaction, using advanced cryptographic protection methods; guaranteeing the confidentiality and privacy of users, protecting their personal information from unauthorized access or data leakage; using advanced technologies, including Multi-Party Computation technology, to increase the level of security and privacy of asset storage. On the basis of the conducted research and analysis of the characteristics of multiparty computing protocol standards, a protocol is proposed that takes into account the existing improvements and shortcomings of previous protocols. To improve security, the protocol implements the principle of interval update of the shared secret. A cryptoasset management system based on multilateral computing includes several important stages: choosing cloud providers, creating and configuring instances, containerizing applications, configuring secure communication and routing. The system provides the possibility of reliable storage of cryptoassets, in particular, private keys to wallets of the Ethereum network, using the Multi-Party Computation technology, which can be used in various cryptoprojects for the safe transfer of cryptoassets. The architecture of the system involves the interaction of the main components to ensure reliable and safe operation. A scheme for establishing a secure connection of nodes is proposed and a local storage model is described, which provides secure storage of key shares, signatures, and system logs.

Long-Acting Reversible Etonogestrel Subdermal Implant in Adolescents.

Several effective contraceptive options are available for use by adolescents, including the long-acting reversible subdermal implant and intrauterine devices, which provide a high level of convenience, privacy, and effectiveness for an adolescent. Knowledge of all the effective birth control methods is essential for the pediatrician to be able to provide effective contraceptive counseling for an adolescent. An approach to counseling using a reproductive justice framework, which allows the provider and adolescent patient to engage in shared decision-making, is described. This article focuses on the long-acting reversible etonogestrel (ENG) subdermal implant for adolescents. The ENG implant is labeled for preventing pregnancy by suppressing ovulation. The ENG implant may also have a role in ameliorating dysmenorrhea and heavy menstrual bleeding. Postlabeling studies indicate that the ENG implant is effective for up to 5 years, although the device's labeling states effectivenessup to 3 years. The main contraindication to using the ENG implant is pregnancy itself. Safe initiation of the ENG implant is described, including an approach to determine whether an adolescent is pregnant. The main adverse effect of the ENG implant is an unpredictable bleeding pattern that is most often ameliorated by use of nonsteroidal anti-inflammatory medications, as well as estrogen, if not contraindicated for the patient. Details of the insertion and removal procedures, including potential complications, are described to enable the pediatrician to provide effective anticipatory guidance for the adolescent.

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

In recent years, systems that collect location information and publish statistics, such as those that publish congestion information, have been extensively employed. Because it is possible to infer an individual’s identity even if the information is not directly disclosed, it is essential to disclose data with privacy protection. Therefore, privacy protection methods based on differential privacy are attracting attention. Geo-indistinguishability is the most famous extension theorem of differential privacy for location information. Geo-indistinguishability can be achieved by adding noise to a target value that must be protected. However, noise addition reduces the usefulness of the data. Thus, it is desirable to add minimal noise to your privacy budget. Therefore, we focus on the fact that the values obtained using measurement devices contain errors. We introduced a novel concept of differential privacy tailored for location information, termed true-value-based geo-indistinguishability (T-Geo-I), which accounts for equipment noise. We also proposed a location information privacy protection method that considers T-Geo-I and reduces the amount of added noise. The object of privacy protection should be the “true value” not the “measured value” that includes measurement errors. According to the experimental results, in the case wherein the measurement error is the normal distribution, our method reduced the noise average and mean square error (MSE) by up to 41% and 63%, respectively, compared with conventional methods while maintaining a prespecified level of privacy in 108 samples of numerical data. In the case wherein the measurement error is the lognormal distribution, the proposed method based on T-Geo-I succeeded in reducing the noise average and MSE by up to 60% and 67%, respectively, compared with methods based on Geo-I, while maintaining a prespecified level of privacy. These findings indicate that the proposed method can improve the usefulness of data while maintaining a prespecified degree of privacy protection.

