DATA SECURITY, PRIVACY AND CRYPTOLOGY IN MODERN ERA

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

Edge computing enhances data processing efficiency by minimizing latency and bandwidth usage, but its decentralized nature creates severe privacy concerns. The traditional security methodologies are ineffective in edge environments, so robust privacy frameworks are critical to ensure data confidentiality, integrity, and regulatory compliance. This paper presents various privacy-preserving techniques, including cryptographic methods, federated learning, access control mechanisms, and anonymization schemes. Cryptographic techniques such as homomorphic encryption and secure multi-party computation enable secure processing of data, and federated learning minimizes data exposure in AI-driven applications. Access control frameworks such as role-based and zero-trust models prevent unauthorized access, and anonymization techniques minimize re-identification threats. Edge-to-cloud privacy frameworks and secure communication protocols also ensure data security.

Transport Layer Security (TLS) is a secure communication protocol that is used in many secure electronic applications. In order to establish a TLS connection, a client and server engage in a handshake, which usually involves the transmission of digital certificates. In this paper we present a practical speedup of TLS handshakes over bandwidth-constrained, high-latency (i.e. disadvantaged) links by reducing the communication overhead associated with the transmission of digital certificates. This speedup is achieved by deploying two specialized TLS proxies across such links. Working in tandem, one proxy replaces certificate data in packets being sent across the disadvantaged link with a short reference, while the proxy on the other side of the link restores the certificate data in the packet. Local or remote caches supply the certificate data. Our solution preserves the end-to-end security of TLS and is designed to be transparent to third-party applications, and will thus facilitate rapid deployment by removing the need to modify existing installations of TLS clients and TLS servers. Testing shows that this technique can reduce the overall bandwidth used during a handshake by 50% in test emulation and by over 20% of TLS session volume in practice. In addition, it can reduce the time required to establish a secure channel by over 40% across Iridium, a widely used satellite link in practice.

With the rapid growth of digital data and inter-connected devices, the integration of blockchain and Internet of Things technology offers a promising chance to revolutionize data management approaches. With today's global connectivity, the management and securing of ever-increasing amounts the amount of data produced by Internet of Things (IoT) devices is becoming an increasing concern. This study investigates a new solution to address such a concern by integrating blockchain technology into IoT systems. The aim is to develop a strong framework that takes advantage of the decentralized and secure character of blockchain and the wall-to-wall connectivity offered by the IoT. This integration is intended to address important needs such as data integrity, The integration aims to tackle crucial issues like data integrity, privacy, and unauthorized access. With the connectivity and sensing capabilities of the IoT, along with the blockchain technology of decentralized consensus and immutable ledgers, businesses are able to develop secure systems to retrieve, transfer, and store such data. In the experiments conducted on the Ethereum blockchain platform, it has been found that the blockchain and IoT technology solution not only secures data but also facilitates information management. The experiment results demonstrate that the proposed solution is able to protect against tampering, increase the transparency of transactions, and to minimize the chances of unauthorized access. The framework is scalable and highly adaptable, and it is therefore a promising solution for various applications. This framework employs an innovative approach to ensure secure and efficient data management.

Secure multiparty computation protocol based on homomorphic encryption and its application in blockchain

With the continuous development of the Internet era, people's demand for network security is increasingly high, information is an essential component of the network, which implies numerous privacy, secrets, but also contains a large number of value, thus generating a multiparty trust issues of data security. Blockchain technology itself is still in the early stages of rapid development, the existing blockchain system in the design and implementation of the use of distributed systems, cryptography, game theory, network protocols and many other disciplines, for learning the principles and practical applications have brought considerable challenges. The blockchain, with its decentralized quality, quickly captures the attention of the public and solves many security problems derived from data security, thus attracting wide attention from people. In recent years, along with the increasing maturity of homomorphic encryption technology and push new, it is more and more people's favour and attention. Homomorphic encryption is an encryption technology that provides the ability to perform various operations on data in an encrypted state without compromising its confidentiality. Its concern is the security of data processing. That is, others can process the encrypted data, but the process does not reveal any of the original content. Also, the user who has the Key decrypts the processed data and gets the result of the processing precisely. The main encryption technique used in blockchain applications is the RSA encryption algorithm, [1] which is used to ensure that the user's private key is not compromised, but no project has been noticed about the homomorphic encryption method for smart contracts. [2] The purpose of this paper is to analyze the principle of homomorphic encryption and discuss its practical application in data security in combination with blockchain technology.

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

The integrity of Internet of Things (IoT) devices has caused a fast spread in an era of data-driven decision-making across businesses. This tutorial survey provides a comprehensive review of current IoT data handling advances, focusing on data quality management (DQM). The article starts with the key aspects of IoT data management. In this regard, we shed light on the data source, volume and velocity, variety, lifecycle, security and privacy, scalability and distribution processing, anomaly detection, and energy efficiency. Then, We present a comprehensive taxonomy of IoT DQM based on the application type, such as smart cities, healthcare, agriculture, environmental monitoring, retail and supply chain, and smart grids (SGs). As IoT data processing, analysis, and security play a significant role in DQM; this tutorial survey carefully addresses how modern technologies maintain this role. More particularly, this work investigates the use of edge computing for real-time data processing and the incorporation of synthetic data to supplement restricted resources incorporating the issues of managing the massive datasets created by IoT implementations. In addition, the paper addresses the use of machine learning (ML) algorithms for in-depth analysis of IoT data streams, DQ evaluation protocols, and detection tactics during data transfer. Moreover, the article investigates complete security measures for protecting sensitive data, such as access control regulations and several security techniques, including authentication, encryption, and secure communication protocols that enable IoT data management. Besides, blockchain technology’s significant roles in this regard have been comprehensively addressed. Along with summarizing and reviewing the latest efforts in DQM in IoT-based systems, we shed light on their strong and weak points and discuss upcoming trends and potential difficulties in IoT data management. Last but not least, we continue by emphasizing the cumulative impact of these advances and shedding light on the open issues and challenges. Finally, this in-depth tutorial survey aims to be a significant resource for academics, practitioners, and stakeholders interested in the changing environment of IoT data management, with a particular emphasis on DQ.

