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302 articles for “Privacy”
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A Framework for Privacy-preserving AI Models in Cloud Computing: Challenges and Solutions
Abstract: The growing adoption of cloud computing for deploying artificial intelligence (AI) models has led to significant advancements in sectors such as healthcare, finance, and e-commerce. However, the integration of AI with cloud computing raises critical privacy concerns, particularly when handling sensitive data. This paper presents a comprehensive framework for implementing privacy-preserving AI models in cloud environments, addressing the unique challenges, and proposing effective solutions. The suggested framework employs advanced privacy-preserving …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 1–12 Read article
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Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and GPU-based architectures without centralizing sensitive data. This work proposes a parallel Adaptive Federated Learning (AFL) framework that integrates Differential Privacy and Secure Aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, and aggregation weights …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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An Adaptive and Privacy-Aware Federated Learning Framework for Efficient and Secure Model Training Across Heterogeneous Datasets
Abstract: The problem of efficiency and privacy regarding heterogeneous data in modern distributed machine learning systems is a vital point that should be taken into account. The absence of IID data distribution, client heterogeneity, and privacy invasion during the aggregation model are the bane of conventional federated learning (FL) approaches to learning like FedAvg and FedProx. The paper proposes that the adaptive and privacy-aware FL framework (AFL-P) can be used to …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 16–25 Read article
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Proof-of-Minimum Privacy Leak Consensus Strategy in Blockchain
Abstract: In this study, we propose a novel consensus algorithm to preserve the security and privacy of a transaction. We propose a Proof-of-Minimum Privacy Leak consensus strategy. This means that the competing nodes which participate in the competition to mine the next block should give a proof of minimum privacy leak during its transaction. Only this proof will give highest votes to that node, and it will be elected as the …
Published in E-Commerce for Future & Trends Read article
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Privacy-preserving Multi-keyword Search in Multi-owner Setting Using Blockchain
Abstract: Searchable encryption (SE) has become an essential cryptographic technique, allowing users to securely search through encrypted data. However, most existing SE schemes rely on a single intermediary, such as a cloud server, leading to potential single-point failures, privacy breaches, and untrustworthy results. Many blockchain-based SE schemes have been proposed to address these issues. However, they frequently encounter difficulties such as supporting a multi-keyword, multi-owner model, ensuring query privacy, and maintaining …
Published in Journal of Advances in Shell Programming · Vol. 11, Issue 2, 2024 · pp. 12–18 Read article
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Navigating Privacy and Security in Cloud Computing
Abstract: Cloud computing has rapidly evolved, serving as a cornerstone for storage, information processing, and various applications. Its adoption has surged across enterprises and small businesses alike, offering them efficient means to store and process data. However, alongside its undeniable benefits, the cloud also presents inherent risks to privacy and security, as data traverses and resides on remote servers. Employing tactics such as data encryption, multifactor authentication, access control, and intrusion …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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An Overview of Privacy-Preserving Data Encryption Techniques in Mobile Cloud Computing for Big Data
Abstract: With the introduction of mobile cloud computing (MCC), data processing, storage, and sharing have undergone a radical transformation that has greatly improved organizational effectiveness and quality of life. But there are also serious worries about data security and privacy due to the increasing usage of mobile devices and cloud computing, particularly when managing large amounts of data from many sources like sensors and cellphones. The privacy issues surrounding MCC are …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 1, 2025 · pp. 1–7 Read article
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Differential Privacy-Aware Data Sanitization for Multi-Level Security
Abstract: Multi-level security (MLS) models are fundamental for enforcing mandatory access control in high-security environments such as government, military, healthcare, and finance. However, traditional MLS frameworks, including the Bell-LaPadula and Biba models, often create rigid data silos, preventing efficient data utilization. Differential privacy (DP) presents a novel solution by enabling controlled information leakage while preserving confidentiality. By injecting statistical noise into query results, DP allows lower-clearance users to access sanitized versions …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 1, 2025 · pp. 42–52 Read article
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Data Privacy in AI: Securing the Sensitive Information Through Homomorphic Encryption
Abstract: Artificial intelligence (AI) technology increasingly relies on sensitive user data, particularly finance and healthcare. While legacy encryption technologies safeguard data in transit and at rest, they are of no use when data must be decrypted to be processed. This is a bleak privacy threat, particularly in AI applications that call for constant processing of data. The objective of this study is to apply homomorphic encryption, a feature in which operations …
Published in International Journal of Information Security Engineering · Vol. 3, Issue 2, 2025 · pp. 25–30 Read article
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Privacy Preservation Methods in Multimedia Applications: A Comprehensive Review
Abstract: This comprehensive review explores the current landscape of privacy preservation techniques in multimedia applications, offering a detailed examination of their effectiveness, limitations, and future directions. As the use of multimedia data continues to grow across diverse sectors such as healthcare, surveillance, social media, and entertainment, ensuring the confidentiality and integrity of this data has become a pressing concern. The study covers a broad spectrum of privacy-preserving approaches, from conventional cryptographic …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 2, 2025 · pp. 33–41 Read article
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An Effective Privacy Preservation Technique for Enhancing Data Usability