A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses

Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without a significant drop in model utility. Most existing defenses against membership inference attacks assume access to reference data, defined as an additional dataset coming from the same (or a similar) underlying distribution as training data. Despite the common use of reference data, previous works are notably reticent about defining and evaluating reference data privacy. As gains in model utility and/or training data privacy may come at the expense of reference data privacy, it is essential that all three aspects are duly considered. In this paper, we conduct the first comprehensive analysis of empirical privacy defenses. First, we examine the availability of reference data and its privacy treatment in previous works and demonstrate its necessity for fairly comparing defenses. Second, we propose a baseline defense that enables the utility-privacy tradeoff with respect to both training and reference data to be easily understood. Our method is formulated as an empirical risk minimization with a constraint on the generalization error, which, in practice, can be evaluated as a weighted empirical risk minimization (WERM) over the training and reference datasets. Although we conceived of WERM as a simple baseline, our experiments show that, surprisingly, it outperforms the most well-studied and current state-of-the-art empirical privacy defenses using reference data for nearly all relative privacy levels of reference and training data. Our investigation also reveals that these existing methods are unable to trade off reference data privacy for model utility and/or training data privacy, and thus fail to operate outside of the high reference data privacy case. Overall, our work highlights the need for a proper evaluation of the triad model utility / training data privacy / reference data privacy when comparing privacy defenses.

PRIVIC: A privacy-preserving method for incremental collection of location data

With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate the privacy risks for location data consists in adding noise to the true values to achieve geo-indistinguishability (geo-ind). However, geo-ind alone is not sufficient to cover all privacy concerns. In particular, isolated locations are not sufficiently protected by the state-of-the-art Laplace mechanism (LAP) for geo-ind. In this paper, we focus on a mechanism based on the Blahut-Arimoto algorithm (BA) from the rate-distortion theory. We show that BA, in addition to providing geo-ind, enforces an elastic metric that mitigates the problem of isolation. Furthermore, BA provides an optimal trade-off between information leakage and quality of service. We then proceed to study the utility of BA in terms of the statistics that can be derived from the reported data, focusing on the inference of the original distribution. To this purpose, we de-noise the reported data by applying the iterative Bayesian update (IBU), an instance of the expectation-maximization method. It turns out that BA and IBU are dual to each other, and as a result, they work well together, in the sense that the statistical utility of BA is quite good and better than LAP for high privacy levels. Exploiting these properties of BA and IBU, we propose an iterative method, PRIVIC, for a privacy-friendly incremental collection of location data from users by service providers. We illustrate the soundness and functionality of our method both analytically and with experiments.

NEXT EDITION AND SCOPIO & CONTRAST INTERNATIONAL CONFERENCE

NEXT EDITION AND SCOPIO & CONTRAST INTERNATIONAL CONFERENCE

Student Perceptions of the Effectiveness of the Scola Learning Management System (LMS) in Improving the Quality of Learning

This study aims to explore students' perceptions of the Teaching and Learning Process (Kegiatan Belajar Mengajar - KBM) involving the use of Learning Management System (LMS), particularly Scola. The research method employed is qualitative, supported by a descriptive quantitative approach, combining observations, literature reviews, and questionnaires as data collection tools. The results of the study indicate a high interest among students in using LMS Scola to support the teaching and learning process. Students express satisfaction with the use of LMS Scola, considering it effective, efficient, user-friendly, and ensuring a high level of data privacy. These findings indicate that the utilization of technology, especially LMS, can have a positive impact on the learning process, motivate students, and create a more interactive learning environment. The implications of this research provide a crucial push to encourage the adoption of LMS, such as Scola, in various educational institutions. By leveraging this technology, it is anticipated that the learning process can become more efficient, effective, and supportive of overall educational development. This study serves as a robust foundation for enhancing the quality of education through the integration of technology in the Teaching and Learning Process (KBM).

Regulasi Layanan Kesehatan Digital di Indonesia: Tantangan Etis dan Hukum

Digitalization promotes more effective and efficient health-care services. Following the COVID-19 epidemic, there has been a surge in global interest and investment in digital healthcare. WHO has issued guidelines and recommendations to countries in order for them to properly use telemedicine on a global scale. This is not only beneficial to people who want to protect themselves against COVID-19 and other diseases to which they may be susceptible, but it also has the potential to increase access to health care. There are various problems in integrating digital healthcare across the country in Indonesia, a country that does not yet have universal healthcare and struggles with high levels of skepticism in the public health system. Indonesian technology laws currently do not explicitly control telehealth, nor do suitable data protection regulations exist to manage the massive volumes of data generated by digital health services if adopted on a wide scale. Furthermore, there are concerns regarding the level of patient privacy, which may be jeopardized by digital health services. Aside from legal concerns about privacy, there is no framework in place to ensure informed consent in the context of digital healthcare. In this study, the researcher examines the legal, structural, and ethical difficulties surrounding digital health and offer insight into the problems caused by these flaws, as well as policy proposals to remedy these issues. The outcomes of the discussion indicate that data protection rules in Indonesia must address the different concerns listed above. Furthermore, gaps in health-care access and public-sector distrust make universal implementation of digital health services difficult. Aside from legal considerations, the digitalization of health services in Indonesia has ethical implications. The key ethical considerations are informed consent, which is a critical issue, particularly for marginalized groups with poor reading levels and communities that have historically been targets of medical exploitation.

