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Journal articles on the topic 'Privacy threats'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Tanvir Rahman Akash, Nusrat Jahan Sany, Lamia Akter, and Sanjida Akter Sarna. "Privacy - Preserving Technique in cybersecurity: Balancing Data Protection and User Rights." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 248–63. https://doi.org/10.32996/jcsts.2025.7.3.90.

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Increasing technological complexity of cyber threats creates a major challenge between securing data privacy and maintaining potent cybersecurity practices. The paper examines privacy-protecting security methods in cybersecurity by detailing organizational approaches to defend private information throughout the cyber threat detection and mitigation process. Organizations need to establish the appropriate levels of data security because implementations that limit privacy too much threaten their security capabilities but weak protection measures create vulnerabilities to data breaches. The resea

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Kunz, Immanuel, Konrad Weiss, Angelika Schneider, and Christian Banse. "Privacy Property Graph: Towards Automated Privacy Threat Modeling via Static Graph-based Analysis." Proceedings on Privacy Enhancing Technologies 2023, no. 2 (2023): 171–87. http://dx.doi.org/10.56553/popets-2023-0046.

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Privacy threat modeling should be done frequently throughout development and production to be able to quickly mitigate threats. Yet, it can also be a very time-consuming activity. In this paper, we use an enhanced code property graph to partly automate the privacy threat modeling process: It automatically generates a data flow diagram from source code which exhibits privacy properties of data flows, and which can be analyzed semi-automatically via queries. We provide a list of such reusable queries that can be used to detect various privacy threats. To enable this analysis, we integrate a tain

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Park, Sungchae, and Heung-Youl Youm. "Security and Privacy Threats and Requirements for the Centralized Contact Tracing System in Korea." Big Data and Cognitive Computing 6, no. 4 (2022): 143. http://dx.doi.org/10.3390/bdcc6040143.

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As COVID-19 became a pandemic worldwide, contact tracing technologies and information systems were developed for quick control of infectious diseases in both the private and public sectors. This study aims to strengthen the data subject’s security, privacy, and rights in a centralized contact tracing system adopted for a quick response to the spread of infectious diseases due to climate change, increasing cross-border movement, etc. There are several types of contact tracing systems: centralized, decentralized, and hybrid models. This study demonstrates the privacy model for a centralized cont

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Barash, C. I. "Threats to Privacy Protection." Science 318, no. 5852 (2007): 913c—914c. http://dx.doi.org/10.1126/science.318.5852.913c.

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Vemou, Konstantina, and Maria Karyda. "Requirements for private communications over public spheres." Information & Computer Security 28, no. 1 (2019): 68–96. http://dx.doi.org/10.1108/ics-01-2019-0002.

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Purpose In the Web 2.0 era, users massively communicate through social networking services (SNS), often under false expectations that their communications and personal data are private. This paper aims to analyze privacy requirements of personal communications over a public medium. Design/methodology/approach This paper systematically analyzes SNS services as communication models and considers privacy as an attribute of users’ communication. A privacy threat analysis for each communication model is performed, based on misuse scenarios, to elicit privacy requirements per communication type. Fin

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Rodrigues, Andrey, Maria Lúcia Villela, and Eduardo Feitosa. "Exploring how experienced and unexperienced professionals use a privacy threat modeling methodology." Journal on Interactive Systems 14, no. 1 (2023): 274–91. http://dx.doi.org/10.5753/jis.2023.3235.

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Online Social Networks (OSNs) have become one of the principal technological phenomena of the Web, gaining an eminent popularity among its users. With the growing worldwide expansion of OSN services, people have devoted time and effort to maintaining and manipulating their online identity on these systems. However, the processing of personal data through these networks has exposed users to various privacy threats. Consequently, new solutions need to be developed for addressing the threat scenarios to which a user is potentially exposed. In this sense, this paper proposes PTMOL (Privacy Threat

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Renee Staton, A., and Michele Kielty. "A Lurking Threat: Counselor Practices to Guard Against Cyber Threats." Journal of Mental Health Counseling 45, no. 1 (2023): 20–33. http://dx.doi.org/10.17744/mehc.45.1.02.

