Level Of Data Security Research Articles

Perceptions Toward Telemedicine of Health Care Staff in Nursing Homes in Northern Germany: Cross-Sectional Study.

Digitalization in the German health care system is progressing slowly, even though it offers opportunities for improvement of care. In nursing homes, most of the staff's work is paper based. Following the pandemic, there has been a decrease in the use of telemedicine applications. To ensure long-term implementation, the views of users, in this case nurses, are of interest. This cross-sectional study was conducted to describe which digital applications are already being used at inpatient care facilities, the attitude of nurses toward telemedicine, and for which areas the use of telemedicine in the facilities is considered appropriate by the participants. All inpatient care facility staff in Schleswig-Holstein were invited to participate in the survey from August 1 to October 31, 2022. The questionnaire consists of 17 determinants that ask about the attitude, use, and possible applications of telemedicine. In addition to a descriptive analysis, the influence of the general attitude toward telemedicine on various determinants was examined using the Fisher exact test for nominal variables and Spearman correlation coefficient for metric variables. A total of 425 caregivers participated in the survey. Of these respondents, 10.7% (n=41) currently used video consultations, and 76.1% (n=321) of the respondents were in favor of video consultations being practiced in training. Furthermore, 74.8% (n=312) of the respondents would attend a training on telephone medical consultation. Respondents indicated that video consultations have a small added value compared to asynchronous telemedicine (eg, sending photos). However, video consultations were perceived as somewhat less time-consuming than other communication channels. Video consultations are perceived as most useful for clarifying urgent problems. The respondents estimated that one in five paramedic calls at their facilities could be reduced through telemedicine approaches. It was important to the participants that telemedicine is as simple as possible and that there is a high level of data security. Although many caregivers have a positive attitude toward telemedicine and perceive its advantages, communication channels such as video consultation are still used infrequently in care facilities. To promote the use of telemedicine applications, it is important to emphasize their benefits. The presumed saving of paramedic calls thus represents a benefit, and it is crucial to train caregivers in the use of telemedicine to avoid uncertainties in dealing with the newer technologies. It is important to give them enough time and repetitions of the training.

Strategic integration of cyber security in business intelligence systems for data protection and competitive advantage

In the current world, business intelligence systems play a crucial role in guiding organizations on the best strategies to adopt depending on the analytical information and results obtained (Ahmad et al., 2020). Nonetheless, they are being used frequently and, hence, are vulnerable to ransom attacks, requiring appropriate security measures. This study examines the opportunities for BI solution providers to implement cybersecurity, mitigate risks, and gain competitive advantage. The study adopts a mixed-methods approach to the research involving a literature review study, self-complete questionnaires, interviews, and case studies among a variable of industry professionals. The observation in this research process shows that incorporating enhanced encryption methods, two-factor verification, monitoring, and security culture advancements leads to increased data security levels. Furthermore, apart from defending an organization from cyber-threats, properly implementing cyber security in BI systems ensures that an organization acquires better customer trust, compliance with regulations, and a competitive edge in the market. This paper outlines vital tactics and best practices businesses can apply to enhance, design, and implement robust cybersecurity strategies for organizational benefit. This research aims at the following question; How can cybersecurity be integrated in Business Intelligence (BI) systems considering the facts that businesses are adopting data-driven strategies in the current century? Since more organizations opt for BI systems to collect pertinent information and generate strategic decisions, the systems named remain vulnerable to grabs and threats. Similarly, the research on the role of cyber security is a prime example to show how it need not to be a risk factor but in fact could be a key strength.

