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Artykuły w czasopismach na temat „Healthcare Privacy”

Autor: Grafiati

Data publikacji: 7 czerwca 2025

Data aktualizacji: 2 sierpnia 2025

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1

Iyengar, Arun, Ashish Kundu, and George Pallis. "Healthcare Informatics and Privacy." IEEE Internet Computing 22, no. 2 (2018): 29–31. http://dx.doi.org/10.1109/mic.2018.022021660.

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Lewitz, Joel A. "Speech privacy for healthcare." Journal of the Acoustical Society of America 116, no. 4 (2004): 2612. http://dx.doi.org/10.1121/1.4785417.

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Marak, Trealindora, Labianglang Sohkhlet, and Upasana Das. "BLOCKCHAIN-BASED HEALTHCARE RECORD MANAGEMENT SYSTEM." International Journal of Engineering Applied Sciences and Technology 6, no. 9 (2022): 288–95. http://dx.doi.org/10.33564/ijeast.2022.v06i09.042.

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Blockchain is an engrossing research field area to introduce in the healthcare sector due to its security, privacy, confidentiality and decentralization. In Blockchain-based systems, data and authority can be distributed, and transparent and reliable transaction ledgers created. Privacy-enabling approaches for Blockchain have been introduced, such as private blockchains, and methods for enabling parties to act pseudonymously. We explore a set of proposed uses of Blockchain within cyber security and consider their requirements for privacy. We compare these requirements with the privacy provisio

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Alalouch, Chaham, Peter A. Aspinall, and Harry Smith. "Design Criteria for Privacy-Sensitive Healthcare Buildings." International Journal of Engineering and Technology 8, no. 1 (2016): 32–39. http://dx.doi.org/10.7763/ijet.2016.v6.854.

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Alalouch, Chaham, Peter A. Aspinall, and Harry Smith. "Design Criteria for Privacy-Sensitive Healthcare Buildings." International Journal of Engineering and Technology 8, no. 1 (2016): 32–39. http://dx.doi.org/10.7763/ijet.2016.v8.854.

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Abdul Manap, Nazura, Mohamad Rizal Abd Rahman, and Siti Nur Farah Atiqah Salleh. "HEALTH DATA OWNERSHIP IN MALAYSIA PUBLIC AND PRIVATE HEALTHCARE: A LEGAL ANALYSIS OF HEALTH DATA PRIVACY IN THE AGE OF BIG DATA." International Journal of Law, Government and Communication 7, no. 30 (2022): 33–41. http://dx.doi.org/10.35631/ijlgc.730004.

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Health data ownership in big data is a new legal issue. The problem stands between the public and private healthcare as the main proprietor of health data. In Malaysia, health data ownership is under government hospitals and private healthcare jurisdictions. Who owns the data will be responsible for safeguarding it, including its privacy. Various technical methods are applied to protect health data, such as aggregation and anonymization. The thing is, do these technical methods are still reliable to safeguard privacy in big data? In terms of legal protection, private healthcare is governed und

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Pasupuleti, Murali Krishna. "Privacy-Preserving Data Sharing Using Differential Privacy in Healthcare." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 06 (2025): 386–98. https://doi.org/10.62311/nesx/rphcrcscrcp2.

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Abstract: This study evaluates the effectiveness of differential privacy (DP) techniques for privacy-preserving data sharing in healthcare environments. Healthcare data is highly sensitive, making data sharing both a valuable and risky endeavor. Traditional anonymization techniques fail to provide sufficient guarantees against re-identification attacks, particularly in high-dimensional datasets. Differential privacy offers mathematically rigorous privacy guarantees while enabling data utility. Using real-world healthcare datasets, the proposed approach evaluates the trade-off between privacy b

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Tocci, Gregory C., and David Sykes. "Speech privacy in healthcare facilities." Journal of the Acoustical Society of America 122, no. 5 (2007): 3027. http://dx.doi.org/10.1121/1.2942825.

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D Kavitha. "Preserving Privacy of IoT Healthcare Data using Differential Privacy and LSTM." Journal of Electrical Systems 20, no. 7s (2024): 2483–92. http://dx.doi.org/10.52783/jes.4071.

