Search results for: social networks privacy issues

Authors: Ramin Karimi

Abstract:

One of common highly mobile wireless ad hoc networks is Vehicular Ad Hoc Networks. Hence routing in vehicular ad hoc network (VANET) has attracted much attention during the last few years. VANET is characterized by its high mobility of nodes and specific topology patterns. Moreover these networks encounter a significant loss rate and a very short duration of communication. In vehicular ad hoc networks, one of challenging is routing of data due to high speed mobility and changing topology of vehicles. Geographic routing protocols are becoming popular due to advancement and availability of GPS devices. Delay Tolerant Networks (DTNs) are a class of networks that enable communication where connectivity issues like sparse connectivity, intermittent connectivity; high latency, long delay, high error rates, asymmetric data rate, and even no end-to-end connectivity exist. In this paper, we review the existing Geographic Routing Protocols for VANETs and also provide a qualitative comparison of them.

Keywords: vehicular ad hoc networks, mobility, geographic routing, delay tolerant networks

Procedia PDF Downloads 524

Authors: Cheng Xu

Abstract:

As the metaverse emerges as a dynamic virtual simulacrum of reality, its implications on user privacy have become a focal point of interest. While previous discussions have ventured into metaverse privacy dynamics, a glaring empirical gap persists, especially concerning the effects of personalization in the context of news recommendation services. This study stands at the forefront of addressing this void, meticulously examining how users' privacy concerns shift within the metaverse's personalization context. Through a pre-registered randomized controlled experiment, participants engaged in a personalization task across both the metaverse and traditional online platforms. Upon completion of this task, a comprehensive news recommendation service provider offers personalized news recommendations to the users. Our empirical findings reveal that the metaverse inherently amplifies privacy concerns compared to traditional settings. However, these concerns are notably mitigated when users have a say in shaping the algorithms that drive these recommendations. This pioneering research not only fills a significant knowledge gap but also offers crucial insights for metaverse developers and policymakers, emphasizing the nuanced role of user input in shaping algorithm-driven privacy perceptions.

Keywords: metaverse, privacy concerns, personalization, digital interaction, algorithmic recommendations

Procedia PDF Downloads 120

Authors: Asmaa Sokrat, Aykut Karaman

Abstract:

Public and private spaces are major components of the morphology of the city. This research aims to study the interactions between public and private domains in terms of urban space in Casablanca. The research focuses on a general vision of a socio-spatial issue. It plans to identify the public, private, and transition (semi-public, semi-private) spaces as the constituent of the urban space. Moreover, the study investigates the link between public and private spaces with the social dimensions. Additionally, the research argues that the public space is a place of social interaction; as a reflection, this interaction is the intersection between urban space and social connectedness. Besides, social interaction can be the key to distinguishing between the public and private spheres. The methodological approach of the research is based on the literature review and field study. The article is targeting a case study on the old Medina of Casablanca, from daily use of the public and private spaces, the urban tissue, and the urban space types. In conclusion, the research exhibits that a public space could influence the privacy of the residents of a local urban area; thus, this privacy is inverted on the social interaction. This social interaction is the link between the urban space and social connectedness. Hence, this equation affects the typology of the private space.

Keywords: public sphere, private sphere, social connectedness, old Medina of Casablanca

Procedia PDF Downloads 143

Authors: Tayebeh Jameh-Bozorgi, M. Soleymani

Abstract:

In this research, the role of social networks in promoting ethics in Iranian sports was investigated. The research adopted a descriptive-analytic method, and the survey’s population consisted of all the athletes invited to the national football, volleyball, wrestling and taekwondo teams. Considering the limited population, the size of the society was considered as the sample size. After the distribution of the questionnaires, 167 respondents answered the questionnaires correctly. The data collection tool was chosen according to Hamid Ghasemi`s, standard questionnaire for social networking and mass media, which has 28 questions. Reliability of the questionnaire was calculated using Cronbach's alpha coefficient (94%). The content validity of the questionnaire was also approved by the professors. In this study, descriptive statistics and inferential statistical methods were used to analyze the data using statistical software. The benchmark tests used in this research included the following: Binomial test, Friedman test, Spearman correlation coefficient, Vermont Creamers, Good fit test and comparative prototypes. The results showed that athletes believed that social network has a significant role in promoting sport ethics in the community. Telegram has been known to play a big role than other social networks. Moreover, the respondents' view on the role of social networks in promoting sport ethics was significantly different in both men and women groups. In fact, women had a more positive attitude towards the role of social networks in promoting sport ethics than men. The respondents' view of the role of social networks in promoting the ethics of sports in the study groups also had a significant difference. Additionally, there was a significant and reverse relationship between the sports experience and the attitude of national athletes regarding the role of social networks in promoting ethics in sports.

