Secure Data Processing Research Articles

Engaging Patients and Other Stakeholders in "Designing for Dissemination" of Record Linkage Methods and Tools.

Novel record linkage (RL) methods have the potential to enhance clinical informatics by integrating patient data from multiple sources-including electronic health records, insurance claims, and digital health devices-to inform patient-centered care. Engaging patients and other stakeholders in the use of RL methods in patient-centered outcomes research (PCOR) is a key step in ensuring RL methods are viewed as acceptable, appropriate, and useful. The University of Colorado Record Linkage (CURL) platform empowers the use of RL in PCOR. This study aimed to describe the process of engaging patients and other stakeholders in the design of an RL dissemination package to support the use of RL methods in PCOR. Customer discovery, value proposition design, and user experience methods were used to iteratively develop an RL dissemination package that includes animated explainer videos for patients and an RL research planning workbook for researchers. Patients and other stakeholders (researchers, data managers, and regulatory officials) were engaged in the RL dissemination package design. Patient partners emphasized the importance of conveying how RL methods may benefit patients and the rules researchers must follow to protect the privacy and security of patient data. Other stakeholders described accuracy, flexibility, efficiency, and data security compared with other available RL solutions. Dissemination package communication products reflect the value propositions identified by key stakeholders. As prioritized by patients, the animated explainer videos emphasize the data privacy and security processes and procedures employed when performing research using RL. The RL workbook addresses researchers' and data managers' needs to iteratively design RL projects and provides accompanying resources to alleviate leadership and regulatory officials' concerns about data regulation compliance. Dissemination products to promote adoption and use of CURL include materials to facilitate patient engagement in RL research and investigator step-by-step decision-making materials about the integration of RL methods in PCOR.

A reinforcement federated learning based strategy for urinary disease dataset processing

Urinary disease is a complex healthcare issue that continues to grow in prevalence. Urine tests have proven valuable in identifying conditions such as kidney disease, urinary tract infections, and lower abdominal pain. While machine learning has made significant strides in automating urinary tract infection detection, the accuracy of existing methods is hindered by concerns surrounding data privacy and the time-intensive nature of training and testing with large datasets. Our proposed method aims to address these limitations and achieve highly accurate urinary tract infection detection across various healthcare laboratories, while simultaneously minimizing data security risks and processing delays. To tackle this challenge, we approach the problem as a combinatorial optimization task. We optimize the accuracy objective as a concave function and minimize computation delay as a convex function. Our work introduces a framework enabled by federated learning and reinforcement learning strategy (FLRLS), leveraging lab urine data. FLRLS employs deterministic agents to optimize the exploration and exploitation of urinary data, while the actual determination of urinary tract infections is performed at a centralized, aggregated node. Experimental results demonstrate that our proposed method improves accuracy by 5% and reduces total delay. By combining federated learning, reinforcement learning, and a combinatorial optimization approach, we achieve both high accuracy and minimal delay in urinary tract infection detection.

Reconciling the biomedical data commons and the GDPR: three lessons from the EUCAN ELSI collaboratory

The coming-into-force of the EU General Data Protection Regulation (GDPR) is a watershed moment in the legal recognition of enforceable rights to informational self-determination. The rapid evolution of legal requirements applicable to data use, however, has the potential to outstrip the capabilities of networks of biomedical data users to respond to the shifting norms. It can also delegitimate established institutional bodies that are responsible for assessing and authorising the downstream use of data, including research ethics committees and institutional data custodians. These burdens are especially pronounced for clinical and research networks that are of transnational scale, because the legal compliance burden for outbound international data transfers from the EEA is especially high. Legislatures, courts, and regulators in the EU should therefore implement the following three legal changes. First, the responsibilities of particular actors in a data sharing network should be delimited through the contractual allocation of responsibilities between collaborators. Second, the use of data through secure data processing environments should not trigger the international transfer provisions of the GDPR. Third, the use of federated data analysis methodologies that do not provide analysis nodes or downstream users access to identifiable personal data as part of the outputs of those analyses should not be considered circumstances of joint controllership, nor lead to the users of non-identifiable data to be considered controllers or processors. These small clarifications of, or modifications to, the GDPR would facilitate the exchange of biomedical data amongst clinicians and researchers.