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As a result of shifts in global power, the growth of cyber capabilities worldwide, and the impact of COVID-19 on the increasing use of technology in varied workplaces, the potential for threat and disruption to mental health facilities is expected to increase. Counselors and their clients are vulnerable to these threats any time they share data online with each other, with third-party payers, and with allied health professionals. In fact, any private information stored digitally is potentially at risk for exploitation or theft. This current environment highlights the need for an intentional pr

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Olarinde, Mobolaji Olusola`, Akeem Adekunle Abiona, and Micheal Olalekan Ajinaja. "Adaptive and Privacy-Preserving Security for Federated Learning Using Biological Immune System Principles." Asian Journal of Computer Science and Technology 13, no. 2 (2024): 67–72. https://doi.org/10.70112/ajcst-2024.13.2.4303.

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The increasing frequency and sophistication of cyber threats pose significant challenges to the security of distributed systems handling sensitive data. Federated learning, a decentralized machine learning framework, enables collaborative model training without sharing raw data, offering privacy advantages. However, ensuring the security and resilience of federated learning systems remains a pressing concern due to potential vulnerabilities, such as data poisoning and model inversion attacks. This study aims to enhance the security of federated learning systems for website threat intelligence

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Sleem, Ahmed, and Ibrahim Elhenawy. "Enhancing Cyber Threat Intelligence Sharing through a Privacy-Preserving Federated Learning Approach." Journal of Cybersecurity and Information Management 9, no. 2 (2022): 51–59. http://dx.doi.org/10.54216/jcim.090205.

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This paper proposes a privacy-preserving federated learning approach to enhance cyber threat intelligence sharing. Cyber threats are becoming more sophisticated and are posing serious security risks to organizations. Sharing threat intelligence information can help to detect and mitigate these threats quickly. However, privacy concerns and data protection regulations hinder the sharing of sensitive information. Federated learning is a promising approach that allows multiple parties to collaborate in building a global model while preserving data privacy. We propose a framework that utilizes fed

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Sridevi Kakolu, Muhammad Ashraf Faheem, and Muhammad Aslam. "Privacy-preserving AI for cybersecurity: Balancing threat intelligence collection with user data protection." International Journal of Science and Research Archive 2, no. 2 (2021): 280–92. http://dx.doi.org/10.30574/ijsra.2021.2.2.0071.

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This paper explores the case of using privacy-preserving artificial intelligence in cybersecurity by analyzing the importance of effective threat intelligence in the conflict with potential invasions and high user data protection standards. With the increased articulation of cyber threats, AI is crucial in fortifying detection, reaction, and prevention measures for cyber threats in CSFs. However, such large-scale information feeding these systems raises many privacy issues, and hence, strong privacy preservation mechanisms that ensure user anonymity and protect the information from misuse are

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Oyekan, Basirat. "DEVELOPING PRIVACY-PRESERVING FEDERATED LEARNING MODELS FOR COLLABORATIVE HEALTH DATA ANALYSIS ACROSS MULTIPLE INSTITUTIONS WITHOUT COMPROMISING DATA SECURITY." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 3, no. 3 (2024): 139–64. http://dx.doi.org/10.60087/jklst.vol3.n3.p139-164.

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Federated learning is an emerging distributed machine learning technique that enables collaborative training of models among devices and servers without exchanging private data. However, several privacy and security risks associated with federated learning need to be addressed for safe adoption. This review provides a comprehensive analysis of the key threats in federated learning and the mitigation strategies used to overcome these threats. Some of the major threats identified include model inversion, membership inference, data attribute inference and model extraction attacks. Model inversion

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12

Nahmias, Yifat. "Privacy Preserving Social Norm Nudges." Michigan Technology Law Review, no. 26.1 (2019): 43. http://dx.doi.org/10.36645/mtlr.26.1.privacy.