Keywords: ethics, social networks, mass media, Iranian sports, internet

Procedia PDF Downloads 291

Authors: Benny Sand, Yotam Lurie, Shlomo Mark

Abstract:

Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions.

Keywords: MIoT, data privacy, stakeholders, home healthcare, information privacy, AI

Procedia PDF Downloads 105

Authors: Jumana Majdi Qutub

Abstract:

Rationale: Cloud computing has rapidly developed the past few years. Because of the importance of providing protection for personal data used in cloud computing, the role of data protection in promoting trust and confidence in users’ data has become an important policy priority. This research examines key regulatory challenges rose by the growing use and importance of cloud computing with focusing on protection of individuals personal data. Methodology: Describing and analyzing governance challenges facing policymakers and industry in Saudi Arabia, with an account of anticipated governance responses. The aim of the research is to describe and define the regulatory challenges on cloud computing for policy making in Saudi Arabia and comparing it with potential complied issues rose in respect of transported data to EU member state. In addition, it discusses information privacy issues. Finally, the research proposes policy recommendation that would resolve concerns surrounds the privacy and effectiveness of clouds computing frameworks for data protection. Results: There are still no clear regulation in Saudi Arabia specialized in legalizing cloud computing and specialty regulations in transferring data internationally and locally. Decision makers need to review the applicable law in Saudi Arabia that protect information in cloud computing. This should be from an international and a local view in order to identify all requirements surrounding this area. It is important to educate cloud computing users about their information value and rights before putting it in the cloud to avoid further legal complications, such as making an educational program to prevent giving personal information to a bank employee. Therefore, with many kinds of cloud computing services, it is important to have it covered by the law in all aspects.

Keywords: cloud computing, cyber crime, data protection, privacy

Procedia PDF Downloads 263

Authors: Yanqun Li, Hong Geng

Abstract:

The occurrence of Wenchuan earthquake in 2008 has led to severe damage to the rural areas of Chengdu city, such as the rupture of the social network, the stagnation of economic production and the rupture of living space. The post-disaster reconstruction has become a sustainable issue. As an important link to maintain the order of rural social development, social network should be an important content of post-disaster reconstruction. Therefore, this paper takes rural reconstruction communities in earthquake-stricken areas of Chengdu as the research object and adopts sociological research methods such as field survey, observation and interview to try to understand the transformation of rural social relations network under the influence of earthquake and its impact on rural space. It has found that rural societies under the earthquake generally experienced three phases: the break of stable social relations, the transition of temporary non-normal state, and the reorganization of social networks. The connotation of phased rural social relations also changed accordingly: turn to a new division of labor on the social orientation, turn to a capital flow and redistribution in new production mode on the capital orientation, and turn to relative decentralization after concentration on the spatial dimension. Along with such changes, rural areas have emerged some social issues such as the alienation of competition in the new industry division, the low social connection, the significant redistribution of capital, and the lack of public space. Based on a comprehensive review of these issues, this paper proposes the corresponding response mechanism. First of all, a reasonable division of labor should be established within the villages to realize diversified commodity supply. Secondly, the villages should adjust the industrial type to promote the equitable participation of capital allocation groups. Finally, external public spaces should be added to strengthen the field of social interaction within the communities.

