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Cloud computing for utility-based services has emerged rapidly in recent years. Characteristics of cloud computing like on-demand network access, ubiquitous access to a shared pool of computing resources, pay as you go model, scalability gave popularity over traditional computing technologies. Now, by moving to the cloud environment, organizations can focus on quality of services by saving cost and time in infrastructure setup. Although the features of cloud computing empower the users, but few features like scalability, multitenancy leads threat to confidentiality and integrity. As a user of cloud does not have administrative control over physical resources and client data resides on vendor's premises, threat to confidentiality, integrity, and privacy may lead to customers to think before switching to a cloud environment. In this paper, we investigate flexible distributed storage integrity auditing mechanism, utilizing the homomorphic token and distributed erasure-coded data.

Although P-tensor product compressed sensing (PTP-CS) breaks the strict dimension matching restriction between measurement matrix and signal in multiplication, it still faces the huge resource consumption problems of the frequent update and transmission of measurement matrix and signal reconstruction. Accordingly, we design a public measurement matrix and utilize the cloud to solve the PTP-CS reconstruction (PTP-CSR) task under privacy protection.Specifically, we propose a low complexity and secure PTP-CSR outsourcing model to protect the signal privacy, and further introduce user authentication and data verification services. In our model, the client samples the signal based on PTP-CS and uploads the encrypted measurement to the cloud. The cloud further encrypts the data asymmetrically for storage security and user management. After receiving the request, the cloud authenticates the user's identity. Once successful, the cloud processes the encrypted PTP-CSR task and returns result to users. More importantly, we provide result verification approach for users. Our experimental results demonstrate the privacy protection of the signal and the effectiveness of the proposed model.

In the digital world, systems are usually distributed, where health data are fragmented across multiple healthcare providers’ data may be accidentally corrupted or intentionally tampered. A mechanism is important to verify the integrity of the content in the network to ensure the security. The main objective of this paper is to propose a Modified Merkle Tree (MMT) data structure for data storage and data authentication. In this paper, a new Modified Merkle Tree data structure that can ensure storage security with integrity management is proposed. The Merkle Tree data structure, utilized in Blockchain technology is modified in this work for the purpose of designing a new three-stage Modified Merkle Tree based on hashed transactions. The objective of the proposed hashed transaction is to verify the authenticity and integrity of the content. The use of the Modified Merkle Tree data structure is highlighted in designing a system that takes care of Data Authentication, Consistency Verification and Data Synchronization. The effectiveness of the proposed system has been experimented in the healthcare scenario and proved to store and verify the content integrity. The quantitative metrics of the proposed system have been evaluated and compared with the binary Merkle Tree data structure.

Carbon dioxide (CO2) storage security is a key issue in CO2 geological storage (CGS). A three-dimensional (3D) conceptual reservoir model of the Shenhua CO2 geological storage site in the Ordos Basin has been used to investigate the impact of reservoir formation dip and the influence of enhanced permeability fault zones on CO2 storage and migration security. A total of 8 simulations were carried out using the TOUGH2 integral finite difference modelling code with the ECO2N fluid property module. The simulation results showed that the dip of the reservoir formation and fault had a significant impact on CO2 migration and storage security. Increasing the dip of the reservoir increased CO2 migration distance, decreased the total volume of CO2 safely stored in the formation and resulted in increased maximum gas saturation and liquid mass fraction of dissolved CO2. The presence of fault provided a channel for CO2 leakage and caused an irregular distribution of formation pressure. The onset time of leakage through the fault proved to be a function of formation dip, occurring at 465, 230, and 160 years following commencement of CO2 injection for dips of 5°, 10°, and 15° respectively. The lateral extent of both the high saturation CO2 plume and the plume of dissolved CO2 was greater in the steeply dipping faulted reservoir model, suggesting that gently dipping un-faulted reservoir formations should be selected for future CGS projects in the Ordos Basin.