With the rapid development of Internet of Things, the demand of data sharing is not only to ensure the data integrity, but also to protect user's individual privacy. Secure multi-party computation, as a technology paradigm based on cryptography technology, enables privacy protection in data sharing among multiple parties, while the implement of secure multi-party computation is limited due to the inefficient, complex computation protocol and frequent interaction. Fortunately, the emergence of blockchain technology provides excellent properties of decentralization, verifiability and high reliability for secure multi-party computation. In this paper, we propose a blockchain-based secure multi-party computation architecture for data sharing, where we design an aggregator consortium for data storage, verification and joint computation. Moreover, we introduce a straightforward secure multi-party computation scheme based on homomorphic encryption. In addition, we analyze and discuss this scheme in terms of data security, access flexibility, attack resistance and potential applications.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

The paper explores how blockchain technology can transform data management, especially when it comes to handling privacy and security issues. Blockchain's immutability, transparency, and decentralization make it a potentially useful tool for addressing the expanding problems associated with data security and integrity. However, the slow adoption of blockchain technology is influenced by a complex interplay of developing privacy concerns, legislative uncertainty, and technological subtleties. The current study aims to identify key data privacy, protection, and security challenges associated with blockchain adoption in various domains, assess the adequacy of the current data privacy, protection, and security laws to address these challenges, and propose potential solutions and areas for further research to mitigate data privacy, protection, and security risks in blockchain applications. Additionally, the research has examined the adaptability and dependability of the two main kinds of blockchain—public and private—as well as the variations in their reach and visibility.

The decentralized and immutable nature of blockchain technology offers promising avenues for secure healthcare data sharing among legitimate institutions. However, current blockchain systems face critical bottlenecks in handling large volumes of medical data, low storage efficiency, and vulnerabilities in security. To overcome these challenges, a novel healthcare blockchain data-sharing scheme integrating Secure Multi-party Computation (SMPC) is proposed. To mitigate the risk of data leaks resulting from key compromise, Blakley's secret sharing technique is employed for key distribution management, and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is integrated for access control. To address the challenge of handling large data on the chain, data that needs to be uploaded to the blockchain is encoded and fragmented using erasure coding techniques. Additionally, a Byzantine Fault Tolerance mechanism based on Reputation and Polling verification (TRP-PBFT) is introduced to enhance data sharing efficiency and system robustness. Simulation experiments conducted on platforms like Fabric demonstrate that this solution improves data sharing efficiency while ensuring data confidentiality and integrity. In comparison to Practical Byzantine Fault Tolerance (PBFT) under similar conditions, TRP-PBFT reduces the average latency from 655.42 ms to 342.22 ms and increases the average throughput from 107.31 TPS to 143.45 TPS. The scheme maintains high throughput and offers relatively stable latency performance.

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

Blockchain technology has emerged as a solution to tackle challenges in the healthcare sector, such as fragmented data vulnerabilities in security and a lack of transparency. In this chapter, the authors provide an overview of blockchain and its potential benefits for the healthcare industry. These include collaboration, improved integrity and security of data, enhanced efficiency, empowerment of patients, and advancements in supply chain management. However, implementing blockchain faces obstacles like scalability issues, the absence of interoperability standards uncertainties regarding regulatory aspects, and usability concerns. To ensure the adoption of technology in healthcare, the authors propose a strategic roadmap for both short-term implementation at individual hospitals and long-term transformation of insurance and billing processes through smart contract automation. This requires integrating with existing IT systems training for staff members addressing ethical considerations effectively and customizing approaches based on regional requirements.

The healthcare industry faces significant challenges in safeguarding data security and integrity, particularly with the rise of digital health systems and the sensitive nature of patient information. Blockchain technology offers a decentralized, immutable, and secure solution to address these issues, allowing healthcare organizations to protect and manage patient data effectively. This paper examines how blockchain can enhance data security and integrity within healthcare by examining its key features, such as immutability, transparency, and decentralized access, which collectively provide a robust framework for secure data management. The potential applications of blockchain in healthcare, including patient consent management, access control through smart contracts, and data-sharing solutions, are analyzed alongside challenges such as regulatory compliance, cost, and interoperability. By integrating blockchain, healthcare systems can achieve improved data security, fostering trust and resilience in patient data management. This paper concludes with case studies and discusses future trends in blockchain implementation within healthcare, underscoring its transformative potential for securing data while addressing privacy and ethical concerns. Keywords: Blockchain, healthcare, data security, data integrity, patient data management, smart contracts.