Preserving Data Security Research Articles

Application of Simple Additive Weighting Method To Design an Employee Performance Assessment System in a National Logistics Company

This research aims to discover how an employee performance appraisal system using the website-based SAW method can help managers carry out the employee performance appraisal process. The method used in developing a web-based decision-making system is the prototype method. For data collection, two techniques were used. First, obtain the necessary information through interviews. Second, by collecting performance assessment data previously carried out manually by managers. It can be observed that the system may function effectively by performing its functions as an employee performance assessment utilizing the simple additive weighting calculation technique after creating a prototype decision-making system using the website-based simple additive weighting method. After managers have completed an evaluation, website-based solutions can combine data to make it easier to access, preserve data security, and support decision-making. The results provided yield high accuracy based on the SAW formula. This system uses the SAW calculation method so that employee performance assessments are carried out based on the percentage weight of the criteria that the manager has determined. From the requirements and results of this assessment, SAW can produce a ranking of employees with the best to worst performance. From this ranking, the manager can determine the employee with the best and worst performance, and then managers can make decisions based on that data.

BJWT-EHR: A novel JWT based Blockchain System for Electronic Health Records

Since most transactions in the world now take place digitally, it is crucial to maintain data integrity, share data securely, and provide quick access to it. Our reliance on computers in our daily lives is growing, and new data will inevitably be generated to meet this need. Providing for safe data exchange is becoming increasingly crucial every day. Maintaining and safeguarding the security of this data is crucial. Recently, the term "blockchain" has been used extensively for data security. A decentralized digital ledger called blockchain is in charge of preserving data integrity and facilitating safe data sharing. Blockchain transfers data by the principle of transparency, records data one way, and prevents reverse engineering. According to reports, blockchain is the general term for the technology that promotes common data sharing of these recorded data and reduces access times, essential for preserving data security. There are several risks to patients' privacy when information from medical records is stolen or altered. The damage caused by malicious people with this information causes serious harm to both the patient and the financing institution. Therefore, cloud-based EHR (electronic health records) is becoming a focal point in many areas. Storing patient data, ensuring data accuracy, protecting data confidentiality, and ensuring fast data transmission in the cloud system is a significant concerns in the healthcare industry. In this study, the layered architecture structure is also addressed in addition to the blockchain for sharing health data with the help of cloud service providers while providing data access control and auditing for EHR records. To enter this layered architectural system, new solutions are proposed by applying JWT (JSON web token) and hashing function of patient information. This proposed method manages and controls the patient's health records electronically. Therefore, these technologies used in the blockchain platform open new doors to more secure and sustainable solutions.

ShareChain: Blockchain‐enabled model for sharing patient data using federated learning and differential privacy

AbstractEvery individual in our technologically evolved world needs proper data security. The procedure of exchanging medical information is increasingly concerned with data privacy. Many techniques have been offered for preserving data security. These techniques use approaches such as ‐anonymity, ‐diversity, and others. However, such solutions are vulnerable to attribute disclosure, homogeneity, and background knowledge risks due to their syntactic nature. In this work, we describe a safe and secure architecture and semantic approach for data sharing that is based on blockchain, local differential privacy (LDP), and federated learning (FL). The proposed framework generates an atmosphere devoid of trust in which data owners are no longer required to have trust in the controllers. The FL models enable the whole network to decentralize its data‐driven learning. Interplanetary file system (IPFS) is used to provide data security in a distributed environment because each file in IPFS has a digital fingerprint that is computed using a cryptographic hash function on the file's whole contents. Due to the rigorous privacy guarantee, data owners no longer need to be worried about the security of their data. The proposed model's assessment parameters include latency, throughput, privacy, and accuracy. The data privacy of the proposed model is protected via LDP and FL, and its latency and throughput communication transactions on permissioned blockchain are calculated and compared with those of the benchmark model. The findings indicate that the proposed model delivers 85% more accurate privacy than the benchmark model.