Integrity and Privacy Assurance Framework for Remote Healthcare Monitoring Based on IoT

Remote healthcare monitoring (RHM) has become a pivotal component of modern healthcare, offering a crucial lifeline to numerous patients. Ensuring the integrity and privacy of the data generated and transmitted by IoT devices is of paramount importance. The integration of blockchain technology and smart contracts has emerged as a pioneering solution to fortify the security of internet of things (IoT) data transmissions within the realm of healthcare monitoring. In today’s healthcare landscape, the IoT plays a pivotal role in remotely monitoring and managing patients’ well-being. Furthermore, blockchain’s decentralized and immutable ledger ensures that all IoT data transactions are securely recorded, timestamped, and resistant to unauthorized modifications. This heightened level of data security is critical in healthcare, where the integrity and privacy of patient information are nonnegotiable. This research endeavors to harness the power of blockchain and smart contracts to establish a robust and tamper-proof framework for healthcare IoT data. Employing smart contracts, which are self-executing agreements programmed with predefined rules, enables us to automate and validate data transactions within the IoT ecosystem. These contracts execute automatically when specific conditions are met, eliminating the need for manual intervention and oversight. This automation not only streamlines the process of data processing but also enhances its accuracy and reliability by reducing the risk of human error. Additionally, smart contracts provide a transparent and tamper-proof mechanism for verifying the validity of transactions, thereby mitigating the risk of fraudulent activities. By leveraging smart contracts, organizations can ensure the integrity and efficiency of data transactions within the IoT ecosystem, leading to improved trust, transparency, and security. Our experiments demonstrate the application of a blockchain approach to secure transmissions in IoT for RHM, as will be illustrated in the paper. This showcases the practical applicability of blockchain technology in real-world scenarios.

Impact of quantum technologies on innovation processes in the global economy

The article examines the impact of quantum technologies on innovation processes in the global economy. It analyzes the critical aspects of integrating quantum technologies in various sectors of the economy, particularly in the financial, health care, production, and IT spheres. Theoretical models and empirical data confirming how quantum technologies can accelerate innovation processes, increase research efficiency, and create new economic opportunities are considered. The article also provides practical recommendations for strategic planning and management of innovation processes in the conditions of the quantum revolution. Special attention is paid to the importance of an interdisciplinary approach and cooperation between business and the state. The author noted that the modern economy is on the threshold of a new era, where quantum technologies play a decisive role, which represents a new class of tools capable of radically changing traditional approaches to data processing, security, and computing. Quantum computers, quantum cryptography and quantum communication open opportunities for the economy that can accelerate the development of new materials, optimize logistics chains and increase the level of data security. At the same time, the article highlights the need to develop global standards in the field of quantum technologies. The results of the research will contribute to a better understanding of the potential of quantum technologies to stimulate innovation and increase the competitiveness of the economy. Keywords: quantum technologies, global economy, innovation processes, quantum cryptography, quantum computing, quantum modeling, quantum communication, economic models, quantum algorithms, cyber security, logistics optimization, risk management, economic growth, financial markets, health care, production, quantum networks, development strategies, digital economy, technological revolution.

On the Evaluation Framework of Comprehensive Trust for Data Interaction in Intermodal Transport

Due to the necessity to realize “building a strong transportation nation”, the construction of intermodal transportation is based on the information resource integration of diverse transport systems. To ensure the data security during the interaction between different transport modes, as well as the effect of data application, the status of entities and data flow in the network should be supervised throughout. Therefore, an evaluation framework of comprehensive trust is proposed in this paper. With feature analysis of transportation big data, a quality assessment is conducted by three-dimensional metric sets, which is considered as a significant factor of trust measurement. Furthermore, a hierarchical trust structure is put forward to assess the trust of entities in different levels, in terms of the static and dynamic evidence. Furthermore, the visualization of a dynamic global information security state is discussed, based on temporal knowledge graphs. As shown in practical application and simulation analysis, this framework can meet the requirements of data security supervision and lay the foundation of further intelligent management. This research is of great significance to improve the data security level in intermodal transport, and to promote the utilization and sharing of public information resources.