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The Internet of Things (IoT) is a powerful technology creating revolutions in multiple industries for ex: Traffic and Healthcare domains. The patient data collected by continuous monitoring using IoT will support in treating the patients and make a positive impact on patients' well-being and increase the efficiency of healthcare workers. It is crucial to be aware of certain drawbacks and risks associated with protecting the privacy of the patient data which is one of the major problems being faced in the healthcare domain. Harmful individuals/Agencies will use IoT devices to obtain private dat

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D. Rajalakshmi, Et al. "HealthBlock: A Blockchain-IoT Fusion for Secure Healthcare Data Exchange." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2018–23. http://dx.doi.org/10.17762/ijritcc.v11i9.9199.

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Managing healthcare data while ensuring its security and privacy is critical to providing quality care to patients. However, traditional approaches to healthcare data sharing have limitations, including the risk of data breaches and the lack of privacy-preserving mechanisms. This research paper proposes a novel hybrid blockchain-IoT approach for privacy-preserving healthcare data sharing that addresses these challenges. Our system incorporates a private blockchain for protected and tamper-proof data sharing, with privacy-preserving techniques such as differential privacy and homomorphic encryp

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Swapna, Nadakuditi. "Is Block Chain Secure for Healthcare Interoperability?" Journal of Scientific and Engineering Research 7, no. 8 (2020): 211–14. https://doi.org/10.5281/zenodo.11210427.

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With the exponential growth in healthcare data, there is a greater need for an elevated level of security and privacy. Privacy means having the correct rights to allow or disclose personal information to others. Privacy helps to determine how access to personal patient information is controlled. Healthcare security on the other hand is extremely important to healthcare providers to help safeguard the privacy of patient’s health information. This includes managing access control of patient information, the security of patient data from unauthorized users, and the modification and destruct

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Pika, Anastasiia, Moe T. Wynn, Stephanus Budiono, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, and Hajo A. Reijers. "Privacy-Preserving Process Mining in Healthcare." International Journal of Environmental Research and Public Health 17, no. 5 (2020): 1612. http://dx.doi.org/10.3390/ijerph17051612.

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Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in t

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Bhavani Sankar Telaprolu. "Privacy-Preserving Federated Learning in Healthcare - A Secure AI Framework." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 703–7. https://doi.org/10.32628/cseit2410347.

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Federated Learning (FL) has transformed AI applications in healthcare by enabling collaborative model training across multiple institutions while preserving patient data privacy. Despite its advantages, FL remains susceptible to security vulnerabilities, including model inversion attacks, adversarial data poisoning, and communication inefficiencies, necessitating enhanced privacy-preserving mechanisms. In response, this study introduces Privacy-Preserving Federated Learning (PPFL), an advanced FL framework integrating Secure Multi-Party Computation (SMPC), Differential Privacy (DP), and Homomo

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14

Chong, Kah Meng. "Privacy-preserving healthcare informatics: a review." ITM Web of Conferences 36 (2021): 04005. http://dx.doi.org/10.1051/itmconf/20213604005.

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Electronic Health Record (EHR) is the key to an efficient healthcare service delivery system. The publication of healthcare data is highly beneficial to healthcare industries and government institutions to support a variety of medical and census research. However, healthcare data contains sensitive information of patients and the publication of such data could lead to unintended privacy disclosures. In this paper, we present a comprehensive survey of the state-of-the-art privacy-enhancing methods that ensure a secure healthcare data sharing environment. We focus on the recently proposed scheme

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Pramod, B. Deshmukh, N. Wani Nilesh, S. Malphedwar Laxmikant, and A. Ghanwat Deepali. "STUDY OF A SECURE AND PRIVACY-PRESERVING OPPORTUNISTIC COMPUTING FRAMEWORK FOR MOBILE-HEALTHCARE EMERGENCY." International Journal of Multidisciplinary Research and Modern Education 2, no. 2 (2016): 397–405. https://doi.org/10.5281/zenodo.192369.