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Nudges comprise a key component of the regulatory toolbox. Both the public and private sectors use nudges extensively in various domains, ranging from environmental regulation to health, food and financial regulation. This article focuses on a particular type of nudge: social norm nudges. It discusses, for the first time, the privacy risks of such nudges. Social norm nudges induce behavioral change by capitalizing on people’s desire to fit in with others, on their predisposition to social conformity, and on their susceptibility to the way information is framed. In order to design effective soc

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Van De Graaf, Jeroen. "Long-Term Threats to Ballot Privacy." IEEE Security & Privacy 15, no. 3 (2017): 40–47. http://dx.doi.org/10.1109/msp.2017.77.

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14

Hu, Teng, Bangzhou Xin, Xiaolei Liu, Ting Chen, Kangyi Ding, and Xiaosong Zhang. "Tracking the Insider Attacker: A Blockchain Traceability System for Insider Threats." Sensors 20, no. 18 (2020): 5297. http://dx.doi.org/10.3390/s20185297.

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The insider threats have always been one of the most severe challenges to cybersecurity. It can lead to the destruction of the organisation’s internal network system and information leakage, which seriously threaten the confidentiality, integrity and availability of data. To make matters worse, since the attacker has authorized access to the internal network, they can launch the attack from the inside and erase their attack trace, which makes it challenging to track and forensics. A blockchain traceability system for insider threats is proposed in this paper to mitigate the issue. First, this

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Thompson, London, and Csilla Farkas. "PRIVACY AND SECURITY FOR TELEHEALTH DEVICES." Innovation in Aging 3, Supplement_1 (2019): S836. http://dx.doi.org/10.1093/geroni/igz038.3081.

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Abstract In this research, we study the privacy and security capabilities provided by telehealth devices. Our aim is to evaluate how vulnerable these popular devices are in the presence of malicious cyber attackers. As older adults increasingly rely on telehealth devices, it is crucial that cybersecurity aspects of these devices are clearly communicated to them. Moreover, older adults frequently lack the technical expertise to evaluate the security and privacy capabilities of the devices. The lack of control over telehealth devices is a major concern for older adults. Older adults view certain

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Mavriki, Paola, and Maria Karyda. "Automated data-driven profiling: threats for group privacy." Information & Computer Security 28, no. 2 (2019): 183–97. http://dx.doi.org/10.1108/ics-04-2019-0048.

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Purpose User profiling with big data raises significant issues regarding privacy. Privacy studies typically focus on individual privacy; however, in the era of big data analytics, users are also targeted as members of specific groups, thus challenging their collective privacy with unidentified implications. Overall, this paper aims to argue that in the age of big data, there is a need to consider the collective aspects of privacy as well and to develop new ways of calculating privacy risks and identify privacy threats that emerge. Design/methodology/approach Focusing on a collective level, the

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Wang, Zhaohui, Bo Luo, and Fengjun Li. "PrivacyGuard: Exploring Hidden Cross-App Privacy Leakage Threats In IoT Apps." Proceedings on Privacy Enhancing Technologies 2025, no. 1 (2025): 776–91. http://dx.doi.org/10.56553/popets-2025-0040.

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The increasing use of the Internet of Things (IoT) technology has made our lives convenient, however, it also poses new security and privacy threats. In this work, we study a new type of privacy threat enabled by cross-app chains built among multiple seemingly benign IoT apps. We find that interactions among apps could leak privacy-sensitive information, e.g., users' identification, location and tracking, activity patterns, etc. To tackle this challenge, we introduce PrivacyGuard, which extracts cross-app chains in the form of trigger-condition-action rules and identifies the corresponding pri

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Patil, Chetan, and Zuber. "A Reinvent Survey on Machine Learning Attacks." E3S Web of Conferences 453 (2023): 01016. http://dx.doi.org/10.1051/e3sconf/202345301016.