Guest Editorial: Security and Privacy of Federated Learning Solutions for Industrial IoT Applications

The Industrial Internet of Things (IoT) typically consists of several thousands of heterogeneous devices, such as sensors, actuators, access points, machinery, end-users' handheld equipment, and supply chain. In such an industrial environment, a multitude of data is generated from massive IoT devices, e.g., sensors for monitoring the environment, reading temperature, and gauging pressure. Most of the data are from delay-sensitive and computation-intensive applications, such as real-time manufacturing and automated diagnostics, which require big data analytics with low latency. Machine learning (ML) has been witnessed as an efficient solution for big data analytics. The majority of such ML algorithms are centralized methods, meaning that they first gather data from different users for use as a training dataset, which is placed on the ML server, and then build a model to classify the new data samples by applying the ML algorithms to this training dataset. However, the access to these datasets in the centralized ML methods raises concerns about data privacy for users. Federated learning (FL) was designed to protect data privacy to address a part of these issues. In FL, each participant uses a global training model without uploading their private data to a third-party server. Compared with the conventional ML, FL can preserve data security, especially in terms of participant data during the learning process. In particular, FL can also help in updating server-side data for the global model, and the participant is not required to provide their data. However, in FL, individual computing units may show abnormal actions, such as faulty software, hardware invasions, unreliable communication channels, and malicious samples deliberately crafting the model. To mitigate these challenges, we require robust policies to control the learning phases in FL. Motivated by the abovementioned issues, this special section solicits original research and practical contributions that advance the security and privacy of the FL solutions for industrial IoT applications as follows.

Preserving Data Security in Cloud Environment Using an Adaptive Homomorphic Blockchain Technique

Preserving Data Security in Cloud Environment Using an Adaptive Homomorphic Blockchain Technique

Federated learning for preserving data privacy in collaborative healthcare research.

Generalizability, external validity, and reproducibility are high priorities for artificial intelligence applications in healthcare. Traditional approaches to addressing these elements involve sharing patient data between institutions or practice settings, which can compromise data privacy (individuals' right to prevent the sharing and disclosure of information about themselves) and data security (simultaneously preserving confidentiality, accuracy, fidelity, and availability of data). This article describes insights from real-world implementation of federated learning techniques that offer opportunities to maintain both data privacy and availability via collaborative machine learning that shares knowledge, not data. Local models are trained separately on local data. As they train, they send local model updates (e.g. coefficients or gradients) for consolidation into a global model. In some use cases, global models outperform local models on new, previously unseen local datasets, suggesting that collaborative learning from a greater number of examples, including a greater number of rare cases, may improve predictive performance. Even when sharing model updates rather than data, privacy leakage can occur when adversaries perform property or membership inference attacks which can be used to ascertain information about the training set. Emerging techniques mitigate risk from adversarial attacks, allowing investigators to maintain both data privacy and availability in collaborative healthcare research. When data heterogeneity between participating centers is high, personalized algorithms may offer greater generalizability by improving performance on data from centers with proportionately smaller training sample sizes. Properly applied, federated learning has the potential to optimize the reproducibility and performance of collaborative learning while preserving data security and privacy.

Effective resource scheduling using hybrid gradient descent cuckoo search algorithm and security enhancement in cloud via blockchain for healthcare 4.0

The Modern Healthcare System (MHS) has become a revolutionized domain over the past decade and has extemporized from version 1.0 to 4.0 with an amalgamation of IoT and its allied technologies. The record sustaining in the MHS is the superlative task in Healthcare 4.0 and the need for maintaining the integrity and resource availability congregate challenges like cyber attacks, lack of resources, etc. However, these challenges can be subdued through augmented resource sharing and blockchain technology. Blockchain technology has proven to be more promising in preserving data security and maintaining the integrity of sensitive data. The conventional healthcare system is slowly cruising towards the modern healthcare domain for the ease of process by the users and for adopting rapid sharing and accessing of healthcare data by the authorized users. Cloud computing offers a major role in modernizing the conventional healthcare system as the patient’s sensitive data were shared and stored in the cloud resources so that to access across the world. Even with the preoccupation of technology like cloud computing, there exists certain challenge like resource availability and data security and to address the aforementioned challenges this paper introduces an integrated model of Hybrid Gradient Decent Cuckoo Search (HGDCS) algorithm for effective resource sharing and Blockchain technology for affording inherent security and high level of integrity for the sensitive data.

A Secure and Lightweight Data Sharing Scheme for Internet of Medical Things

As cloud computing has many advantages such as large storage capacity, low cost and scalability, more and more patients prefer to store their health data in cloud to share with physicians, researchers or other users. However, storing shared data in remote cloud is out of patient's control and exposes to lots of security problems such as privacy and data integrity. So far, more and more data sharing schemes to preserve data security in health field have been put forward, but in most of them, data encryption and decryption are completely implemented by terminal devices, which increases the communication and computation burden of patient and user. Furthermore, most sharing schemes have no integrity verification mechanism, resulting in incomplete data for users to share. To solve the problems, we propose a secure and lightweight data sharing scheme for Internet of Medical Things. Firstly, the scheme guarantees the privacy and authorized access of shared data. Secondly, the scheme realizes efficient integrity verification before user downloads shared data to avoid incorrect query or computation result. Finally, the scheme achieves lightweight operations of patient and user.