Keywords: social relations, social support networks, industrial division, capital allocation, public space

Procedia PDF Downloads 159

Authors: C. L. Huang, J. J. Sia

Abstract:

The interest-based social networks, also known as social bookmark sharing systems, are useful platforms for people to conveniently read and collect internet resources. These platforms also providing function of social networks, and users can share and explore internet resources from the social networks. Providing personalized internet resources to users is an important issue on these platforms. This study uses two types of relationship on the social networks—following and follower and proposes a collaborative recommender system, consisting of two main steps. First, this study calculates the relationship strength between the target user and the target user's followings and followers to find top-N similar neighbors. Second, from the top-N similar neighbors, the articles (internet resources) that may interest the target user are recommended to the target user. In this system, users can efficiently obtain recent, related and diverse internet resources (knowledge) from the interest-based social network. This study collected the experimental dataset from Diigo, which is a famous bookmark sharing system. The experimental results show that the proposed recommendation model is more accurate than two traditional baseline recommendation models but slightly lower than the cosine model in accuracy. However, in the metrics of the diversity and executing time, our proposed model outperforms the cosine model.

Keywords: recommender systems, social networks, tagging, bookmark sharing systems, collaborative recommender systems, knowledge management

Procedia PDF Downloads 175

Authors: Derick M. Rakgoale, Topside E. Mathonsi, Vusumuzi Malele

Abstract:

Cloud computing offers distributed online and on-demand computational services from anywhere in the world. Cloud computing services have grown immensely over the past years, especially in the past year due to the Coronavirus pandemic. Cloud computing has changed the working environment and introduced work from work phenomenon, which enabled the adoption of technologies to fulfill the new workings, including cloud services offerings. The increased cloud computing adoption has come with new challenges regarding data privacy and its integrity in the cloud environment. Previously advanced encryption algorithms failed to reduce the memory space required for cloud computing performance, thus increasing the computational cost. This paper reviews the existing encryption algorithms used in cloud computing. In the future, artificial neural networks (ANN) algorithm design will be presented as a security solution to ensure data integrity, confidentiality, privacy, and availability of user data in cloud computing. Moreover, MATLAB will be used to evaluate the proposed solution, and simulation results will be presented.

Keywords: cloud computing, data integrity, confidentiality, privacy, availability

Procedia PDF Downloads 140

Authors: Veniamin Boiarkin, Bruno Bogaz Zarpelão, Muttukrishnan Rajarajan

Abstract:

The manufacturing sector is a vital component of most economies, which leads to a large number of cyberattacks on organisations, whereas disruption in operation may lead to significant economic consequences. Adversaries aim to disrupt the production processes of manufacturing companies, gain financial advantages, and steal intellectual property by getting unauthorised access to sensitive data. Access to sensitive data helps organisations to enhance the production and management processes. However, the majority of the existing data-sharing mechanisms are either susceptible to different cyber attacks or heavy in terms of computation overhead. In this paper, a privacy-preserving data-sharing scheme for smart utilities is proposed. First, a customer’s privacy adjustment mechanism is proposed to make sure that end-users have control over their privacy, which is required by the latest government regulations, such as the General Data Protection Regulation. Secondly, a local differential privacy-based mechanism is proposed to ensure the privacy of the end-users by hiding real data based on the end-user preferences. The proposed scheme may be applied to different industrial control systems, whereas in this study, it is validated for energy utility use cases consisting of smart, intelligent devices. The results show that the proposed scheme may guarantee the required level of privacy with an expected relative error in utility.

Keywords: data-sharing, local differential privacy, manufacturing, privacy-preserving mechanism, smart utility

Procedia PDF Downloads 81

Authors: Dhaksha Vivekanandan

Abstract:

DeFi is a network that avoids reliance on financial intermediaries through its peer-to-peer financial network. DeFi operates outside of government control; hence it is important for us to understand its impacts. This study employs a literature review to understand DeFi and its emergence, as well as its implications on transparency, social impact, and regulation. Further, 3 case studies are analysed within the context of these categories. DeFi’s provision of increased transparency poses environmental and storage costs and can lead to user privacy being endangered. DeFi allows for the provision of entrepreneurial incentives and protection against monetary censorship and capital control. Despite DeFi's transparency issues and volatility costs, it has huge potential to reduce poverty; however, regulation surrounding DeFi still requires further tightening by governments.