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<em>With the pervasiveness of smart phones and the advance of wireless body sensor networks (BSNs), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider into a pervasive environment for better health monitoring, has attracted considerable interest recently. However, the flourish of m-Healthcare still faces many challenges including information security and privacy preservation. In this paper, we propose a secure and privacy-preserving opportunistic computing framework, called SPOC, for m-Healthcare emergency. With SPOC, smart phone resources including computing

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Sohail, Syeda Amna, Faiza Allah Bukhsh, and Maurice van Keulen. "Multilevel Privacy Assurance Evaluation of Healthcare Metadata." Applied Sciences 11, no. 22 (2021): 10686. http://dx.doi.org/10.3390/app112210686.

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Healthcare providers are legally bound to ensure the privacy preservation of healthcare metadata. Usually, privacy concerning research focuses on providing technical and inter-/intra-organizational solutions in a fragmented manner. In this wake, an overarching evaluation of the fundamental (technical, organizational, and third-party) privacy-preserving measures in healthcare metadata handling is missing. Thus, this research work provides a multilevel privacy assurance evaluation of privacy-preserving measures of the Dutch healthcare metadata landscape. The normative and empirical evaluation co

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17

Satheesh, Reddy Gopireddy. "Privacy-Aware Federated Data Sharing Models for Healthcare Cloud Systems." Journal of Scientific and Engineering Research 6, no. 12 (2019): 324–27. https://doi.org/10.5281/zenodo.14096825.

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With the rapid adoption of cloud technologies in healthcare, data sharing has become essential for advancing research, diagnostics, and patient outcomes. However, sharing sensitive health information across organizations introduces significant privacy risks, requiring strict adherence to regulations such as HIPAA and GDPR. Federated learning offers a solution by enabling data sharing without centralized data aggregation, thus preserving privacy. This paper investigates privacy-aware federated data sharing models tailored for healthcare cloud systems, highlighting their benefits, challenges, an

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George, Jomin, and Takura Bhila. "Security, Confidentiality and Privacy in Health of Healthcare Data." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 373–77. http://dx.doi.org/10.31142/ijtsrd23780.

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Meksi, Andia, Enkelejda Shkurti, and Bardhyl Çipi. "CONFIDENTIALITY AND PRIVACY IN ALBANIAN HEALTHCARE." International Journal of Ecosystems and Ecology Science (IJEES) 11, no. 4 (2021): 911–18. http://dx.doi.org/10.31407/ijees11.431.

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Kelly, Eileen P., and Fahri Unsal. "Health information privacy and e-healthcare." International Journal of Healthcare Technology and Management 4, no. 1/2 (2002): 41. http://dx.doi.org/10.1504/ijhtm.2002.001128.

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Roy, Kenneth P., and Kenneth W. Good. "Measurements of speech privacy in healthcare." Journal of the Acoustical Society of America 121, no. 5 (2007): 3036. http://dx.doi.org/10.1121/1.4781690.

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Mundy, Darren P. "Customer privacy on UK healthcare websites." Medical Informatics and the Internet in Medicine 31, no. 3 (2006): 175–93. http://dx.doi.org/10.1080/14639230600804820.

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Hu, Long, Yongfeng Qian, Jing Chen, Xiaobo Shi, Jing Zhang, and Shiwen Mao. "Photo Crowdsourcing Based Privacy-Protected Healthcare." IEEE Transactions on Sustainable Computing 4, no. 2 (2019): 168–77. http://dx.doi.org/10.1109/tsusc.2017.2705181.

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Mukesh, Soni1 *. Yashkumar Barot2 and S. Gomathi3. "Privacy in Preprocessing of Healthcare Data." Journal of Cybersecurity and Information Management (JCIM) 7, no. 1 (2021): 13–25. https://doi.org/10.5281/zenodo.5171447.

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Churi, Prathamesh, Ambika Pawar, and Antonio-José Moreno-Guerrero. "A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study." Inventions 6, no. 3 (2021): 45. http://dx.doi.org/10.3390/inventions6030045.

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Background: According to the renowned and Oscar award-winning American actor and film director Marlon Brando, “privacy is not something that I am merely entitled to, it is an absolute prerequisite.” Privacy threats and data breaches occur daily, and countries are mitigating the consequences caused by privacy and data breaches. The Indian healthcare industry is one of the largest and rapidly developing industry. Overall, healthcare management is changing from disease-centric into patient-centric systems. Healthcare data analysis also plays a crucial role in healthcare management, and the privac

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P, Arul, and Renuka S. "Securing Healthcare Data in Blockchain Using TSE Algorithm." Indian Journal of Science and Technology 16, no. 43 (2023): 3942–47. https://doi.org/10.17485/IJST/v16i43.1815.