Preserving data security in distributed fog computing

In this paper, a novel cryptographic solution is proposed to secure data in fog computing. The solution combines the AES-GMAC operation mode with information dispersal over GF(2w) to provide data confidentiality, integrity, and availability along with source authentication. The value of w is flexible (8, 16, 32 or 64) and it could be configured according to the fog device features. Moreover, the proposed cryptographic solution is based on the dynamic key-dependent approach, which allows for a good compromise between the security level and computational complexity. In the proposed solution, the collected data at one fog node is encrypted, authenticated and dispersed in a pseudo-random manner to its n neighbor fog nodes. For data recovery, any k of the n fragments along with the corresponding dynamic key are required to retrieve the original data. This complicates the attackers task who needs to compromise at least k fog nodes to disclose the encrypted data. Additionally, attackers should seek the dynamic key, which is different for each input data. On the other hand, redundant fragments protect the stored data against up to (n−k) fog nodes’ failure or unavailability. The security and performance analysis tests show that the proposed security scheme exhibits a high level of efficiency and robustness.

Recent advances in cloud data centers toward fog data centers

Recent advances in cloud data centers toward fog data centers

On the Effectiveness of IP-Routable Entire-Packet Encryption Service Over Public Networks (November 2018)

The Internet is an unsecured public network accessed by approximately half of the world population. There are several techniques, such as cryptography, end-to-end encryption, and tunneling, used to preserve data security and integrity and to reduce information theft. This is because the security of data transmission over public networks is an ever-questionable issue. However, none of the above techniques are capable of providing the flexibility of changing either the algorithm or its key at the intermediary routers according to the requirements of stakeholders, e.g., ISPs or Internet users. Although the transmitted data are encrypted and unreadable, the metadata contained in the packet headers are readable during traversal. Nonetheless, service-based Internet architectures, e.g., IoT architectures, demand the analysis the data streams at the intermediary routers to provide smart services such as strengthening the security of the data streams. To this end, this paper proposes a method to use service-oriented routers for providing secure data transmission by encrypting data packets including the header and trailer information. A prototype of the proposed method is implemented on the ns-3 simulator, and this paper discusses the implementation notes and evaluation of the test results. The test results demonstrate that there is only an average processing cost of 180.14/191.35, 213.96/257.41, 157.56/170.68, and 235.48/ $249.49~\mu \text{s}$ for encrypting the total encrypted combined packets/total encrypted separate packets using IDEA, DES, AES-GCM, and AES-CTR encryption algorithms, respectively, with a 256-bit key space. This is significantly lower than the tolerable transmission delay (150 ms) defined by the ITU-T.

Design Perspectives of People Centric Sensing Systems

Mobile devices play a vital role in our day to day life. The various sensing modalities available in mobile devices are identified and justify the growth of the capabilities on today’s smart phones.Taking advantage of the sensing capabilities of the mobile devices paves way for a new era of network, referred as people centric sensing network. In this paper, a broad study about the applications, architectural components and the existing privacy and security architectures available are discussed. The applications of mobile sensing are categorized as personal, social and public sensing based on what they sense, how they share and infer. All these applications clearly depict the roles played by people. People may act as end users, as participants and as application administrators. Since people are in the loop, the basic privacy and security requirements are analyzed that can be realized as the need in the design of any mobile phone sensing systems. The possible threats that arise in this sensing context may be due to both internal and external entities. The strategies and types of adversarial models in the people centric sensing approach provides us light to how the framework works and solutions needed. Existing literature in terms of privacy preserving data aggregation schemes and security Frameworks are also discussed in detail. Applications: The model clearly portraits the importance of people who are no more only passive data users. More focus has to be given on privacy preserving models to make the system more acceptable by the people.