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The increasing prevalence of machine learning technology highlights the urgent need to delve into its insinuations for safety and confidentiality. While inquiry on the safety aspects of mechanism knowledge has garnered considerable attention, privacy considerations have often taken a backseat, although recent years have seen a significant upswing in privacy-focused research. In an effort to contribute to this growing field, we conducted an analysis encompassing more than 40 articles addressing privacy threats in the context of mechanism knowledge, published ended the historical seven centuries

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Gonzalez-Granadillo, Gustavo, Sofia Anna Menesidou, Dimitrios Papamartzivanos, et al. "Automated Cyber and Privacy Risk Management Toolkit." Sensors 21, no. 16 (2021): 5493. http://dx.doi.org/10.3390/s21165493.

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Addressing cyber and privacy risks has never been more critical for organisations. While a number of risk assessment methodologies and software tools are available, it is most often the case that one must, at least, integrate them into a holistic approach that combines several appropriate risk sources as input to risk mitigation tools. In addition, cyber risk assessment primarily investigates cyber risks as the consequence of vulnerabilities and threats that threaten assets of the investigated infrastructure. In fact, cyber risk assessment is decoupled from privacy impact assessment, which aim

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DeHart, Jasmine, Makya Stell, and Christan Grant. "Social Media and the Scourge of Visual Privacy." Information 11, no. 2 (2020): 57. http://dx.doi.org/10.3390/info11020057.

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Online privacy has become immensely important with the growth of technology and the expansion of communication. Social Media Networks have risen to the forefront of current communication trends. With the current trends in social media, the question now becomes how can we actively protect ourselves on these platforms? Users of social media networks share billions of images a day. Whether intentional or unintentional, users tend to share private information within these images. In this study, we investigate (1) the users’ perspective of privacy, (2) pervasiveness of privacy leaks on Twitter, and

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Dopierała, Renata. "Społeczne wyobrażenia prywatności." Kultura i Społeczeństwo 50, no. 1-2 (2006): 307–19. http://dx.doi.org/10.35757/kis.2006.50.1-2.14.

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In the paper author considers social representations of privacy based on empirical research — The Unfinished Sentences Test. The main goals of the text are to begin defining such terms as: private life and private sphere, and to discuss functions of privacy, how it is experienced and its role in individual and social life. It also deals with the threats to privacy in contemporary societies.

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Vlachos, Vasileios, Yannis C. Stamatiou, and Sotiris Nikoletseas. "The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation." Journal of Cybersecurity and Privacy 3, no. 1 (2023): 26–43. http://dx.doi.org/10.3390/jcp3010003.

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Instilling good privacy practices to developers and users appears to be a difficult and daunting task. The World Wide Web encompasses a panspermia of different technologies, commercial and open source apis, evolving security standards and protocols that can be deployed towards the implementation of complex, powerful, web applications. At the same time, the proliferation of applications and services on all types of devices has also increased the attack surface for privacy threats. In this paper, we present the Privacy Flag Observatory, a platform which is one of the main tools produced by the P

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Sheik, Al Tariq, Carsten Maple, Gregory Epiphaniou, and Mehrdad Dianati. "A Comprehensive Survey of Threats in Platooning—A Cloud-Assisted Connected and Autonomous Vehicle Application." Information 15, no. 1 (2023): 14. http://dx.doi.org/10.3390/info15010014.

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Cloud-Assisted Connected and Autonomous Vehicles (CCAV) are set to revolutionise road safety, providing substantial societal and economic advantages. However, with the evolution of CCAV technology, security and privacy threats have increased. Although several studies have been published around the threat and risk estimation aspects of CCAV, limited research exists on the security implications and emerging threat landscapes in the CCAV platooning application. We conducted an extensive review and categorisation of real-world security incidents and created an account of 132 threats from scholarly

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Noreen Khalid and Shafaat Ahmed Bazaz. "ENHANCING IOT SECURITY: THREAT MODEL-BASED ANALYSIS IN THE WEB OF THINGS ECOSYSTEM." Kashf Journal of Multidisciplinary Research 2, no. 02 (2025): 190–206. https://doi.org/10.71146/kjmr287.