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Abstract <strong>Objective:</strong> The main objective of this work is to create a novel patient-centered management system that uses the two-stage encryption TSE algorithm to secure a private blockchain-based healthcare system while providing users with the highest privacy, complete control, and security over their sensitive data. <strong>Methods:</strong> TSE Encryption Process: The user or the patient asymmetrically encrypts the data's symmetric key using the public key of the party with whom the data is to be shared. TSE Decryption Process: The party wishing to access the h

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Mishra, Abhishek. "Privacy-Preserving Data Sharing Platform." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32225.

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In today's data-driven healthcare landscape, the secure sharing of sensitive medical information is essential for improving patient care, facilitating medical research, and advancing healthcare outcomes. However, ensuring the integrity, confidentiality, and privacy of patient data poses significant challenges, particularly in the context of big data environments. This presents a comprehensive framework for privacy-preserving data sharing in healthcare, leveraging a combination of cryptographic techniques, encryption, and secure computation protocols. The framework encompasses various privacy-p

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Clark, Mason. "Consumer Privacy and the Dobbs Disruption." University of Michigan Journal of Law Reform, no. 58.1 (2025): 1. https://doi.org/10.36646/mjlr.58.1.consumer.

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The right to reproductive privacy is under attack in the United States, and it is losing ground. Dobbs v. Jackson Women’s Health Organization, the Supreme Court’s 2022 decision that overruled Roe v. Wade’s constitutional protection of abortion and jeopardized privacy rights by proxy, reflects this losing posture. Scholarship in reproductive privacy varyingly critiques federal privacy initiatives, evaluates regulatory interventions, and proposes civil rights frameworks in response to Dobbs. This Article, however, pinpoints how Dobbs created a gaping hole in state consumer privacy laws even as t

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Eklöf, Niina, Hibag Abdulkarim, Maija Hupli, and Helena Leino-Kilpi. "Somali asylum seekers’ perceptions of privacy in healthcare." Nursing Ethics 23, no. 5 (2016): 535–46. http://dx.doi.org/10.1177/0969733015574927.

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Background: Privacy has been recognized as a basic human right and a part of quality of care. However, little is known about the privacy of Somali asylum seekers in healthcare, even though they are one of the largest asylum seeker groups in the world. Objectives: The aim of the study was to describe the content and importance of privacy and its importance in healthcare from the perspective of Somali asylum seekers. Research design: The data of this explorative qualitative study were collected by four focus group interviews with 18 Somali asylum seekers with the help of an interpreter. The data

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Florczak, Kristine L. "Privacy: An Elusive Concept." Nursing Science Quarterly 34, no. 2 (2021): 113. http://dx.doi.org/10.1177/0894318420987172.

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The subject of this Research Issues Column is privacy as it applies to healthcare. To that end, the notion of privacy from the perspective of the law is considered, followed by a consideration of its linkage to healthcare. This serves as an introduction to research on opinions of physicians and nurses in Turkey about the nature of privacy for the patients they serve.

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Alnasser, Manar, and Shancang Li. "Privacy-Enhancing Technologies in Collaborative Healthcare Analysis." Cryptography 9, no. 2 (2025): 24. https://doi.org/10.3390/cryptography9020024.

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Healthcare data is often fragmented across different institutions (hospitals, clinics, research centers), creating data silos. Privacy-enhancing technologies (PETs) play a fundamental role in collaborative healthcare analysis, enabling healthcare providers to improve care while protecting patient privacy. By providing a compliant framework for data sharing and research, PETs facilitate collaboration while adhering to stringent regulations like HIPAA and GDPR. This work conducts a comprehensive survey to investigate PETs in healthcare industry. It investigates the privacy requirements and chall

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Tentori, Mónica, Jesus Favela, and Victor González. "Quality of Privacy (QoP) for the Design of Ubiquitous Healthcare Applications." JUCS - Journal of Universal Computer Science 12, no. (3) (2006): 252–69. https://doi.org/10.3217/jucs-012-03-0252.