Biobank Semantic Information Management With The Health Intelligence Platform

Introduction/ Background Traditionally Biobanks were mostly used as repositories of cells and tissues. They are addressing scientific questions mainly for retrospective trials where stored tissues and cells can be used. This paradigm has been changing lately. Biobanks can play a different and more prominent role in the scientific process. In fact, the information contained in biobanks can fuel the scientific reasoning itself more than just accompanying it with management of specimens and data. Information management for biobanks should integrate all kinds of information from various sources including clinical records and documents to support flexible retrieval and analytics based on that information. One of the key concepts to support this is to treat the sample like the patient or donor. Selecting a sample for a research trial may be done based on real-time data from the clinical processes. This closes the loop and opens the iron curtain between clinical processes and clinical research. It is a key requirement for supporting research for precision medicine allowing for dynamic decisions and trial designs. Another important aspect is collaboration and sharing of important information preserving data security and patient safety. Informed consent should be dynamically integrated into the scientific process and support patient/donor literacy for the scientific question. Aims More precisely we define requirements for biobank management systems to be: Collaboration and global sharing of data; Support for Scientific workflows and knowledge management; Association of genotype and phenotype data and Integration of patient records; Integration with registries and standards integration; Support for informed consent. Methods We present a biobank architecture based on our product ‘Health Intelligence Platform (HIP)’ that supports these requirements. We describe a way to support scientific reasoning directly with HIP and integrate sample management in a way that it contributes to scientific reasoning and outcome. The NLP semantic information extraction together with information extraction from (semi)structured sources provide a necessary integration to support the link between sample and clinical information. The semantic convergence model of HIP supports semantic queries based on an integrated semantic information base. Selecting samples or running analytics based on this information is possible in real time. The flexible knowledge management supports quick adaption of the knowledge base. Researchers are able to adapt the ontologies by visual interaction with the data sources and semantic tagging. The semantic workbench supports roundtrip engineering of ontologies and conflict resolution. We will present an evaluation framework that compares traditional biobank processes with HIP biobank management. We present the integration capabilities and provide an outlook to allow patient participation in research to address upcoming issues in precision medicine. Conclusion: Biobank management with HIP supports biobank management by providing tools and services for horizontal (across stakeholders, workflows and collaborations) and vertical (across institutions and disciplines) semantic integration of data into one common data model. HIP biobank management supports semantic retrieval of sample data and associated scientific and patient centered information. The HIP Core is used already and is used to identify patients for clinical trials based on information from clinical records and documents.

Trustworthy and effective person-to-person payments over multi-hop MANETs

Due to the rapid development of mobile technologies, nowadays mobile devices are not expensive and almost every person can easily possess a mobile device. This fact boosts investments in mobile applications, among which are the person-to-person mobile payment applications. These applications are pretty sensitive in that they are related to monetary transactions, thus involving strict security and privacy requirements. To this purpose, we propose a secure protocol leveraging online social network connections to help users enforce their trust preferences locally to make a money transfer. The protocol exploits mobile ad-hoc network as a communication means. To improve the network performance by still preserving data security and user privacy, we also propose some optimization strategies to decrease the number of tokensets sent over mobile ad-hoc network. The experimental results demonstrate the effectiveness of our proposal.

Fine-Grained Access Control via XACML Policy Optimization in Cloud Computing

One primary challenge of enforcing access control in cloud computing is how to ensure access with high efficiency while preserving data security. This paper proposes a fine-grained access control method for cloud resources. The basic idea is to use XACML as access control language and to optimize policies by data fragmentation and policy refinement algorithms. Through data fragmentation, the accessible resources are divided into disjoint data blocks, and each of them will be combined with a set of policy rules. This helps to refine the policy and to avoid data leakage caused by rule conflicting on the resource intersections. Finally, the disjoint data blocks and the optimized policy are distributed in the three-layered cloud, and the decision to a request is made by rule matching on a specific resource rather than traversing the whole policy rules. Experiments show that our proposal enjoys higher efficiency in cloud-based access control.

Per-hop Data Encryption Protocol for Transmitting Data Securely Over Public Networks

It is a well-known fact that the Internet traffic travels through public networks. These networks lack security and are vulnerable. Encryption and public key cryptography are important technologies that are used to preserve data security and integrity, and to reduce information theft on the public networks. However, the existing routing protocols are incapable of providing secure data transmission on public networks. To this end, our laboratory introduced the Service-oriented Router (SoR) to maintain rich information for the next-generation networks by shifting the current Internet infrastructure to an information-based and an open-innovation platform. An SoR can analyze all packet stream transactions on its interfaces and store them in high throughput databases. Using the features of the SoR, in this paper, we propose a hop-by-hop routing protocol that provides per-hop data encryption. This infrastructure is proposing to preserve both the security and the privacy of data that traverses through public networks. We implemented a prototype of per-hop data encryption protocol on the ns-3 simulator and the results obtained are discussed in this paper.