Keywords: DeFi, transparency, regulation, social impact

Procedia PDF Downloads 91

Authors: M. A. T. AlSudairi, T. G. K. Vasista

Abstract:

The concept of social media is becoming the top of the agenda for many business executives and public sector executives today. Decision makers as well as consultants, try to identify ways in which firms and enterprises can make profitable use of social media and network related applications such as Wikipedia, Face book, YouTube, Google+, Twitter. While it is fun and useful to participating in this media and network for achieving the communication effectively and efficiently, semantic and sentiment analysis and interpretation becomes a crucial issue. So, the objective of this paper is to provide literature review on social media, network and related technology related to semantics and sentiment or opinion analysis covering business and governance perspectives. In this regard, a case study on the use and adoption of Social media in Saudi Arabia has been discussed. It is concluded that semantic web technology play a significant role in analyzing the social networks and social media content for extracting the interpretational knowledge towards strategic decision support.

Keywords: CRASP methodology, formative assessment, literature review, semantic web services, social media, social networks

Procedia PDF Downloads 454

Authors: M. Sumana, K. S. Hareesha

Abstract:

Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

Procedia PDF Downloads 414

Authors: Quinn Au, Amedeo Carmine, Tauheed Khan Mohd

Abstract:

The Internet of Things (IoT) is emerging as a new development in information technology in recent years, with the potential to improve convenience and efficiency in life. Following the rise of IoT, the Social Internet of Things (SIoT) is another new development in which the benefits of connectivity and user-friendliness from social network services (SNS) are its main features. With the introduction of IoT, the world will be much more modernized, convenient, and industrialized. This paper will discuss the applications of IoT in different sectors such as healthcare services, education, and lifestyle. The privacy challenges that IoT still poses to user data will also be discussed. Finally, an empirical study to evaluate the number of active installed IoT connections in recent years demonstrates the increase in usage of IoT regardless of the privacy challenges. The study also examines some types of IoT devices that are being preferred in the market and predictions from researchers about IoT in the upcoming years.

Keywords: IoT, health care, robotics, social Internet of Things

Procedia PDF Downloads 155

Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

Abstract:

This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

Procedia PDF Downloads 105

Authors: Bojan Najdenov, Danco Davcev

Abstract:

Reduction of traffic congestions and the effects of pollution and waste of resources that come with them has been a big challenge in the past decades. Having reliable systems to facilitate the process of modeling and prediction of traffic conditions would not only reduce the environmental pollution, but will also save people time and money. Social networks play big role of people’s lives nowadays providing them means of communicating and sharing thoughts and ideas, that way generating huge knowledge bases by crowdsourcing. In addition to that, crowdsourcing as a concept provides mechanisms for fast and relatively reliable data generation and also many services are being used on regular basis because they are mainly powered by the public as main content providers. In this paper we present the Social-NETS-Traffic-Control System (SNTCS) that should serve as a facilitator in the process of modeling and prediction of traffic congestions. The main contribution of our system is to integrate data from social networks as Twitter and also implements a custom created crowdsourcing subsystem with which users report traffic conditions using an android application. Our first experience of the usage of the system confirms that the integrated approach allows easy extension of the system with other social networks and represents a very useful tool for traffic control.

Keywords: traffic, congestion reduction, crowdsource, social networks, twitter, android

Procedia PDF Downloads 483

Authors: Muhammad Abubakar Alhassan, Diane Pennington

Abstract:

Social networking sites have become a space for people to discuss public health issues such as non-suicidal self-injury (NSSI). There are thousands of tweets containing self-harm and self-injury hashtags on Twitter. It is difficult to distinguish between different users who participate in self-injury discussions on Twitter and how their opinions change over time. Also, it is challenging to understand the topics surrounding NSSI discussions on Twitter. We retrieved tweets using #selfham and #selfinjury hashtags and investigated those from the United kingdom. We applied inductive coding and grouped tweeters into different categories. This study used the Latent Dirichlet Allocation (LDA) algorithm to infer the optimum number of topics that describes our corpus. Our findings revealed that many of those participating in NSSI discussions are non-professional users as opposed to medical experts and academics. Support organisations, medical teams, and academics were campaigning positively on rais-ing self-injury awareness and recovery. Using LDAvis visualisation technique, we selected the top 20 most relevant terms from each topic and interpreted the topics as; children and youth well-being, self-harm misjudgement, mental health awareness, school and mental health support and, suicide and mental-health issues. More than 50% of these topics were discussed in England compared to Scotland, Wales, Ireland and Northern Ireland. Our findings highlight the advantages of using the Twitter social network in tackling the problem of self-injury through awareness. There is a need to study the potential risks associated with the use of social networks among self-injurers.