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Privacy is a complex social process that will persist in one form or another as a fundamental feature of the substrate into which ubiquitous computing (ubicomp) is threaded. Hospitals are natural candidates for the deployment of ubicomp technology while at the same time face significant privacy requirements. To better understand the privacy issues related to the use of ubicomp we place our efforts in understanding the contextual information relevant to privacy and how its interplay shapes the perception of privacy in a hospital. The results indicate that hospital workers tend to manage privacy

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Mahandule, Vikas, Achal Parab, Samruddhi Shelar, Sneha Parab, Sharayu Patil, and Harsha Patil. "PRIVACY PRESERVING DATA SHARING FRAMEWORK FOR HEALTHCARE IN IOT SYSTEMS." Journal of Trends and Challenges in Artificial Intelligence 1, no. 4 (2024): 143–48. http://dx.doi.org/10.61552/jai.2024.04.005.

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A healthcare data is very important for the purpose of organizing the data, analyzing the data etc. It is also important for Scientific Research, sharing of healthcare data has rapidly growing in Society. The healthcare data brings more privacy to the patient’s health data. Sharing that particular data without any permission can bring many threats to the patient’s privacy. Data collection and Preserving of data is the most sensitive part in the medical sector. Now a day’s many of the Organizations are focusing on the data privacy for healthcare. In this Research Paper we are going to introduce

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‏Aladwani, Sultan O. "Security & Privacy of Electronic Health Records." Journal of Medical Science And clinical Research 11, no. 06 (2023): 88–93. http://dx.doi.org/10.18535/jmscr/v11i6.17.

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Healthcare facilities like hospitals and clinics are adopting new technology at a dizzying pace. However, confidentiality concerns are frequently neglected, placing healthcare institutions at risk of cyber security problems, fines, reputational harm, and even catastrophic patient implications. Clinical documentation, patient profiles, lab findings, imaging findings, and diagnostic procedures make up Electronic Health Record (EHR) systems. EHRs are getting more and more complex with time, necessitating more and more data storage. In order to secure healthcare-based technologies and networks, ne

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Jee Jha, Kanhaiya, Gaurav Kumar Ameta, Esan P. Panchal, Keyurbhai A. Jani, Pramod Tripathi, and Shruti B. Yagnik. "Privacy- Enhanced Fungal Infection Detection: Leveraging Differential Privacy and Federated Learning in Healthcare System." Journal of Neonatal Surgery 14, no. 2 (2025): 142–53. https://doi.org/10.52783/jns.v14.1845.

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In the era of big data, safeguarding the privacy and security of sensitive healthcare information is predominant. This research paper investigates the integration of differential privacy and federated learning to create a robust framework for privacy-preserving analysis of fungal infection data. The proposed framework ensures the confidentiality of individual patient data while enabling collaborative analysis across multiple healthcare organizations. Differential privacy mechanisms are employed to provide strong privacy guarantees, ensuring that the inclusion of individual data does not compro

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Das, Sanchari, Kapil Madathil, Josiah Dykstra, et al. "Privacy and Security of Telehealth Services." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 1524–28. http://dx.doi.org/10.1177/1071181322661032.

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Telehealth technologies have aided in the distribution of health-related services, especially during the COVID-19 pandemic. With increased telehealth use, privacy risks and security concerns among healthcare providers and patients have subsequently increased. In previous research where 205 research papers were reviewed related to healthcare privacy and security, the results reveal a significant lack of research on telehealth security and privacy. In this panel, we aim to discuss this critical gap by studying how current healthcare process designs and provider workflows associated with the use

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Reena, Prasad*, and Tripti Arjariya Dr. "AN APPROACH TO MITIGATE THE PRIVACY ISSUES IN SMARTPHONE HEALTHCARE SYSTEM THROUGH VISUAL CRYPTOGRAPHY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 102–7. https://doi.org/10.5281/zenodo.48839.

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Smartphone Healthcare Systems are the immerging pervasive technology which provides the healthcare services at any location through mobile phones. It contributes to access the Electronic Medical Records (EMR) from and to the practitioners, researchers, patients, pathologist and doctors to treating their illness. The emerging technologies help to ease the treatment through discussing the doctors at any location according to the availability of time schedule. These EMR contains the private information of the patients. Therefore exchanging the EMR’s must be secured at network as well as sto

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Davenny, Ben, and Alex Odom. "Healthcare communication in acoustical consulting practice." Journal of the Acoustical Society of America 152, no. 4 (2022): A95. http://dx.doi.org/10.1121/10.0015662.