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The Internet of things using collaborative technologies such as 5G, cloud, artificial intelligence, analytics, and automation enables people and objects/devices to communicate with each other at anytime and anywhere using the internet. The aim of this study is to understand the phenomenon of the Internet of Things and identify security, privacy, and trust threats associated with it. For this, we used a renowned Microsoft threat model such as STRIDE and a threat analysis tool like DREAD to examine the security threats through IoT Soil moisture system and this application is deployed on an IoT d

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Appelbaum, Paul S. "Privacy in Psychiatric Treatment: Threats and Responses." FOCUS 1, no. 4 (2003): 396–406. http://dx.doi.org/10.1176/foc.1.4.396.

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Abawajy, J. H. "Security and privacy threats to volunteer computing." International Journal of Critical Infrastructures 4, no. 3 (2008): 245. http://dx.doi.org/10.1504/ijcis.2008.017439.

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Appelbaum, Paul S. "Privacy in Psychiatric Treatment: Threats and Responses." American Journal of Psychiatry 159, no. 11 (2002): 1809–18. http://dx.doi.org/10.1176/appi.ajp.159.11.1809.

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28

Al-Rubaie, Mohammad, and J. Morris Chang. "Privacy-Preserving Machine Learning: Threats and Solutions." IEEE Security & Privacy 17, no. 2 (2019): 49–58. http://dx.doi.org/10.1109/msec.2018.2888775.

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Huesch, Marco D. "Privacy Threats When Seeking Online Health Information." JAMA Internal Medicine 173, no. 19 (2013): 1838. http://dx.doi.org/10.1001/jamainternmed.2013.7795.

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John, Lizy Kurian. "Hardware Security and Privacy: Threats and Opportunities." IEEE Micro 43, no. 5 (2023): 4–5. http://dx.doi.org/10.1109/mm.2023.3304091.

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Kopczewski, Marian, Zbigniew Ciekanowski, Julia Nowicka, and Katarzyna Bakalarczyk-Burakowska. "Security threats in cyberspace." Scientific Journal of the Military University of Land Forces 205, no. 3 (2022): 415–26. http://dx.doi.org/10.5604/01.3001.0016.0040.

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The article is an attempt to highlight the main types of security threats in cyberspace. As literature provides a multitude of different approaches, standards, methodologies, and proposals for the classification of threats, the article focuses on threats to privacy and national ICT security. Cyberspace is subject to increasingly sophisticated and targeted threats, while our growing reliance on cyberspace exposes our privacy to risks, giving rise to new and significant security gaps. Due to its specific characteristics, it generates serious threats to individuals as well as to national and inte

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Matzner, Tobias. "Why privacy is not enough privacy in the context of “ubiquitous computing” and “big data”." Journal of Information, Communication and Ethics in Society 12, no. 2 (2014): 93–106. http://dx.doi.org/10.1108/jices-08-2013-0030.

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Purpose – Ubiquitous computing and “big data” have been widely recognized as requiring new concepts of privacy and new mechanisms to protect it. While improved concepts of privacy have been suggested, the paper aims to argue that people acting in full conformity to those privacy norms still can infringe the privacy of others in the context of ubiquitous computing and “big data”. Design/methodology/approach – New threats to privacy are described. Helen Nissenbaum's concept of “privacy as contextual integrity” is reviewed concerning its capability to grasp these problems. The argument is based o

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Duan, Li, Kejia Zhang, Bo Cheng, and Bingfei Ren. "Privacy Threats of Acoustic Covert Communication among Smart Mobile Devices." Wireless Communications and Mobile Computing 2021 (July 5, 2021): 1–16. http://dx.doi.org/10.1155/2021/9179100.