Studi Aspek Healing Environment Dalam Desain Interior Kafe

Currently, the cafe is a place to relieve stress through a healing concept in its interior design. So, the cafe design must be as comfortable as possible to meet the concept goals. This research aims to determine user perception of the healing environment in a cafe environment based on three aspects (the five basic senses, user psychology, and indoor and outdoor natural elements) and was conducted using quantitative research methodology. Data retrieval was carried out through Google Forms media using closed-ended questions. The results proved that three aspects of the healing environment, including sensory, psychological, and natural aspects described in lighting, color tone selection, air, a good level of privacy, and the presence of plants, could help the healing process. If there is one aspect that is lacking, then visitors cannot feel healing. If a café wants to create a healing environment, then the three aspects (senses, psychological, and natural) must be fulfilled.

An effective iris biometric privacy protection scheme with renewability

Iris is considered a unique, robust biometric that can be used as an authentication factor. Privacy protection of individuals’ iris templates is an essential requirement. The essential attributes needed to ensure privacy of biometrics are irreversibility, renewability, and unlinkability. Information distortion is an effective way to achieve the privacy. However, this may lead to some degradation of recognition performance. A trade-off between recognition performance and the privacy level provided by the authentication system arises when it tries to provide both simultaneously. This paper proposes a novel approach that ensures the privacy of each individual's iris by distorting iris feature information. At the meantime, this approach balances the trade-off mentioned above up to a tolerable level. The binary feature templates of iris biometrics are transformed into another irreversible binary representation by manipulating them block-wise. The results obtained against two datasets using proposed approach illustrate excessive Genuine Acceptance Rates (GAR) when the approach accepts imposters at a rate which is reduced to 0.01 %. The proposed approach satisfies the three attributes that a biometric system should fulfil, namely irreversibility, renewability, and unlinkability. The renewability attribute induces the cancellability of iris biometric template. Compared to the existing works, the proposed approach maintains Equal Error Rate (EER) at 0.18 %.

Killing two birds with one stone: Quantization achieves privacy in distributed learning

Communication efficiency and privacy protection are two critical issues in distributed machine learning. Existing methods tackle these two issues separately and may have a high implementation complexity that constrains their application in a resource-limited environment. We propose a comprehensive quantization-based solution that could simultaneously achieve communication efficiency and privacy protection, providing new insights into the correlated nature of communication and privacy. Specifically, we demonstrate the effectiveness of our proposed solutions in the distributed stochastic gradient descent (SGD) framework by adding binomial noise to the uniformly quantized gradients to reach the desired differential privacy level but with a minor sacrifice in communication efficiency. We theoretically capture the new trade-offs between communication, privacy, and learning performance.

Golden Search Optimization based adaptive and diagonal kernel convolution neural network for disease prediction and securing IoT data in cloud

Due to their open architecture, Internet of Things (IoT) devices have limited user access and are susceptible to various security vulnerabilities. When healthcare data is stored in the cloud, it often becomes vulnerable to side-channel attacks, Denial of Service (DoS) attacks, and eavesdropping. Consequently, there is a need for an efficient access control scheme to enhance the data-sharing processes of IoT devices. Different machine learning and rule-based decision support systems have been proposed to improve the security of cloud-based data storage. However, these models have exhibited increased false alarm rates, complex processing, and longer response times. To address this research gap, we propose a Fuzzy TOPSIS model based on Golden Search Optimization (GSO) to enhance the security of patient information at both IoT and cloud server levels. Additionally, we introduce a revolutionary Diagonal Kernel Convolution Neural Network with an adaptable kernel (DKCNN-AK) to enhance the accuracy of lung disease diagnosis. The GSO algorithm categorizes the security ranks determined by the fuzzy TOPSIS model into the best and worst security principles. We conducted simulations using the COVID-19 X-Ray detection dataset, the lung cancer dataset, and the data collected from IoT devices for analysis.The experiments were conducted to assess various aspects, including computational time, security level analysis, information leakage, privacy level, prediction accuracy, sensitivity, specificity, and confusion matrix. The improved prediction accuracy and security provided by our proposed approach demonstrate its efficiency

A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.