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With respect to speech communication in healthcare facilities, acoustical consultants are often concerned with overheard speech in the context of patient privacy. Low background sound is often a culprit with speech privacy problems, and examples will be given in exam rooms and common areas. Case studies of both poor speech privacy and poor speech communication will be given along with proposed solutions. Finally, popular science communications from the authors’ corporate blog during the COVID pandemic on speech communication and personal protective equipment will be discussed.

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Dr., Vinod Varma Vegesna. "Secure and Privacy-Based Data Sharing Approaches in Cloud Computing for Healthcare Applications." Mediterranean Journal of Basic and Applied Sciences (MJBAS) 4, no. 4 (2020): 194–209. https://doi.org/10.46382/MJBAS.2020.4409.

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The cloud framework is extensively employed in the medical industry for a broad range of applications including medical information storage, distribution, as well as administration. Given the advantages of cloud computing, several medical companies are exploring implementing such techniques to address various difficulties inside the medical sector. It evolved into an essential component of healthcare delivery. It may help medical companies concentrate on their activities, and medical assistance, including clinical management. This provides a safer approach for sharing confidential material wit

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Sonu, Deshmukh Shivani Shahu* Thorvi Kubde Kunal Darote Payal Deshmukh. "Scope of Artificial Intelligence (AI) in Data Privacy and Security Concerns in Healthcare: A Narrative Review." International Journal of Pharmaceutical Sciences 3, no. 3 (2025): 2829–39. https://doi.org/10.5281/zenodo.15101069.

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This study looks at how artificial intelligence (AI) affects patient data security and privacy in healthcare. AI, which can think like humans, is improving healthcare by making diagnostics better, personalizing treatments, and making hospital operations more efficient. However, there are big concerns about keeping patient data private and secure. AI needs a lot of data, which increases the risk of unauthorized access to sensitive information. This raises ethical issues and the potential misuse of personal data. Devices like wearables and the Internet of Medical Things (IoMT) make these problem

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Grandison, Tyrone, and Rafae Bhatti. "Regulatory Compliance and the Correlation to Privacy Protection in Healthcare." International Journal of Computational Models and Algorithms in Medicine 1, no. 2 (2010): 37–52. http://dx.doi.org/10.4018/jcmam.2010040103.

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Recent government-led efforts and industry-sponsored privacy initiatives in the healthcare sector have received heightened publicity. The current set of privacy legislation mandates that all parties involved in the delivery of care specify and publish privacy policies regarding the use and disclosure of personal health information. The authors’ study of actual healthcare privacy policies indicates that the vague representations in published privacy policies are not strongly correlated with adequate privacy protection for the patient. This phenomenon is not due to a lack of available technology

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Omran, Esraa, Tyrone Grandison, David Nelson, and Albert Bokma. "A Comparative Analysis of Chain-Based Access Control and Role-Based Access Control in the Healthcare Domain." International Journal of Information Security and Privacy 7, no. 3 (2013): 36–52. http://dx.doi.org/10.4018/jisp.2013070103.

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The importance of electronic healthcare has caused numerous changes in both substantive and procedural aspects of healthcare processes. These changes have produced new challenges for patient privacy and information secrecy. Traditional privacy policies cannot respond to rapidly increased privacy needs of patients in electronic healthcare. Technically enforceable privacy policies are needed in order to protect patient privacy in modern healthcare with its cross-organizational information sharing and decision making. This paper proposes a personal information flow model that proposes a limited n

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Yekaterina, Kan. "Challenges and Opportunities for AI in Healthcare." International Journal of Law and Policy 2, no. 7 (2024): 11–15. http://dx.doi.org/10.59022/ijlp.203.