Future prospects: AI and machine learning in cloud-based SIP trunking

The relevance of the study lies in the consideration of artificial intelligence and machine learning as one of the most important technologies that determine the future of the telecommunications industry. Integration of artificial intelligence and machine learning into cloud-based Session Initiative Protocol trunking solutions can potentially significantly improve the efficiency, performance, and security of these solutions. The purpose of the study was to analyse the possibilities of integrating artificial intelligence and machine learning in cloud-based Session Initiation Protocol trunking solutions. The analysis and the case study methods were applied. The study found that in the modern world, artificial intelligence and machine learning can no longer be considered separately from many aspects of human activity. These technologies are widely used in the telecommunications sector. The integration of artificial intelligence and machine learning in this sector is a key to solving various problems. The findings underline that artificial intelligence and machine learning have the potential to significantly improve the efficiency, performance, and security of cloud-based Session Initiation Protocol trunking solutions. In particular, it was found that these technologies can be successfully used for intelligent call routing, optimising resource allocation, and providing a higher level of security. The results of the study are an important contribution to improving intelligent call routing, optimising resource allocation, and improving the level of security for data and network protection. In addition, the results of the study have the potential to increase the competitiveness of telecommunication companies and ensure the sustainable development of this industry

Security of Employee Salary Data Using the ElGamal Algorithm By Utilizing the Diffie-Hellman Algorithm Key Generator

Data security is something that is very important for companies or organizations, one of which is the South Binjai District Office. Almost all work in the South Binjai sub-district office uses a computer, especially in processing data stored in the form of data files on storage media without encoding. In today's digital era, many irresponsible parties find it easy to tap company data so that it can be easily accessed and there is also some software that can be easily used to crack passwords. One of the data that needs to be kept confidential is employee salary data. To overcome these problems, a security system is needed to secure text data. The cryptographic method is regarding encryption techniques in which plaintext is scrambled using an encryption key to become ciphertext which is applied in data security systems. The Diffie-Hellman algorithm is a key exchange algorithm, this algorithm is limited to the key exchange process, so it must require another algorithm for the encryption and decryption process such as the ElGamal algorithm. The use of these two algorithms can strengthen the level of data security. From the results of trials conducted, the sample data used containing the word "KARYAWAN" was successfully secured with both diffie-helman and ElGamal algorithms.

Rewritable Photoluminescence and Structural Color Display for Dual-Responsive Optical Encryption.

Optical encryption using coloration and photoluminescent (PL) materials can provide highly secure data protection with direct and intuitive identification of encrypted information. Encryption capable of independently controlling wavelength-tunable coloration as well as variable light intensity PL is not adequately demonstrated yet. Herein, a rewritable PL and structural color (SC) display suitable for dual-responsive optical encryption developed with a stimuli-responsive SC of a block copolymer (BCP) photonic crystal (PC) with alternating in-plane lamellae, of which a variety of 3D and 2D perovskite nanocrystals is preferentially self-assembled with characteristic PL, is presented. The SC of a BCP PC is controlled in the visible range with different perovskite precursor doping times. The perovskite nanocrystals developed in the BCP PC are highly luminescent, with a PL quantum yield of ≈33.7%, yielding environmentally stable SC and PL dual-mode displays. The independently programmed SC and PL information is erasable and rewritable. Dual-responsive optical encryption is demonstrated, in which true Morse code information is deciphered only when the information encoded by SCs is properly combined with PL information. Numerous combinations of SC and PL realize high security level of data anticounterfeiting. This dual-mode encryption display offers novel optical encryption with high information security and anti-counterfeiting.

Music teaching software development based on neural network algorithm and user analysis

With the continuous development of digital technology, music teaching is gradually shifting from traditional face-to-face teaching to online teaching and intelligent learning methods. Developing music teaching software plays an important role in improving users’ music learning methods and learning experience. However, current music teaching software still has issues such as poor compatibility, low level of data security and privacy protection, and functional requirements that are still in the early stages. This article aimed to research and develop a music teaching software based on neural network algorithm and user analysis, so as to improve user satisfaction with music teaching software. In the article, the overall design module of the system was first analyzed, and then music teaching resource data was collected and normalized using clustering algorithms to improve the effectiveness of music teaching resource data. Afterwards, a BP (Back Propagation) neural network model with parallel structure was used to optimize the design of the neural network model, and the effectiveness of the neural network model in user interface design was analyzed. Finally, user behavior analysis and personalized recommendation were achieved by constructing a user profile feature dataset. In order to verify the performance of the developed music teaching software, the software performance and security were tested. The results showed that the required loading time for the music score data in test case 1 of this article was only 1.21 s, and the response time was only 0.015 s. At this time, the security was 92.6%; the required loading time for audio data was only 1.06 s. The response time was only 0.019 s, and the security was 88.7%; the loading time required for user interaction data was only 0.78 s. The response time was only 0.012 s, and the safety was 91.3%. The research results indicated that the music teaching software developed in this article could better meet users’ music teaching needs and provide users with a more intelligent, personalized, and efficient music learning experience.