A cloud‐edge service offloading method for the metaverse in smart manufacturing

SummaryWith the development of artificial intelligence, cloud‐edge computing and virtual reality, the industrial design that originally depends on human imagination and computing power can be transitioned to metaverse applications in smart manufacturing, which offloads the services of metaverse to cloud and edge platforms for enhancing quality of service (QoS), considering inadequate computing power of terminal devices like industrial sensors and access points (APs). However, large overhead and privacy exposure occur during data transmission to cloud, while edge computing devices (ECDs) are at risk of overloading with redundant service requests and difficult central control. To address these challenges, this paper proposes a minority game (MG) based cloud‐edge service offloading method named COM for metaverse manufacturing. Technically, MG possesses a distribution mechanism that can minimize reliance on centralized control, and gains its effectiveness in resource allocation. Besides, a dynamic control of cut‐off value is supplemented on the basis of MG for better adaptability to network variations. Then, agents in COM (i.e., APs) leverage reinforcement learning (RL) to work on MG history, offloading decision, QoS mapping to state, action and reward, for further optimizing distributed offloading decision‐making. Finally, COM is evaluated using a variety of real‐world datasets of manufacturing. The results indicate that COM has 5.38% higher QoS and 8.58% higher privacy level comparing to benchmark method.

People’s political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data

BackgroundAs the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people’s residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people’s concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people’s private locations.MethodsWe conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people’s political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables.ResultsWe observed a tradeoff relationship between people’s privacy concerns and the acceptance (and perceived social benefits) of the control measures. People’s perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns.ConclusionsOur results first suggest that people’s privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people’s acceptance and perceived social benefits of the control measures but may not reduce people’s privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people’s privacy concerns tend to increase with their conservatism and individualism.

Forward Secrecy Attack on Privacy Preserving Machine Authenticated Key Agreement for Internet of Things

Internet of things (IoT) is to connect billions of devices and machines via Internet and to have a smart system. Sensors and devices in IoT environment are connected and communicated together. Connecting such a huge number of devices requires high level of security and privacy. A crucial characteristic of ubiquitous IoT devices is their limited resources. In recent times, a scheme for privacy-preserving machine authenticated key agreement scheme (PPMAKA) has been introduced for the IoT environment. It was argued that PPMAKA provides security and privacy at the same time including forward secrecy. Nevertheless, this paper will demonstrate that PPMAKA lacks forward secrecy, a crucial security and privacy feature in the IoT environment. We use Cannetti and Krawzyck threat model for the detailed analysis of PPMAKA. Furthermore, we provide remarks for the future research as it is recommendable to design any security and privacy schemes over IoT environments with lightweight operation and communication property, authenticated key agreement with forward secrecy, anonymity and unlinkability.

Rental and Tenement Housing Problems in Sapele Local Government Area, Delta State

Rental/tenement housing problem is a persistent sustainable development challenge that affects livability, particularly in developing countries. The research examined the availability of rental housing, accessibility of rental housing location and neighbourhood facilities, environmental condition and overcrowding as factors of rental/tenement housing problems in Sapele Local Government Area. A total of 384 questionnaires were systematically distributed to household heads and 339 were returned. Data were analysed using both inferential and descriptive statistics. The result revealed that there is inadequacy in the availability of rental housing, and that the location of rental housing is highly accessible and rentals have high accessibility to neighbourhood facilities. It was found that there is an association between road conditions and the level of impediment to rental housing unit. The findings further showed that there was low crime rate and the studyarea had low susceptibility to flooding. Rental housing was found not to be affordable since a higher percentage of residents spends more than 30% of their income on rent and the maximum occupancy ratio was 2 persons per room and of such, there is no problem of overcrowding and privacy. The findings also revealed that there is a link between the number of persons per room and the level of privacy. It is recommended that government should be more involved in the provision of low-income rental housing units and formulation of rental policy.

A novel deep-learning based approach to DNS over HTTPS network traffic detection

<div align="center"><span lang="EN-US">Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a <a name="_Hlk143263730"></a>DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification.</span></div>

Distributed Anti-Eavesdropping Fusion Estimation Under Energy Constraints

This paper studies the distributed fusion estimation problem with energy-constrained sensors in the presence of eavesdroppers, where smart sensors send their local estimates to a remote fusion center (FC). To enhance privacy level, a novel encryption strategy is proposed by establishing an optimization problem, where the optimization objective is constructed by maximizing a combination of the fusion terminal estimation error covariance and the cost of encryption process in finite time domain. Meanwhile, the established problem is decomposed into several independent sub-optimization problems under a relaxation condition, and a sufficient condition that depends on the system parameters is derived such that the sub-optimization problems have optimal solutions. In this case, an optimal encryption strategy is designed with an analytical solution. Finally, a simulation example is given to show the effectiveness of the proposed methods.