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The integration of artificial intelligence (AI) in healthcare presents a dual challenge: maximizing the efficiency of medical processes while safeguarding patient privacy. This comprehensive review examines the delicate balance between leveraging AI's potential in healthcare and preserving individual data privacy. Through analysis of recent literature, case studies, and regulatory frameworks, we explore the current landscape of AI applications in healthcare, associated privacy risks, and emerging solutions. Findings reveal that while AI significantly enhances diagnostic accuracy and treatment

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Jayakumar, Sujayaraj Samuel, Kunal Meher, Udaybhanu Rout, et al. "Risk Analysis of Data Privacy Violations in Digital Health Records and Patient Confidentiality." Seminars in Medical Writing and Education 3 (December 31, 2024): 498. https://doi.org/10.56294/mw2024498.

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The fast growth of digital health tools has changed the way healthcare is provided, making it easier for both people and healthcare workers to get the care they need and more efficient. On the other side, digitising health data seriously compromises patient privacy and data security. The various hazards resulting from violations of data privacy in digital health records are discussed in this article. It emphasises the larger picture for healthcare systems and how these breaches can compromise patient privacy. Patient data is saved and distributed across many platforms as Electronic Health Reco

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Popuri, Venkatesh. "Securing Healthcare Data: Federated Learning for Privacy-Preserving AI in Medical Applications." International Journal of Management Technology 11, no. 3 (2024): 64–82. https://doi.org/10.37745/ijmt.2013/vol11n36482.

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Federated Learning (FL) is a technique used when sharing raw data cannot be done because of privacy laws. FL is used to train machine learning algorithms on decentralized data. Electronic health records, which hold private patient data, are one type of such data. In FL, local models are trained, and the model parameters are then combined on a central server instead of sharing sensitive data. But this approach poses privacy risks, so before disclosing the model parameters, privacy protection measures such data confidentiality must be put in existence. During the pandemic, there is a need to imp

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Panahi, Omid. "Secure IoT for Healthcare." European Journal of Innovative Studies and Sustainability 1, no. 1 (2025): 17–23. https://doi.org/10.59324/ejiss.2025.1(1).03.

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The Internet of Things (IoT) has revolutionized healthcare, enabling remote patient monitoring, personalized medicine, and improved diagnostics. However, the interconnected nature of healthcare IoT devices introduces significant security and privacy challenges. This paper explores the critical need for robust security measures in healthcare IoT systems, focusing on protecting sensitive patient data and ensuring the integrity of medical devices. We propose a multi-layered security framework that addresses vulnerabilities at the device, network, and application levels. This framework incorporate

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Omid, Panahi. "Secure IoT for Healthcare." European Journal of Innovative Studies and Sustainability 1, no. 1 (2025): 17–23. https://doi.org/10.59324/ejiss.2025.1(1).03.

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The Internet of Things (IoT) has revolutionized healthcare, enabling remote patient monitoring, personalized medicine, and improved diagnostics. However, the interconnected nature of healthcare IoT devices introduces significant security and privacy challenges. This paper explores the critical need for robust security measures in healthcare IoT systems, focusing on protecting sensitive patient data and ensuring the integrity of medical devices. We propose a multi-layered security framework that addresses vulnerabilities at the device, network, and application levels. This framework incorporate

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P, Arul, and Renuka S. "Preserving the Privacy of the Healthcare, Clinical and Personal Data using Blockchain." Indian Journal of Science and Technology 16, no. 1 (2023): 23–31. https://doi.org/10.17485/IJST/v16i1.1842.

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ABSTRACT <strong>Objective:</strong> The healthcare sector produces more data in every day. The security and privacy of this data are prone to misuse. This work aims to use blockchain technology to create healthcare systems that provide users with privacy, control, and data security. <strong>Methods:</strong> This study envisages a new method to secure private healthcare data and allows only the patients, doctors, and a few medically equipped persons to access the precious data by incorporating a recognition method, authorization of the user, user access monitoring, verification

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Karunarathne, Sivanarayani M., Neetesh Saxena, and Muhammad Khurram Khan. "Security and Privacy in IoT Smart Healthcare." IEEE Internet Computing 25, no. 4 (2021): 37–48. http://dx.doi.org/10.1109/mic.2021.3051675.

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Kadam, Sarika, Akshata Meshram, and Sheetal Suryawanshi. "Blockchain for Healthcare: Privacy Preserving Medical Record." International Journal of Computer Applications 178, no. 36 (2019): 5–9. http://dx.doi.org/10.5120/ijca2019919092.

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