Big Data Analytics and Implementation Challenges of Machine Learning in Big data

The term "big data" refers to an information processing system that combines different conventional data techniques. Big data also includes a large amount of personally identifiable and authenticated data, making privacy a major concern. Various techniques have been developed to provide security and efficient data processing. Machine learning is a form of data technology that deals with one of the most important and least understood aspects of the data collected. Deep learning algorithms, similar to machine learning algorithms, learn programmers automatically from data and are thought to improve the efficiency and security of large data sets. The efficiency of machine learning and deep learning in a sensitive environment was evaluated in this paper, which reviewed security problems in big data. This paper begins by providing an overview of machine learning and deep learning algorithms. The research then moves on to machine learning problems and challenges, as well as potential solutions. The investigation into deep learning principles of big data continues after that. Finally, the report examines approaches used in recent research developments and concludes with recommendations for the future.

Tackling security and privacy challenges in the realm of big data analytics

As organizations increasingly harness the power of big data analytics to derive insights and drive decision-making, the paramount concerns of security and privacy have come to the forefront. This paper presents a comprehensive framework for addressing the multifaceted challenges of big data security and privacy. Drawing on a synthesis of cutting-edge technologies, encryption methods, and access control mechanisms, our approach aims to fortify the entire big data lifecycle. The paper delves into innovative strategies for secure data storage, transmission, and processing, ensuring that sensitive information is shielded from unauthorized access or malicious attacks. Additionally, the framework incorporates robust privacy-preserving techniques, including anonymization and differential privacy, to uphold individual confidentiality. Through a meticulous analysis of current trends, emerging threats, and regulatory landscapes, this paper not only provides theoretical insights but also practical guidelines for organizations seeking to navigate the intricate landscape of big data while safeguarding the integrity, security, and privacy of the vast datasets at their disposal.

Vulnerability detection in the network traffic flow of the RADIUS protocol based on the object-oriented model


 
 
 The RADIUS protocol was analyzed from the point of view of its functionality and security. The internal structure and the division into functions were shown. There were described the structure of the RADIUS network, the functions of the network access server or NAS, and the RADIUS server. The advantages of the centralized secure data processing technology based on the RADIUS protocol were shown. Mechanisms of secure processing of requests at the authentication and authorization stage were described. Sources and types of protocol vulnerabilities were studied and possible attack scenarios were identified. The relevance of creating models for evaluating the vulnerabilities of the RADIUS protocol was substantiated, and the methodology for building the model was chosen. An object-oriented model of the RADIUS protocol has been developed. A software application was developed and various attacks on the RADIUS protocol were tested. A number of potential vulnerabilities have been identified.
 
 

Research on Data Processing Security System Based on Bio commercial technology and Artificial Intelligence

Data security has become an increasingly important concern for organizations and individuals in the digital age. To address this issue, there has been growing interest in using bio-commercial technology and artificial intelligence (AI) to develop innovative security solutions. This paper presents a research study on the development of a data processing secure new entrants in the market for medical devices in Germany can use the use of biometric data, such as fingerprints, iris scans, and voice recognition, in combination with AI algorithms to enhance the security of data processing. The paper provides an overview of the technical framework and architecture of the proposed system, and evaluates its effectiveness in comparison to traditional security measures. The study uses a combination of quantitative and qualitative research methods, including surveys, interviews, and simulations, to collect and analyze data on the performance, accuracy, and usability of the proposed system. The results suggest that the data processing security system based on bio-commercial technology and AI is highly effective in preventing unauthorized access and enhancing data protection. The system provides a high level of accuracy, speed, and user-friendliness, making it a viable alternative to traditional security measures. The paper concludes with a discussion of the implications of the study for the field of data security and future research directions.

Basic Principles of Secure Information Systems

The relevance of the work lies in the fact that the pace of development of the field of information security does not correspond to progress in the development of data processing methods, resulting in a serious lack of practical knowledge of the subject area, which prevents the creation of conditions for secure recording, processing and storage of data. In the process of writing the article, modern methods and principles of ensuring information security, including a mandatory approach, were studied, and a brief description of software products supporting this approach was given. The mandatory method of information protection implies granting access rights to certain actions in accordance with the user’s status. Such actions can be, for example, writing, reading and changing data. Examples of user statuses are ‘administrator’ and ‘reader’, where the administrator, as a rule, is provided with the entire list of available actions, and the reader is provided with only a minimum, sufficient for familiarization activities. In its architecture, the mandatory approach often contains tools for conducting cluster analysis. Cluster analysis can be used both to carry out work on risk analysis and assessment, and to determine the degree of protection of an object. In any case, when building a cluster, it should be taken into account that some levels of protection may be represented by more objects than others. There are software products on the information security market that allow the use of a mandatory method of ensuring information security. One of the striking examples is the PostgreSQL database management system, which has an apparatus of labels assigned according to the user’s level of rights: the higher the level of rights, the higher the access level.