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The emerging, overclocking signal-based acoustic covert communication technique allows smart devices to communicate (without users’ consent) utilizing their microphones and speakers in ultrasonic side channels, which offers users imperceptible and convenient personalized services, e.g., cross-device authentication and media tracking. However, microphones and speakers could be maliciously used and pose severe privacy threats to users. In this paper, we propose a novel high-frequency filtering- (HFF-) based protection model, named UltraFilter, which protects user privacy by enabling users to sel

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Asad, Muhammad, Ahmed Moustafa, and Chao Yu. "A Critical Evaluation of Privacy and Security Threats in Federated Learning." Sensors 20, no. 24 (2020): 7182. http://dx.doi.org/10.3390/s20247182.

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With the advent of smart devices, smartphones, and smart everything, the Internet of Things (IoT) has emerged with an incredible impact on the industries and human life. The IoT consists of millions of clients that exchange massive amounts of critical data, which results in high privacy risks when processed by a centralized cloud server. Motivated by this privacy concern, a new machine learning paradigm has emerged, namely Federated Learning (FL). Specifically, FL allows for each client to train a learning model locally and performs global model aggregation at the centralized cloud server in o

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Molleti, Ramasankar. "DESIGNING A SECURE N-TIER ARCHITECTURE FOR CLOUD ENVIRONMENTS." International Scientific Journal of Engineering and Management 04, no. 01 (2025): 1–7. https://doi.org/10.55041/isjem00113.

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The study of building secure N-Tier architectures in cloud environments is crucial to the issue in today’s distributed systems. It looks at the development of N-Tier architectures in cloud computing and outlines the main security components of each tier. The study provides detailed solutions for addressing the main security threats such as data privacy, access control, and threat identification. It also covers aspects such as design and implementation, emphasizing depth. The study discusses future works and directions which include zero-trust security systems, and AI security in the future. Th

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Khandare, Miss Komal K. "Various Privacy and Security Issues in Online Social Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 142–46. http://dx.doi.org/10.22214/ijraset.2021.39203.

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Abstract: Social networks have become a part of human life. online interaction, communication, and interest sharing, letting individuals create online profiles that other users can view these are basic features that are offer by most of social networking sites Unfortunately, In many cases, users are not even aware of the disclosure of their personal information through their profiles. Leakage of a user’s private information can happen in different ways. Many of the security risks associated with using social media are presented in this paper. Also, the issue of privacy and how it relates to se

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Riyana, Surapon, Nobutaka Ito, Tatsanee Chaiya, et al. "Privacy Threats and Privacy Preservation Techniques for Farmer Data Collections Based on Data Shuffling." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 3 (2022): 289–301. http://dx.doi.org/10.37936/ecti-cit.2022163.246469.

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Aside from smart technologies, farm data collection is also important for smart farms including farm environment data collection and farmer survey data collection. With farm data collection, we observe that it is generally proposed to utilize in smart farm systems. However, it can also be released for use in the outside scope of the data collecting organization for an appropriate business reason such as improving the smart farm system, product quality, and customer service. Moreover, we can observe that the farmer survey data collection often includes sensitive data, the private data of farmer

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Li, Qingtie, Xuemei Wang, and Shougang Ren. "A Privacy Robust Aggregation Method Based on Federated Learning in the IoT." Electronics 12, no. 13 (2023): 2951. http://dx.doi.org/10.3390/electronics12132951.

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Federated learning has been widely applied because it enables a large number of IoT devices to conduct collaborative training while maintaining private data localization. However, the security risks and threats faced by federated learning in IoT applications are becoming increasingly prominent. Except for direct data leakage, there is also a need to face threats that attackers interpret gradients and infer private information. This paper proposes a Privacy Robust Aggregation Based on Federated Learning (PBA), which can be applied to multiple server scenarios. PBA filters outliers by using the

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Vassilakis, Costas. "Blockchain technologies for leveraging security and privacy." Homo Virtualis 2, no. 1 (2019): 7. http://dx.doi.org/10.12681/homvir.20188.