Implementasi Algoritma Enkripsi Pada Sistem Informasi Manajemen Helpdesk Berbasis Desktop

This paper discusses the implementation of encryption algorithms in a desktop-based Helpdesk Management Information System developed specifically for IT teams in private hospitals. IT teams in private hospitals often face complex challenges due to the large hospital area and limited resources. Creating a Helpdesk application using Delphi and a MySQL database is very important to provide efficient technical support. One of the key aspects of this application is the interactive chat feature, which is built using a server-client socket system. In this communication context, encryption algorithms are needed to ensure the security of data transmitted between the IT team and system users. This helps protect sensitive patient data and technical information from the potential risk of leakage. The scope of this Helpdesk application includes ticket management, routine checks, maintenance, tool rotation, and various other support The implementation of encryption algorithms in this application effectively increases the level of data security in all operational processes. The helpdesk application was built using the Extreme Programming (XP) method.
 Keywords: Encryption algorithm, AES-256, Helpdesk Management Information System, Delphi, MySQL.

Designing and implementing an information steganography system for transmitting encrypted text messages in digital images

This danger can undoubtedly be eliminated using cryptography and steganography. And Combining encryption with steganography can assist in achieving high levels of data security, making this approach superior to using either of these techniques alone to send data across an unreliable route that is open to hacking. Visible steganography is one of the most secure steganography techniques. When the data text is inserted, the image's color frequency may vary slightly, which would be highly evident to anyone. We suggest levels of data security to get around the image's apparent behavior, where the language of the data is first transformed into an unintelligible cipher. An additional degree of protection is added by incorporating the cipher into an encrypted picture file before dividing it into shares. As a result, the ideas of steganography and cryptography are both applied to offer two levels of security and then a visual cryptography system is used to partition the picture into shares so that it may be transferred over a network channel.

Torus Ring Signcrypted Kademlia Hash Storage-Based Cryptography For Secured Data Storage In Fog Computing

Fog computing is decentralized computing infrastructure which used to present various services. Generally, fog network includes numerous data, computation, storage and application processes among data in the server. The fog node toward large-scale end-users offers a variety of interacted IoT services. Security is main concern for preserving data from unauthorized user access as carry outing secure data storage from fog user as well as server. Thus, secure data storage in fog is a demanding and rising concern for Fog-IoT security paradigms. Several research works have developed for secured fog data storage. But, data security level was minimum because performance of signcryption was not effectual. To overcome such problems, Torus Ring Signcrypted Kademlia Hash Storage (TRSKHS) method is proposed in fog computing. The designed TRSKHS method contains key generation, encryption as well as decryption for enhancing fog data storage by lesser computational cost. At first, registration process is carried to register fog users detail to the fog server. After registering, fog server generates pair of keys namely public key and public key for every registered user using Torus Ring Signcryption. Then, the fog user carry outs encryption as well as signature generation process. Initially, fog user data is encrypted in ciphertext as well as signature is generated for each data to provide secured data storage. Before storing encrypted data in server, hash value of each data is generated by using Kademlia Hash function. Then, encrypted user data through their hash value is stored in database. Finally, the decryption and signature verification is performed by the user to decrypt data. After that authorized users attain original data and enhance secure data storage. Through data accessing process, fog data is decrypted by their generated private key. This assists for TRSKHS method to enhance secure data storage by lesser amount of encryption time. Experimental assessment of TRSKHS method and conventional methods are performed with number of fog users as well as data. Proposed TRSKHS method carries the experimental evaluation using encryption time, storage complexity, data confidentiality rate as well as data integrity rate.