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

AbstractData mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is essential in these systems as healthcare data are highly sensitive. The proposed work first discusses various privacy and security challenges in these systems. To address these next, we discuss different privacy‐preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state‐of‐the‐art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed. Finally, we present open challenges in these systems and future research directions.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Commercial, Legal, and Ethical Issues > Security and Privacy

Homomorphic cryptosystem-based secure data processing model for edge-assisted IoT healthcare systems

The substantial acceptance of Internet-of-Things (IoT) in digital healthcare systems necessitates secure data processing before storing it on a cloud server. Since resource-limited IoT devices engender complexity in secure processing, edge computing technology is adopted. Edge-assisted IoT locates edge nodes in proximity to user devices enabling computation offloading and seamless interaction between IoT applications and cloud servers. Integration of these entities results in severe security and privacy issues due to large amount of sensitive information transmitted over vulnerable communication channels. As such, information protection and data integrity in edge-assisted IoT have become critical factors for improvement. Several data processing models have been proposed regarding the above-stated concerns. However, under a powerful adversarial scenario, traditional solutions lack in preserving robust security and privacy. This motivates us to address critical challenges of communication security and privacy-preserving computation offloading in edge-assisted IoT systems. In this article, we design a homomorphic cryptosystem-based secure data processing model (HC-SDPM), enabling secure data collection and aggregation offloading on edge nodes for our hypothesized edge-assisted IoT healthcare system. Paillier homomorphic encryption and certificateless linear homomorphic ID-based signatures are utilized to achieve end-to-end confidentiality and data integrity during transmission and aggregation. Moreover, HC-SDPM involves a semi-trusted authority to enable key-escrow resilience. The security analysis of HC-SDPM assures signatures unforgeability in the random oracle model under computational Diffie–Hellman assumptions. Compared with similar modeled schemes, HC-SDPM demonstrates a relative advantage in computational overhead, communication overhead, and storage complexity.

Secure Data Processing Technology of Distribution Network OPGW Line with Edge Computing

Promoted by information technology and scalable information systems, the network design and communication method of optical fiber composite overhead ground wire (OPGW) have been in great progress recently. As the overhead transmission line has strict requirements on the outer diameter and weight of OPGW, it is of vital importance to perform the physical-layer secure data processing for the distribution network OPGW line with edge computing. To this end, we examine a physical-layer secure distribution network OPGW with edge computing in this article, where there exists one transmitter S, one receiver D, one authorized legitimate monitor LM, and an interfering node I. To better analyze the system performance, we firstly give the definition of the system outage probability, based on the secure data rate. Then, we evaluate the system performance for the distribution network OPGW, by deriving analytical outage probability of secure data processing, to facilitate the system performance evaluation of secure data processing in the entire SNR regime. Finally, we demonstrate some simulation results to validate the analytical results on the physical-layer secure distribution network OPGW line with edge computing.

Enhancing Cybersecurity Using Blockchain Technology Based on IoT Data Fusion

In this study, blockchain technology is used to strengthen the security of wireless sensing data in the network architecture of the Internet of Things. For many organic farms that contain crops with high unit prices, the environmental parameters of the crops grown on the farm are often regarded as confidential data. First, a wireless sensing network is used to transmit sensing data to the farm’s front-end integrated microcontroller for fusion of the sensing data. After fusion of the sensing data is completed, the processed data is transmitted to the data processing center of the entire farm for encapsulation of the blockchain algorithm. Data are sent to the cloud database for storage after they are packaged by the system. At the same time, the data in the cloud database can be managed and analyzed by remote operators. Data encapsulation based on blockchain technology is used to effectively prevent any data from being stolen or destroyed by hackers. A complete blockchain encapsulation database system is implemented in the experimental stage, and the operator can use this system to encrypt the data that have been fused at the remote end for blockchain technology. In the encryption process of this blockchain, the security of data processing can be specifically enhanced by the system. Finally, each encapsulated block of data is securely stored in a private cloud database. There is a function to check whether the data in the cloud have been tampered with. A very innovative approach to using blockchain technology is proposed in this article to enhance the security of data processing.