Keywords: self-harm, non-suicidal self-injury, Twitter, social networks

Procedia PDF Downloads 136

Authors: Shayan Mohajer Hamidi

Abstract:

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

Procedia PDF Downloads 78

Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

Abstract:

Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

Procedia PDF Downloads 300

Authors: Pi Hsia Liang

Abstract:

The rise of Facebook, Twitter, and other social networks significantly changes in interconnections between people, enhancing the process of information dissemination and amplify the influence of that information. Therefore, to develop informational efficiency or signaling equilibrium type of information environment among social networks, without adverse selection effects, becomes an important issue. Thus, someone may post a piece of intentional information in relation to personal interest for trying to create marginal influence. Therefore, economists are seeking to establish theories of informational efficiency under social network environment in order to resolve adverse selection issues. Reputation could be one of the important factors in the process of creating informational efficiency. Additionally, investors how to process green information, or information of corporate social responsibility is a very important study. This study essentially employs experimental study for examining how investors use stock relevant green information in Facebook and various Taiwan local networks. Facebook, and blogs of Money DJ, Technews and cnYES, respectively, are the primary sites for this examination that also allow to differentiate effects between Facebook and other local social networks. Questionnaire is developed for such an experimental testing. Note that questionnaire allows this study to group, for example, decision frequency and length of time duration focusing on social networks that are used for discriminating investor type and competence of informed investor. This study selects 500 investors that can be separated into two respective 250 samples as the control group and 250 samples in such an experimental. The quantity of sample investor sufficiently results in statistic significance of this experimental study. The empirical results of this study can be used for explaining how financial information in relation to corporate social responsibility would be disseminated in social websites. Therefore, we can lead to better interpretation of price/earnings relationship type of study and empirical studies of green information usefulness or informational efficiency Note that the above mentioned empirical studies did not exist any social network and annual report of corporate social responsibility. This study expects to find the results that both network degree and network cluster significantly affected green information dissemination frequency. In other words, investors with more connections and with high clustered connections might exert a greater influence on their green information dissemination process. The preferred users of financial social networks could make better stock decision that could amplify effects of green information. In addition, Facebook would be more influential than other local Taiwan financial social networks, although Facebook is not a specialized financial social network. In other words, the popularity and reputation effects of Facebook significantly contribute to usefulness of green information and influence of green information. Third, it has a better chance to find rumor or cheating information in local Taiwan financial social networks than Facebook. In other words, Facebook possesses reputation effect, or a better informational efficiency. Or, even though Taiwan local financial social networks have marginal informational effects on stock price, because of shortage of informational efficiency or monitoring system, information could be a tool for those whom owning superior information.

Keywords: network effect on financial services, informational efficiency theory, social networks, social websites

Procedia PDF Downloads 251

Authors: Jehan Fahim M. Alsulami

Abstract:

Academic research about sentiment analysis in sentiment analysis has obtained significant advancement over recent years and is flourishing from the collection of knowledge provided by various academic disciplines. In the current study, the status and development trend of the field of sentiment analysis in social networks is evaluated through a bibliometric analysis of academic publications. In particular, the distributions of publications and citations, the distribution of subject, predominant journals, authors, countries are analyzed. The collaboration degree is applied to measure scientific connections from different aspects. Moreover, the keyword co-occurrence analysis is used to find out the major research topics and their evolutions throughout the time span. The area of sentiment analysis in social networks has gained growing attention in academia, with computer science and engineering as the top main research subjects. China and the USA provide the most to the area development. Authors prefer to collaborate more with those within the same nation. Among the research topics, newly risen topics such as COVID-19, customer satisfaction are discovered.