COMPARATIVE STUDY OF ELGAMAL AND LUC ALGORITHM IN CRYPTOGRAPHIC KEY GENERATION

In today’s era of digital, data security in communication channel becomes important factor to be considered during exchange of information. Cryptography is one of techniques to send and receive information securely through an insecure channel. Based on the number of keys used, encryption methods are categorized as symmetric and asymmetric cryptography. Compared to symmetric cryptography that often suffers from key management issues, asymmetric cryptography delivers higher level of data security. Thus, asymmetric cryptography is more preferred when security if the priority. To determine suitable algorithm, three essential aspects should be considered: security, speed, and prime numbers. This study aims to compare the application of asymmetric cryptographic algorithms between ElGamal and LUC algorithms in the key generation process. A comparative analysis of these two algorithms was conducted by evaluating the processing speed and prime numbers during key generation process to determine the advantages and drawbacks from ElGamal and LUC algorithms. The application in this study was developed using PHP programming language by following the Waterfall Model. Application testing involved two kinds of tests: (i) Black Box test and (ii) System Usability Scale (SUS) test. Results show the application developed from this study successfully performed the encryption, decryption, and checking of prime numbers from ElGamal and LUC algorithms. It displayed ciphertext, plaintext, and the speed of the encryption and decryption process from both methods. The black box test showed that all application functions follow the user's needs, while System Usability Scale (SUS) test obtained an average score of SUS interpretation of 83.75. This value means the adjective ratings was “excellent”, the grade scale was “B”, and the acceptability range was “acceptable”. It is concluded that the LUC algorithm superior to ElGamal in terms of the speed of encryption process. However, during the decryption process the LUC algorithm responded slower than ElGamal.

An Efficient and Secure Fog Based Routing Mechanism in IoT Network

The Internet of Things (IoT) networks are the most prone to internal as well as external attacks in addition to energy consumption problems. Conventional security solutions are not able to address these issues effectively due to the limited resources of sensor nodes participating in IoT communications. In this work, an Efficient and Secure Fog Based Routing Mechanism (ESFRM) is proposed to protect the network from faulty internal as well as external attacks. Every node participating in IoT communications calculates the comprehensive trust value of the next intermediate node which is the addition of direct trust, indirect trust and energy trust values before forwarding the data. This comprehensive trust value is then compared with the comprehensive threshold trust value to decide whether the particular node is a rogue node or a valid normal node. Further, an enhanced RSA (Rivest, Shamir, Adleman) algorithm is implemented to provide three levels of data security from Cluster Head (CH) to fog node, from fog node to cloud server and directly from CH to cloud server. For this purpose, an efficient CH selection technique is also proposed in this work. The proposed methodology is compared with the Secure Energy-efficient Fog-based Routing (SEFR) protocol and Trust-aware Secure Routing Protocol (TSRP). The evaluation results show that the proposed ESFRM outperforms the conventional schemes with respect to energy consumption, malicious node detection and transmission rate.

Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning.