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The contemporary internet has developed into a complex ecosystem involving humans, services, applications, machines and applications that interact exchanging information, ranging from e-mail messages and social media content to crowdsourcing data and videoconferencing. In this context, a number of security threats such as viruses and malware exist, while additionally the users’ privacy is jeopardized by threats such as personal data leakage, usage pattern monitoring, and so forth. The IoT trend renders the Internet ecosystem even more complex, by adding a rich set of services, applications and

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Guo, Yunyong, Bryan Guo, and Nathan Guo. "Advancing security and privacy measures in telehealth IoT/Fog/Cloud ecosystems." Journal of Applied Biotechnology & Bioengineering 11, no. 3 (2024): 77–87. http://dx.doi.org/10.15406/jabb.2024.11.00364.

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Background: As Telehealth becomes integral to modern healthcare, ensuring the security and privacy of patient data in remote monitoring scenarios is paramount. This paper presents an advanced security and privacy model designed to safeguard Telehealth systems, addressing the evolving threats in the interconnected landscape of IoT, Fog, and Cloud. Purpose: The purpose of this research is to evaluate the effectiveness of the proposed security and privacy model in real-world Telehealth scenarios through a comprehensive simulation study. The model integrates encryption, key management, intrusion d

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P. Ananthi and A. Chandrabose. "The socio-technical opportunities and threats of crowdsensing." Scientific Temper 15, spl-1 (2024): 291–97. https://doi.org/10.58414/scientifictemper.2024.15.spl.34.

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The exponential growth of mobile crowd sensing (MCS) has provided unparalleled opportunities to collect large-scale data through a network of mobile devices, empowering diverse applications in smart cities, healthcare, and environmental monitoring. However, the inherently participatory nature of MCS raises critical privacy concerns, as sensitive user information is often at risk of exposure. This literature review examines recent advancements in employing machine learning techniques to enhance privacy preservation in MCS frameworks. It explores methods such as federated learning, differential

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Deepak, Kumar Malviya, and Kumar Lilhore Umesh. "Survey on Security Threats in Cloud Computing." International Journal of Trend in Scientific Research and Development 3, no. 1 (2018): 1222–26. https://doi.org/10.31142/ijtsrd19172.

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Cloud computing is a new and innovative technology, which serves resources as a service to cloud user. Cloud computing technology provides optimum utilization of computing resources in less cost, which attract organization to be part of cloud customer market. Day by day cloud users are getting increases. Cloud serves IaaS, PaaS, SaaS and DaaS as a service on various private public, community and hybrid cloud levels. On cloud data is stores over the network and this network is shared and access by various cloud users, so data security and privacy are key concerns. Due to shared architecture of

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Amanul Islam, Mohammad. "Privacy Risks and Security Threat Strategy to Optimize the Vulnerability in Health Information System (HIS)." International Journal of Advanced Networking and Applications 14, no. 01 (2022): 5271–76. http://dx.doi.org/10.35444/ijana.2022.14106.

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Health information systems (HIS) generally access, process, or maintain large volumes of sensitive data corresponding to the patient, health service provider (HSP), clinicians, and other stakeholders. Hence, ensuring health data security is a primary concern to improve patient outcomes, inform research, and influence policymaking. Many countries across the world practice data protection and privacy preservation laws against numerous threats related to health data and its communication between the computer networks. Hence, resolving the health information security flaws by designing a potential

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Singhal, Mohit, Nihal Kumarswamy, Shreyasi Kinhekar, and Shirin Nilizadeh. "Cybersecurity Misinformation Detection on Social Media: Case Studies on Phishing Reports and Zoom’s Threat." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 796–807. http://dx.doi.org/10.1609/icwsm.v17i1.22189.

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Prior work has extensively studied misinformation related to news, politics, and health, however, misinformation can also be about technological topics. While less controversial, such misinformation can severely impact companies’ reputations and revenues, and users’ online experiences. Recently, social media has also been increasingly used as a novel source of knowledgebase for extracting timely and relevant security threats, which are fed to the threat intelligence systems for better performance. However, with possible campaigns spreading false security threats, these systems can become vulne

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Shetty, Saiman, and Harsh Jaiswal. "Cybersecurity Threats and Solutions in Autonomous Vehicles." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem37841.