Keywords: bibliometric analysis, sentiment analysis, social networks, social media

Procedia PDF Downloads 222

Authors: Lele Dominic Dummene

Abstract:

Environmental issues are predominant in rural areas where indigenous people reside. These environmental issues cover environmental, health, social, economic, and political issues that emanate from poor environmental management and unfair distribution of environmental resources. These issues have greatly affected the lives of the indigenous people and their daily activities. As these environmental issues grow in communities, environmental experts, scientists, and theorists have proposed and developed methods, policies, and strategies to address these environmental-related issues in indigenous communities. Thus, this paper explores how the Ogoni indigenous knowledge and cultural practices could be used to address environmental issues such as oil pollution and other environmental-related issues that have destroyed the Ogoni environment.

Keywords: Ogoniland, indigenous knowledge, environment, environmental education

Procedia PDF Downloads 125

Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

Abstract:

This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

Procedia PDF Downloads 191

Authors: Anushtha Saxena

Abstract:

This paper examines the process of data monetisation bye-commerce companies operating in India. Data monetisation is collecting, storing, and analysing consumers’ data to use further the data that is generated for profits, revenue, etc. Data monetisation enables e-commerce companies to get better businesses opportunities, innovative products and services, a competitive edge over others to the consumers, and generate millions of revenues. This paper analyses the issues and challenges that are faced due to the process of data monetisation. Some of the issues highlighted in the paper pertain to the right to privacy, protection of data of e-commerce consumers. At the same time, data monetisation cannot be prohibited, but it can be regulated and monitored by stringent laws and regulations. The right to privacy isa fundamental right guaranteed to the citizens of India through Article 21 of The Constitution of India. The Supreme Court of India recognized the Right to Privacy as a fundamental right in the landmark judgment of Justice K.S. Puttaswamy (Retd) and Another v. Union of India . This paper highlights the legal issue of how e-commerce businesses violate individuals’ right to privacy by using the data collected, stored by them for economic gains and monetisation and protection of data. The researcher has mainly focused on e-commerce companies like online shopping websitesto analyse the legal issue of data monetisation. In the Internet of Things and the digital age, people have shifted to online shopping as it is convenient, easy, flexible, comfortable, time-consuming, etc. But at the same time, the e-commerce companies store the data of their consumers and use it by selling to the third party or generating more data from the data stored with them. This violatesindividuals’ right to privacy because the consumers do not know anything while giving their data online. Many times, data is collected without the consent of individuals also. Data can be structured, unstructured, etc., that is used by analytics to monetise. The Indian legislation like The Information Technology Act, 2000, etc., does not effectively protect the e-consumers concerning their data and how it is used by e-commerce businesses to monetise and generate revenues from that data. The paper also examines the draft Data Protection Bill, 2021, pending in the Parliament of India, and how this Bill can make a huge impact on data monetisation. This paper also aims to study the European Union General Data Protection Regulation and how this legislation can be helpful in the Indian scenarioconcerning e-commerce businesses with respect to data monetisation.

Keywords: data monetization, e-commerce companies, regulatory framework, GDPR

Procedia PDF Downloads 125

Authors: Malika Yaici, Feriel Lalaoui, Lydia Belhoul

Abstract:

As a new distributed point-to-point (P2P) technology, blockchain has become a very broad field of research, addressing various challenges, including privacy preserving, as is the case in all other technologies. In this work, a study of the existing solutions to the problems related to private life in general and in blockchains in particular is performed. User anonymity and transaction confidentiality are the two main challenges to the protection of privacy in blockchains. Mixing mechanisms and cryptographic solutions respond to this problem but remain subject to attacks and suffer from shortcomings. Taking into account these imperfections and the synthesis of our study, we present a mixing model without trusted third parties, based on group signatures, allowing reinforcing the anonymity of the users, the confidentiality of the transactions, with minimal turnaround time and without mixing costs.