Carbon capture, utilization and storage (CCUS) is considered as a very important technology for mitigating global climate change. Carbon dioxide (CO2) injected into an underground reservoir will induce changes in its physical properties and the migration of CO2 will be affected by many factors. Accurately understanding these changes and migration characteristics of CO2 is crucial for selecting a CCUS project site, estimating storage capacity and ensuring storage security. In this paper, the basic principles of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) technologies are briefly introduced in the context of laboratory experiments related to CCUS. The types of NMR apparatus, experimental samples and testing approaches applied worldwide are discussed and analyzed. Then two typical NMR core analysis systems used in CCUS field and a self-developed high-pressure, low-field NMR rock core flooding experimental system are compared. Finally, a summary of the current deficiencies related to NMR applied to CCUS field is given and future research plans are proposed. Keywords: Geologic carbon storage, Nuclear magnetic resonance (NMR), Core flooding, Experimental apparatus, Low-field NMR, High-field NMR

Nowadays, it is very important to maintain a high level security to ensure safe and trusted communication of information between various organisations. But secured data communication over the Internet and any other network is always under threat of intrusions and misuses. So intrusion detection system (IDS) has become a needful component in terms of computer and network security. In this research, the authors have intended to propose an effective method for text data based IDS and secure data storage. In the proposed preprocessing steps, the input text document is preprocessed and then change to the desired format. Next the resultant output is fed to the IDS. Here user text data is checked; whether the given data is normal or intrusive based on a modified artificial neural network (MANN). Here traditional neural network is modified by means of modified particle swarm optimisation. The final process of the authors' proposed method is to encrypt the file using dual encryption algorithms (RSA and AES). To improve the storage security of the proposed method, steganography techniques are utilised after the dual encryption. Their proposed system is implemented with the help of Cloud simulator in the working platform Java.

To meet the Paris Agreement target of limiting global warming to 2ºC or below it is widely accepted that Carbon Capture and Storage (CCS) will have to be deployed at scale. The influence of residual trapping on CO2 well injectivity and its response over time has a major impact on the injection efficiency and storage capacity of CO2 storage sites. For the first time, experiments have been undertaken over six cycles of water and supercritical CO2 injection using a state of the art high flow rig recreating in-situ conditions of near wellbore injection into analogue storage reservoir rocks. The results show that differential pressure continuously increases over multiple injection cycles. Our interpretation is that multiple cycles of injection results in a reduced effective permeability due to increased residual trapping acting as a barrier to flow resulting in reduced injectivity rates. This is supported by numerical modelling and field observations that show CO2 injectivity and its response over time will be affected by multiple cycles of injection. These results suggest that loss of injectivity must be incorporated into the injection strategy and that careful management of cyclic injection will create the opportunity to increase residual trapping.

Encryption protects sensitive data from unauthorized access, yet is not sufficient when users are forced to surrender keys under duress. In contrast, plausible deniability enables users to not only encrypt data but also deny its existence when challenged. Most existing plausible deniability work (e.g. the successful and unfortunately now-defunct TrueCrypt) tackles “single snapshot” adversaries, and cannot handle the more realistic scenario of adversaries gaining access to a device at multiple time points. Such “multi-snapshot” adversaries can simply observe modifications between snapshots and detect the existence of hidden data. Existing ideas handling “multi-snapshot” scenarios feature prohibitive overheads when deployed on practically-sized disks. This is mostly due to a lack of data locality inherent in certain standard access-randomization mechanisms, one of the building blocks used to ensure plausible deniability. In this work, we show that such randomization is not necessary for strong plausible deniability. Instead, it can be replaced by a canonical form that permits most of writes to be done sequentially. This has two key advantages: 1) it reduces the impact of seek due to random accesses; 2) it reduces the overall number of physical blocks that need to be written for each logical write. As a result, PD-DM increases I/O throughput by orders of magnitude (10–100× in typical setups) over existing work while maintaining strong plausible deniability against multi-snapshot adversaries. Notably, PD-DM is the first plausible-deniable system getting within reach of the performance of standard encrypted volumes (dm-crypt) for random I/O.

In recent years, big data in education has become an important force to promote educational reform. However, the current research at home and abroad mainly focuses on how to use big data in education and pays insufficient attention to the information security problems it faces, which may lead to information security risks. The birth of cloud computing technology provides favorable conditions for the deployment of big data in education, but also brings challenges to the security of big data in education. Based on this, this paper studies the current education big data information security problems, find the existing problems, and targeted puts forward education under the network environment big data security policies, including storage security, access control, security strategies and virtual safety security strategy, and establish a safety technical framework, using standard and safety supervision system as the main content of the guarantee system, ensure the safety of the big data cloud environment education. The research results show that the network is a developing technology, so security problems often occur in the network environment. Only by formulating strict education big data security strategies and effectively ensuring the security of education big data, can education big data be rapidly developed and the legitimate rights and interests of users of education resources and the healthy development of big data be ensured.