Coproduction in the U.S. Department of Defense: Examining How the Evolution of Geographic Information Systems (GIS) Expands Non‐Traditional Partner Engagement

This study examines interactions between the U.S. Department of Defense (DoD) and its non‐traditional partners. Facilitated by advances in technology hardware and software, the nature of collaboration has changed significantly, shifting from authoritarian to symbiotic relationships. We find that DoD partners have expanded to include geographic information systems (GIS) and information technology professionals, and individual GIS users. As more non‐traditional, external partners become involved, the DoD attempts to exert informational power over its partners to assure data integrity and preserve data security. We consider an appropriate role for the DoD in a polycentric regime where public value increasingly is coproduced. This leads us to consider, by analogy, if there are other public policy arenas where data integrity and security concerns will lead to a similar approach.

Analysis of Different Privacy Preserving Cloud Storage Frameworks

Privacy Security of data in Cloud Storage is one of the main issues. Many Frameworks and Technologies are used to preserve data security in cloud storage. [1] Proposes a framework which includes the design of data organization structure, the generation and management of keys, the treatment of change of user’s access right and dynamic operations of data, and the interaction between participants. It also design an interactive protocol and an extirpation-based key derivation algorithm, which are combined with lazy revocation, it uses multi-tree structure and symmetric encryption to form a privacy-preserving, efficient framework for cloud storage. [2] Proposes a framework which design a privacy-preserving cloud storage framework in which he designed an interaction protocol among participants, use key derivation algorithm to generate and manage keys, use both symmetric and asymmetric encryption to hide the sensitive data of users, and apply Bloom filter for cipher text retrieval. A system based on this framework is realized. This paper analyzes both the frameworks in terms of the feasibility of the frameworks, running overhead of the system and the privacy security of the frameworks.

On replacing cryptographic keys in hierarchical key management systems

Shared data access maximizes resource utilization on the Internet but raises the issue of data security. We consider a method of shared data access control whereby the data is sub-divided into categories and each encrypted with a unique cryptographic key that is distributed to the user group requiring access. Key management can be simplified by classifying every user into exactly one of a number of disjoint groups that are partially ordered such that lower level keys are mathematically derivable from higher level keys, but not the reverse. The drawback in this approach is that changes in group membership imply updating both the affected group key and those that are derivable from it. Moreover, the data encrypted with the affected keys must be re-encrypted with the new keys to preserve data security. In the worst case, when the affected group is at the highest level of the hierarchy, the entire hierarchy is affected. This paper presents an algorithm that minimizes the cost of key replacement (rekeying) by associating a timestamp to each key. The timestamp and key are used to compute a verification signature that is used to authenticate users before data access is granted. Thus, whenever group membership changes, instead of rekeying and re-encrypting the affected data, only the timestamp is updated and a new verification signature computed. The new scheme is analyzed using both a time complexity and experimental analysis.

View disassembly: A rewrite that extracts portions of views

We explore a new form of view rewrite called view disassembly. The objective is to rewrite views in order to “remove” certain sub-views (or unfoldings) of the view. This becomes pertinent for complex views which may be defined over other views and which may involve union. Such complex views arise necessarily in environments such as data warehousing and mediation over heterogeneous databases. View disassembly can be used for view and query optimization, preserving data security, making use of cached queries and materialized views, and view maintenance. We provide computational complexity results of view disassembly. One question is whether the unfoldings to be removed effectively cover the view, meaning that the disassembled view is effectively the empty view, evaluating to the empty table. We illustrate the complexity to determine when a collection of unfoldings cover the view definition. The problem is NP- hard with respect to the number of unfoldings to remove, but not with respect to the size (complexity) of the view definition. We next consider rewrites optimal in the size of the rewritten (disassembled) view. We prove that this task is NP- hard for a special class of views, but this time NP- hard in a worse way: it is NP- hard this time with respect to the size of the view definition in addition to the number of unfoldings to be removed. In general, we suspect the problem is computationally even harder, and we show that the general problem is in Π 2 p . However, we provide good news too. We identify a pertinent class of unfoldings for which the removal of such unfoldings always results in a simpler disassembled view than the original view itself. We also develop an algorithm that finds rewrites equivalent to the disassembled view which are, in a sense, locally optimal. The algorithm establishes a cover completion by finding a (minimal) collection of unfoldings of the view that, along with the unfoldings to be removed, covers the original view. This approach is effectively tractable, unlike the search for globally, or absolutely, optimal rewrites. Furthermore, we show that these cover-completion rewrites are preferable to absolutely optimal rewrites in many ways.