Keywords: anonymity, blockchain, mixing coins, privacy

Procedia PDF Downloads 17

Authors: Omar Farshad Jeelani, Makaire Njie, Viktoriia M. Korzhuk

Abstract:

Recently, the application of AI-powered algorithms in healthcare continues to flourish. Particularly, access to healthcare information, including patient health history, diagnostic data, and PII (Personally Identifiable Information) is paramount in the delivery of efficient patient outcomes. However, as the exchange of healthcare information between patients and healthcare providers through AI-powered solutions increases, protecting a person’s information and their privacy has become even more important. Arguably, the increased adoption of healthcare AI has resulted in a significant concentration on the security risks and protection measures to the security and privacy of healthcare data, leading to escalated analyses and enforcement. Since these challenges are brought by the use of AI-based healthcare solutions to manage healthcare data, AI-based data protection measures are used to resolve the underlying problems. Consequently, this project proposes AI-powered safeguards and policies/laws to protect the privacy of healthcare data. The project presents the best-in-school techniques used to preserve the data privacy of AI-powered healthcare applications. Popular privacy-protecting methods like Federated learning, cryptographic techniques, differential privacy methods, and hybrid methods are discussed together with potential cyber threats, data security concerns, and prospects. Also, the project discusses some of the relevant data security acts/laws that govern the collection, storage, and processing of healthcare data to guarantee owners’ privacy is preserved. This inquiry discusses various gaps and uncertainties associated with healthcare AI data collection procedures and identifies potential correction/mitigation measures.

Keywords: data privacy, artificial intelligence (AI), healthcare AI, data sharing, healthcare organizations (HCOs)

Procedia PDF Downloads 100

Authors: Sakulsri Srisaracam

Abstract:

Social media has become an important source of information for the public and the media profession. Some social issues raised on social media are picked up by journalists to report on other platforms. This relationship between social media and mainstream media can sometimes drive public debate or stimulate social movements. The question to examine is in what situations can social media conversations raise awareness and stimulate change on public issues. This study addresses the communication patterns of social media conversations driving covert issues into mainstream media and leading to social advocacy movements. In methodological terms, the study findings are based on a content analysis of Facebook, Twitter, news websites and television media reports on three different case studies – saving Bryde’s whale, protests against a government proposal to downsize the Office of Knowledge Management and Development in Thailand, and a dengue fever campaign. These case studies were chosen because they represent issues that most members of the public do not pay much attention to but social media conversations stimulated public debate and calls to action. This study found: 1) Collective social media conversations can stimulate public debate and encourage change at three levels – awareness, public debate, and action of policy and social change. The level depends on the communication patterns of online users and media coverage. 2) Patterns of communication have to be designed to combine social media conversations, online opinion leaders, mainstream media coverage and call to both online and offline action to motivate social change. Thus, this result suggests that social media is a powerful platform for collective communication and setting the agenda on public issues for mainstream media. However, for social change to succeed, social media should be used to mobilize online movements to move offline too.

Keywords: public issues, mainstream media, social media, social movement

Procedia PDF Downloads 286

Authors: Chechen Liao, Sheng Yi Lin

Abstract:

With the popularity of smartphone usage and the flourish of social networks, many people began to use the 'check-in' functions to share their location information and days of live and self-disclosure. In order to increase exposure and awareness, some stores provide discounts and other benefits to attract consumers to 'check-in' in their stores. The purpose of this study was to investigate whether personality traits, intrinsic motivations, extrinsic motivations, and privacy concerns would affect self-disclosure of location for consumers. Research data were collected from 407 individuals that have used Facebook check-in in Taiwan. This study used SmartPLS 2.0 structural equation modeling to validate the model. The results show that information sharing, information storage, enjoyment, self-presentation, get a feedback, economic reward, and keep up with trends had significant positive effects on self-disclosure. While extroversion and openness to use have significant positive effects on self-disclosure, conscientiousness and privacy concerns have significant negative effects on self-disclosure. The results of the study provide academic and practical implications for the future growth of location-based self-disclosure.

Keywords: check-in, extrinsic motivation, intrinsic motivation, personality trait, self-disclosure

Procedia PDF Downloads 172

Authors: Pantaleon Lutta, Mohamed Sedky, Mohamed Hassan

Abstract:

The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data are stored on the devices. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality).

Keywords: cloud forensics, data protection Laws, GDPR, IoT forensics, machine Learning

Procedia PDF Downloads 153

Authors: Ehsan Khademi, Mahdi Jalili

Abstract:

Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks

Procedia PDF Downloads 629