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Abstract—The integration of autonomous vehicles into modern transportation systems introduces both transformative potential and significant cybersecurity challenges. This paper examines the critical importance of cybersecurity in autonomous vehicles, highlighting key threats such as remote hacking, sensor spoofing, and data breaches that jeopardize vehicle safety and user privacy. Utilizing a mixed-methods approach, the study combines quantitative data analysis with qualitative case studies to thoroughly assess these vulnerabilities. Additionally, it evaluates a range of solutions, including a

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Pendergrass, John C., Karen Heart, C. Ranganathan, and V. N. Venkatakrishnan. "A Threat Table Based Assessment of Information Security in Telemedicine." International Journal of Healthcare Information Systems and Informatics 9, no. 4 (2014): 20–31. http://dx.doi.org/10.4018/ijhisi.2014100102.

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Information security within healthcare is paramount and telemedicine applications present unique security challenges. Technology is giving rise to new and advanced telemedicine applications and understanding the security threats to these applications is needed to ensure, among other things, the privacy of patient information. This paper proposes a threat table approach to assess security threats pertaining to telemedicine applications. The concept and its usefulness are illustrated using a case study. This case study focuses on the capture and representation of salient security threats in tele

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Odeh, Ammar, Eman Abdelfattah, and Walid Salameh. "Privacy-Preserving Data Sharing in Telehealth Services." Applied Sciences 14, no. 23 (2024): 10808. http://dx.doi.org/10.3390/app142310808.

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In today’s healthcare industry, safeguarding patient data is critical due to the increasing digitization of medical records, which makes them vulnerable to cyber threats. Telehealth services, while providing immense benefits in terms of accessibility and efficiency, introduce complex challenges in maintaining data privacy and security. This paper proposes a privacy-preserving framework for secure data sharing within telehealth services, employing blockchain technology and advanced cryptographic techniques. The framework ensures that all patient health data are encrypted using homomorphic encry

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Fu, Zijun. "Adolescent Data Privacy Protection and Threats in the Digital Age." Applied and Computational Engineering 118, no. 1 (2025): 171–77. https://doi.org/10.54254/2755-2721/2025.20936.

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In the current era of rapid development of information technology and the widespread use of the internet by adolescents, data privacy issues have become increasingly prominent. This paper centers on exploring the challenges adolescents face regarding data privacy in the digital age, highlighting the importance and main threats of protecting adolescent data privacy. Using both qualitative and quantitative research methods, the paper analyzes cases and current situations of adolescent privacy breaches. The main research question focuses on the primary threats to adolescent data privacy in the on

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Madanian, Samaneh, Tserendorj Chinbat, Maduka Subasinghage, David Airehrour, Farkhondeh Hassandoust, and Sira Yongchareon. "Health IoT Threats: Survey of Risks and Vulnerabilities." Future Internet 16, no. 11 (2024): 389. http://dx.doi.org/10.3390/fi16110389.

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The secure and efficient collection of patients’ vital information is a challenge faced by the healthcare industry. Through the adoption and application of Internet of Things (IoT), the healthcare industry has seen an improvement in the quality of delivered services and patient safety. However, IoT utilization in healthcare is challenging due to the sensitive nature of patients’ clinical information and communicating this across heterogeneous networks and among IoT devices. We conducted a semi-systematic literature review to provide an overview of IoT security and privacy challenges in the hea

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Srikanth, Kandragula. "Security and Privacy Challenges." Journal of Scientific and Engineering Research 6, no. 10 (2019): 315–17. https://doi.org/10.5281/zenodo.14050056.

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Cloud computing has revolutionized the way businesses operate, offering a paradigm shift from on-premise infrastructure to a virtualized, on-demand environment. However, security and privacy concerns remain a significant hurdle for some businesses considering cloud adoption. This white paper delves into the key challenges associated with cloud security and privacy, empowering businesses of all sizes to make informed decisions and navigate the potential risks involved. We begin by exploring the concept of data security threats in the cloud environment. Data breaches are a major concern, as clou

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