Can machine learning predict the number of oocytes retrieved from controlled ovarian hyperstimulation (COH)? Three machine-learning models were successfully trained to predict the number of oocytes retrieved from COH. A number of previous studies have identified and built predictive models on factors that influence the number of oocytes retrieved during COH. Many of these studies are, however, limited in the fact that they only consider a small number of variables in isolation. This study was a retrospective analysis of a dataset of 11,286 cycles performed at a single centre in France between 2009 and 2020 with the aim of building a predictive model for the number of oocytes retrieved from ovarian stimulation. The analysis was carried out by a data analysis team external to the centre using the Substra framework. The Substra framework enabled the data analysis team to send computer code to run securely on the centre's on-premises server. In this way, a high level of data security was achieved as the data analysis team did not have direct access to the data, nor did the data leave the centre at any point during the study. The Light Gradient Boosting Machine algorithm was used to produce three predictive models: one that directly predicted the number of oocytes retrieved and two that predicted which of a set of bins provided by two clinicians the number of oocytes retrieved fell into. The resulting models were evaluated on a held-out test set and compared to linear and logistic regression baselines. In addition, the models themselves were analysed to identify the parameters that had the biggest impact on their predictions. On average, the model that directly predicted the number of oocytes retrieved deviated from the ground truth by 4.21 oocytes. The model that predicted the first clinician's bins deviated by 0.73 bins whereas the model for the second clinician deviated by 0.62 bins. For all models, performance was best within the first and third quartiles of the target variable, with the model underpredicting extreme values of the target variable (no oocytes and large numbers of oocytes retrieved). Nevertheless, the erroneous predictions made for these extreme cases were still within the vicinity of the true value. Overall, all three models agreed on the importance of each feature which was estimated using Shapley Additive Explanation (SHAP) values. The feature with the highest mean absolute SHAP value (and thus the highest importance) was the antral follicle count, followed by basal AMH and FSH. Of the other hormonal features, basal TSH, LH, and testosterone levels were similarly important and baseline LH was the least important. The treatment characteristic with the highest SHAP value was the initial dose of gonadotropins. The models produced in this study were trained on a cohort from a single centre. They should thus not be used in clinical practice until trained and evaluated on a larger cohort more representative of the general population. These predictive models for the number of oocytes retrieved from COH may be useful in clinical practice, assisting clinicians in optimizing COH protocols for individual patients. Our work also demonstrates the promise of using the Substra framework for allowing external researchers to provide clinically relevant insights on sensitive fertility data in a fully secure, trustworthy manner and opens a number of exciting avenues for accelerating future research. This study was funded by the French Public Bank of Investment as part of the Healthchain Consortium. T.Fe., C.He., J.C., C.J., C.-A.P., and C.Hi. are employed by Apricity. C.Hi. has received consulting fees and honoraria from Vitrolife, Merck Serono, Ferring, Cooper Surgical, Dibimed, Apricity, and Fairtility and travel support from Fairtility and Vitrolife, participates on an advisory board for Merck Serono, was the founder and organizer of the AI Fertility conference, has stock in Aria Fertility, TMRW, Fairtility, Apricity, and IVF Professionals, and received free equipment from Planar in exchange for first user feedback. C.J. has received a grant from BPI. J.C. has also received a grant from BPI, is a member of the Merck AI advisory board, and is a board member of Labelia Labs. C.He has a contract for medical writing of this manuscript by CHU Nantes and has received travel support from Apricity. A.R. haș received honoraria from Ferring and Organon. T.Fe. has received a grant from BPI. N/A.

Conventional Non-Fluorescent Polymers: Unconventional Security Inks for Data Storage and Multidimensional Photonic Cryptography.

Traditional security inks relying on fluorescent/phosphorescent molecules are facing increasing risks of forgery or tampering due to their simple readout scheme (i.e., UV-light irradiation) and the advancement of counterfeiting technologies. In this work, a multidimensional data-encryption method based on non-fluorescent polymers via a "lock-key" mechanism is developed. The non-fluorescent invisible polymer inks serve as the "lock" for data-encryption, while the anti-rigidochromic fluorophores that can distinctively light up the polymer inks with programed emissions are "keys" for decryption. The emission of decrypted data is prescribed by polymer chemical structure, molecular weight, topology, copolymer sequence, and phase structure, and shows distinct intensity, wavelength, and chirality compared with the intrinsic emission of fluorophores. Therefore, the data is triply encrypted and naturally gains a high-security level, e.g., only one out of 20000 keys can access the only correct readout from 40000000 possible outputs in a three-polymers-based data-encryption matrix. Note that fluorophores lacking anti-rigidochrimism cannot selectively light up the inks and fail in data-decryption. Further, the diverse topologies, less well-defined structures, and random-coiled shapes of polymers make it impossible for them to be imitated. This work offers a new design for security inks and boosts data security levels beyond the reach of conventional fluorescent inks.