Abstract: The rapid growth and adoption of modern database systems have created immense opportunities for researchers, industries, and organizations to extract meaningful knowledge and make data-driven decisions. While this progress has enabled the discovery of valuable patterns and trends, it has also intensified the challenge of safeguarding individual privacy. Merely removing direct identifiers such as names, social security numbers, or Aadhar card details is no longer sufficient, as adversaries can often …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 3, 2025 · pp. 35–40 Read article
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Securing the Internet of Things: A Survey on Lightweight Blockchain Framework for Enhancing Security and Privacy
Abstract: Modern technologies, supported by the Internet of Things (IoT), have imparted great speed in data sharing between connected devices across several domains of the economy. The Internet of things transformed data collection activities with its creation of smart homes and cities, healthcare, transportation environments, and industrial automation. The fact that there are multiple resource-limited devices, and continuous data transfer of sensitive information has turned the IoT devices less secure and …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 6–14 Read article
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A Practical Approach for Privacy Preserving in Cloud Computing Using Fully Homomorphic Encryption Scheme BFV and CKKS
Abstract: Cloud computing is a new type of computing architecture whereby data may be accessed over the Internet along with other services linked to its scalable data centers in the cloud. The risk associated with computing is increased since it provides essential services that are typically provided to any third party, making it more difficult to enable data security, privacy, confidentiality, integrity, and authentication. To reduce security risks, most users choose …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 1, 2025 · pp. 1–8 Read article
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Healthcare IT Innovations: Blockchain Technology for Healthcare Data Security and Privacy
Abstract: The healthcare sector has undergone a significant digital transformation, leading to the generation and sharing of vast quantities of sensitive medical data. While this digitization has improved patient care and outcomes, it has also introduced critical challenges related to data security and privacy. The increasing rate of data breaches in healthcare puts patient confidentiality at risk, mainly leading to identity theft, financial fraud, and loss of public trust in healthcare …
Published in International Journal of Information Security Engineering · Vol. 3, Issue 2, 2025 · pp. 01–12 Read article
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Advancements in Privacy-Preserving Techniques for Cloud Database Management Systems: A Review Analysis
Abstract: Cloud computing is a technology that provides a lot of configurable resources, enabling decentralized data execution and space. Cloud technologies have transformed the ideas of internal data storage and access to provide businesses with flexibility and efficiency. Thanks to cloud services like DBaaS, users may make use of advanced database features without having to worry about the burden of traditional databases. With more organizations moving to the usage of Cloud …
Published in Journal of Web Engineering & Technology · Vol. 13, Issue 1, 2026 · pp. 11–22 Read article
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A Study on the Ethical Implications and Data Privacy Challenges in the Age of Artificial Intelligence
Abstract: The fast adoption of Artificial Intelligence (AI) in areas like education, business, and everyday life comes with both tectonic and important ethical and data privacy issues. This paper examines the principle-practice divide between the ideals of the ethics as they are set and applied in the realities of AI implementation. The study is based on a mixed-methodology design, comprising of a quantitative survey of 52 participants and a qualitative study …
Published in Current Trends in Information Technology · Vol. 16, Issue 2, 2025 Read article
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Balancing Progress and Privacy: Exploring the Societal Implications of Artificial Intelligence in the Digital Era
Abstract: This study investigates concerns about privacy, accountability, and justice, underscoring the importance of further examining the societal ramifications of artificial intelligence (AI). Interdisciplinary collaboration and responsible AI development will be fostered as a result. The intricate ramifications of AI on various facets of human existence—including labor, personal privacy, social connections, sound judgment, and ethical concerns—are also examined in this essay. To attain a more profound comprehension of the intricate conduct …
Published in Journal of Advancements in Robotics · Vol. 11, Issue 1, 2024 · pp. 15–19 Read article
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Navigating the Dual Edge: A Comprehensive Technical Survey of Security, Privacy, and Countermeasures in the Era of Artificial Intelligence
Abstract: Artificial Intelligence (AI) is seamlessly weaved into vital sectors such as self-driving cars, high-speed trading systems, and defense strategies, it has triggered a counterintuitive development in advanced cyber-attacks. This survey paper attempts to perform an in-depth technical analysis on “AI Attack Surface.” There are threats across three main vectors. Data Integrity Attacks focuses specifically examining ‘Clean Label’ poisoning and backdoor injection. Model Confidentiality Breaches is discussing the mathematics behind Model …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 1, 2026 Read article
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Mitigation of Location-based Attacks for Increased User Privacy
Abstract: Designing an effective method to mitigate location-based attacks is a pressing imperative in the realm of cybersecurity. As our world becomes increasingly interconnected through the Internet of Things and location-aware services, the risks associated with the misuse of location data continue to escalate. Location-based attacks encompass a spectrum of threats, from geolocation spoofing and eavesdropping to geo-tagging abuse, potentially leading to severe privacy invasions and security breaches. This abstract provides …
Published in International Journal of Mobile Computing Technology · Vol. 1, Issue 2, 2023 · pp. 1–5 Read article
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Enhancing Security and Privacy in Smart Factory: A Blockchain Based Solution
Abstract: The manufacturing industry is currently experiencing a transformative shift due to the rise of smart factories, propelled by interconnected devices and automated systems. However, this integration of diverse technologies within smart factories introduces significant concerns regarding security and privacy. This research paper thoroughly investigates the vulnerabilities and threats prevalent in smart factory ecosystems, shedding light on the potential risks associated with centralized data storage and communication channels. The study emphasizes …
Published in International Journal of Wireless Security and Networks · Vol. 2, Issue 1, 2024 · pp. 13–20 Read article