Homomorphic Encryption on GPU

Homomorphic encryption (HE) is a cryptosystem that allows the secure processing of encrypted data. One of the most popular HE schemes is the Brakerski-Fan-Vercauteren (BFV), which supports somewhat (SWHE) and fully homomorphic encryption (FHE). Since overly involved arithmetic operations of HE schemes are amenable to concurrent computation, GPU devices can be instrumental in facilitating the practical use of HE in real world applications thanks to their superior parallel processing capacity. This paper presents an optimized and highly parallelized GPU library to accelerate the BFV scheme. This library includes state-of-the-art implementations of Number Theoretic Transform (NTT) and inverse NTT that minimize the GPU kernel function calls. It makes efficient use of the GPU memory hierarchy and computes 128 NTT operations for ring dimension of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> only in 176.1 μs on RTX 3060Ti GPU. To the best of our knowledge, this is the fastest implementation in the literature. The library also improves the performance of the homomorphic operations of the BFV scheme. Although the library can be independently used, it is also fully integrated with the Microsoft SEAL library, which is a well-known HE library that also implements the BFV scheme. For one ciphertext multiplication, for the ring dimension 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> and the modulus bit size of 438, our GPU implementation offers 63.4 times speedup over the SEAL library running on a high-end CPU. The library compares favorably with other state-of-the-art GPU implementations of NTT and BFV operations. Finally, we implement a privacy-preserving application that classifies encrypted genome data for tumor types and achieves speedups of 42.98 and 5.7 over CPU implementations using single and 16 threads, respectively. Our results indicate that GPU implementations can facilitate the deployment of homomorphic cryptographic libraries in real-world privacy-preserving applications.

Sustainable MapReduce: Optimizing Security and Efficiency in Hadoop Clusters with Lightweight Cryptography-based Key Management

The exponential growth of big data has led to a significant increase in the volume and complexity of data being generated and stored. This trend has created a huge demand for secure storage and processing of big data. Cryptography is a widely used technique for securing data, but traditional cryptography algorithms are often too resource-intensive for big data applications. To address this issue, light weight cryptography algorithms have been developed that are optimized for low computational overhead and low memory utilization. This research paper explores the use of a new sustainable algorithm that utilizes a lightweight cryptographybased key management scheme to optimize MapReduce security and computational efficiency in Hadoop clusters. The proposed sustainable MapReduce algorithm aims to reduce memory and CPU allocation, thereby significantly reducing the energy consumption of Hadoop clusters. The paper emphasizes the importance of reducing energy consumption and enhancing environmental sustainability in big data processing and highlights the potential benefits of using sustainable lightweight cryptography algorithms in achieving these goals. Through rigorous testing and evaluation, the paper demonstrates the effectiveness of the proposed sustainable MapReduce algorithm in improving the energy efficiency and computational performance of Hadoop clusters, making it a promising solution for sustainable big data processing.

Posterior probability and collaborative filtering based Heterogeneous Recommendations model for user/item Application in use case of IoVT

Intuitive context-aware algorithms called "Next Basket recommender systems" improve consumers' decision-making by offering suggestions for potential products they might like to buy next based on their past behaviour. Even though it is now well-known, this field is still young. Many different industries, such as e-commerce and healthcare, employ recommender systems. Even though these datasets frequently contain sensitive data, most recommender systems place a greater emphasis on the models' accuracy than on their security and privacy. We investigate this concept in the context of the sequential recommendation job known as Next Basket Recommendation (NBR), whose objective is to provide a user with a selection of goods based on their purchasing behaviour. A recent state-of-the-art technology is blockchain. Blockchain creates a trusted environment without the involvement of a third party, thus ensuring privacy and security. It is designed in such a way that it is enough in itself to create trust. In this paper, we propose and assimilate an authentic blockchain privacy system for a fortified user recommendation system for the Next Basket Recommendation. With next basket proposals based on blockchain for safe transactions and distributed context-based processing, this suggested system enables the development of decentralized RSs. Through the use of atomic swaps on the blockchain, this effort aims to provide viable solutions that can lead to fair procedures. It places a focus on effective data deletion procedures that preserve user privacy and transfers the issue of decremental learning to the Next Basket system, a more secure and wise recommendation. By incorporating blockchain into recommender systems (RSs), which contain smart contracts in the main blockchain-based RS protocol, it is feasible to create safe trust-based systems with the benefit of multi-party computation backed by blockchain. Additionally, it aids in protecting user information since blockchain enables the secure processing of customer data in online portals.