Literature Review: Clinical Data Interoperability Models

A medical entity (hospital, nursing home, rest home, revalidation center, etc.) usually includes a multitude of information systems that allow for quick decision-making close to the medical sensors. The Internet of Medical Things (IoMT) is an area of IoT that generates a lot of data of different natures (radio, CT scan, medical reports, medical sensor data). However, these systems need to share and exchange medical information in a seamless, timely, and efficient manner with systems that are either within the same entity or other healthcare entities. The lack of inter- and intra-entity interoperability causes major problems in the analysis of patient records and leads to additional financial costs (e.g., redone examinations). To develop a medical data interoperability architecture model that will allow providers and different actors in the medical community to exchange patient summary information with other caregivers and partners to improve the quality of care, the level of data security, and the efficiency of care should take stock of the state of knowledge. This paper discusses the challenges faced by medical entities in sharing and exchanging medical information seamlessly and efficiently. It highlights the need for inter- and intra-entity interoperability to improve the analysis of patient records, reduce financial costs, and enhance the quality of care. The paper reviews existing solutions proposed by various researchers and identifies their limitations. The analysis of the literature has shown that the HL7 FHIR standard is particularly well adapted for exchanging and storing health data, while DICOM, CDA, and JSON can be converted in HL7 FHIR or HL7 FHIR to these formats for interoperability purposes. This approach covers almost all use cases.

Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey

In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end, preserving privacy is of great importance in protecting sensitive information. In the era of big data, the relationships among data entities have become unprecedentedly complex, and more applications utilize advanced data structures (i.e., graphs) that can support network structures and relevant attribute information. To date, many graph-based AI models have been proposed (e.g., graph neural networks) for various domain tasks, like computer vision and natural language processing. In this paper, we focus on reviewing privacypreserving techniques of graph machine learning. We systematically review related works from the data to the computational aspects. We rst review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information (e.g., graph model parameters) to realize the optimization-based computation when data sharing among multiple parties is risky or impossible. In addition to discussing relevant theoretical methodology and software tools, we also discuss current challenges and highlight several possible future research opportunities for privacy-preserving graph machine learning. Finally, we envision a uni ed and comprehensive secure graph machine learning system.

Lanthanide-hydrogel with reversible dual-stimuli responsive luminescent switching property for data protection

The development of data protection materials with low cost is still challenging in the era of big data and artificial intelligence. Herein, the luminescent hydrogel with luminescent switching property was prepared by the introduction of lanthanide complex into poly(vinyl alcohol) gelation matrix. The developed luminescent hydrogel showed good reversible luminescent switching behavior under the stimulation of HCl/NH3 and Fe3+/EDTA2-, respectively. It enables the hydrogel to be applied as reversible data security storage material with low cost. The results showed that the input and erasure of data was reversible. Moreover, the fabricated data was invisible under daylight whereas it was visible under UV light. It is suitable for data protection. Furthermore, the combination of dual-stimuli was demonstrated to improve the security level of data effectively. The hydrogel also exhibited good stability against UV irradiation. The findings suggested that the stimuli-responsive luminescent hydrogel has potential application in data protection.

Secure cloud data storage solution with better data accessibility and time efficiency

The environment of cloud computing provides several advantages and a variety of data storage models, which entirely frees users from the vexing processes of storage equipment upgradation and data administration. Nevertheless, the customers’ primary worry is the safety of their data, and the literature offers a number of different security-based solutions for addressing this issue. This paper proposes a technique for time-efficient cloud data storage that makes use of keccak and Advanced Encryption Standard (AES) which enhances the data availability with a reasonable storage space. Third party agents are not employed, due to the fact that a third party cannot be relied upon to maintain a high level of data security. Thus, the proposed algorithm is applied by cloud users before the process of outsourcing is carried out. This method is resistant to data tamper and analytical attacks. In addition, the execution of the work that has been proposed requires a limited amount of time, which, in turn, minimizes the amount of energy that is required. In contrast to the already used algorithms, the presented work demonstrates superior performance in terms of its overall effectiveness.