Enhanced PBFT Blockchain based on a Combination of Ripple and PBFT (R-PBFT) to Cryptospatial Coordinate

In this research, we introduce the combination of two Blockchain methods. Ripple Protocol Consensus Algorithm (RPCA) and Practical Byzantine Fault Tolerance (PBFT) are applied to cryptospatial coordinates to support cultural heritage tourism. The PBFT process is still used until the preparation process to ensure a maximum error of 33%, and every node would add a new chain in all nodes, so PBFT has a slower processing speed than other methods. This research cuts the PBFT process. After the preparation process in PBFT, the data was entered into the RPCA node and was calculated using an equation to minimize errors with a maximum limit of 20%. After this process, the was were sent to the commit process to store the data in all connected nodes in the Blockchain network; we call this combination of two methods R-PBFT. Combining the two methods can enhance data processing security and speed because it still uses the PBFT work combined with the speed of RPCA. Furthermore, this method uses a fault tolerance value from the RPCA of 20% to enhance data processing security and speed.

Digital marketing solutions based on consumer data and homomorphic encryption

Abstract In recent years, homomorphic encryption has attracted much research attention in the international cryptography community and is applied in various industries to enhance data confidentiality. With the advent of the era of big data, more and more data are available through online, using users another how to propose privacy protection models applicable to consumer data application scenarios under the premise of meeting data privacy, security and regulatory requirements, so that the models can formulate accurate marketing strategies more scientifically based on a large amount of consumer data, is the key to the current competition. In this process, more and more people are concerned about privacy confidentiality and security, and homomorphic encryption is of great significance for data processing security.

High-Resolution Tap-Based IoT System for Flow Data Collection and Water End-Use Analysis

Knowledge on water-use patterns in residential settings can help policymakers formulate well-targeted water conservation measures and evaluate the efficacy of such measures. Prior studies have applied machine learning techniques to disaggregate household-level water consumption data, collected by smart meters, into specific end-use categories, such as showering, basin use, kitchen use, and use by washing machine. However, the analysis of the effect of the sampling interval on the classification accuracy level remains an underinvestigated issue. This article seeks to fill this knowledge gap by identifying an optimal sampling interval that can achieve a high level of classification accuracy while overcoming constraints of on-device data storage, data transmission, and energy consumption. To understand the benefits of collecting fine-grained data for machine learning, we have built a high-resolution tap-based Internet of Things (IoT) system comprising a set of Wi-Fi-based tap sensors, gateway infrastructure, and a secure data processing pipeline. Based on empirical tap-based data collected over an eight-month period, we concluded that when the sampling interval decreases slightly from 5 to 1 s, the accuracy level of the end-use classification model increases significantly from 66.6% to 76.1 %. This article also highlights the challenges of deploying IoT sensors to collect water consumption data in a domestic setting. In order to collect sufficient ground-truth data for the training and verification of a generalizable water end-use disaggregation model, it is necessary to sophisticate the flow data collection system by adopting low-power wide-area network technologies and reducing the level of energy consumption of the flow sensing components.

Project Spaceman: early British computer security and automatic data processing

ABSTRACT Much has been written about the prehistory of ‘cyber’ and computer security in the United States, but we know less about other countries. This essay seeks to examine early British efforts in this field and especially attacks by ‘Tiger Teams’ against ICL mainframe computers that were being deployed by MI5 and other agencies. Over 2 years, a sophisticated programme was completed. It argues that a severe expertise shortage ensured a degree of convergence and later outsourcing to ICL. This pioneering work also paved the way for ‘Project Spaceman’ which offered government highly secure options together with encryption in the mid-1980s.

SeUpdate: Secure Encrypted Data Update for Multi-User Environments

Searchable Symmetric Encryption (SSE) is a key tool for secure data processing. To date, most of the SSEs were studied alone, while an SSE supporting update operations over encrypted data remained a challenging problem due to various statistical attacks and multi-user environments. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SeUpdate</i> , the first SSE scheme that simultaneously achieves keyword search and controlled update over encrypted data, with flexible read (search) and write (update) access control policies among multiple users. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SeUpdate</i> , users do not need to share secret keys and a single query enables one to efficiently search all his authorized data. We formally define a security model, and prove our scheme have both forward and backward security. We note that the write permission of an SSE is realized for the first time. We further extend the basic scheme with dynamic access policy update and support of a large number of files. We also implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SeUpdate</i> and some related work. The theoretical and experimental analyses demonstrate our scheme and its extension are practical and efficient.