With the rapid growth in the sector of information technology and easy access to cheap and advanced office instruments in the market, the faking of important documents has become a matter of concern nowadays. Therefore, the need for verification and authentication practices of various important documents in the form of banking documents, government documents, transaction documents, educational certificates etc is also increasing. However, various challenging and tedious processes have made document verification very complex and time-consuming which motivated us to conduct this research. In this paper, we present a decentralized web application for digital document verification using Ethereum blockchain-based technology in P2P cloud storage to enhance the verification process by making it more open, transparent, and auditable. The proposed model includes several methods such as public/private key cryptography, online storage security, digital signatures, hash, peer-to-peer networks and proof of work which has made the verification of any uploaded documents for any organization or authority faster and convenient with just a click. Furthermore, respective hash values are also assigned to each individual document. Our proposed model successfully meets up all the criteria for a digital document verification system by alleviating the gaps and difficulties in the traditional methods in document verification.

The CarbonNet Project is operated by the state of Victoria, and jointly funded and managed by State and Federal governments. The Pelican storage site is situated in the shallow marine environment, between 1 to 15 km offshore from the iconic Ninety Mile Beach in Victoria, Australia. The site requires careful and diligent appraisal to demonstrate its suitability for safe and environmentally-benign permanent storage of CO2 at commercial scale. A 125 million tonne storage site has now been confirmed using new state-of-the-art high-resolution 3D seismic data covering the P90 probabilistic 3D volume of future plume travel in a wide range of scenarios and sensitivities. A new appraisal well (data well) has recently been completed, confirming pressure isolation across the main seal interval and suitable sandstone lithologies for storage, with excellent multi-layered reservoirs (up to 10 darcy permeability, over 150m net). The open-file petroleum database that is available to CarbonNet consists of over 1,500 exploration and development wells in this basin and “wall-to-wall” 3D seismic data offshore, supplemented by extensive onshore 2D seismic data. In addition, over 20,000 onshore boreholes targeting brown coal allow an excellent onshore-offshore stratigraphic correlation and detailed interpretation of depositional environments and facies geometries. The drilling and seismic acquisition were both successfully completed, and a wide range of data has been acquired which now enables detailed site characterisation to be fully completed and facilities design to commence. CarbonNet is following international codes of recommended practice for CO2 site characterization, and is working under Australian Greenhouse Gas legislation which contains detailed criteria for definition of a secure storage site - described as a Declaration of an Identified Storage Formation, and similar to a Storage Complex as understood in Europe and North America. The Storage Formation has been fully defined on the basis of pre-existing petroleum industry data. The new GHG data acquisition – well and 3D seismic – is aimed at delineating detailed reservoir and seal properties to inform the site development plan and consequent application for an Injection Licence. The topseal at the CarbonNet site is a sequence of intraformational shales with interbedded coals and minor sands/silts. These are proven by nearby hydrocarbon traps and by the pressure and salinity data from numerous local wells, including the recent appraisal well, Gular-1. A comprehensive set of conventional core and wireline log data was collected from the reservoir and overburden sections at Gular-1. This data will be used for detailed geomechanical studies to guide optimum reservoir injection of CO2. The excellent reservoirs are supported by a world-class aquifer with strong pressure buffering and dissipation capacity which will limit any local pressure increase due to injection. Aquifer performance is quantified by basin-wide pressure responses to a 60-year production history in this prolific petroleum basin. Development of the Pelican site will be linked through a multi-user hub to a potential onshore hydrogen production plant, and other industrial facilities. The CarbonNet project has commenced a commercialisation process for interested and capable industrial partners to participate in the whole project or in selected elements, including the pipeline transport system, storage site development and management, and in environmental and storage security monitoring. This shallow-water marine site offers unique monitoring challenges in terms of ecological sensitivity, operational visibility, and a high-energy marine environment. These challenges strongly affect the choices of effective monitoring technologies and the CarbonNet project has participated closely in field trials of a range of surface, water column, atmospheric, and subsurface sensing technologies in a range of projects conducted by the CSIRO, and leading Australian Universities. These projects have been commissioned and managed by ANLEC R&D.

The hash function is designed to fold an input array of any size into a bit string. For the MD5, this is a very convenient algorithm for comparison and storage. For example, Unix systems use it to store user passwords in a 128-bit encrypted format. It is widely used to check the integrity of files. The length of the output string is 128 bits. When it first appeared, it was impossible to break it, but today this is no longer the case. Many programs select the source word based on the hash. The vast majority of them make an enumeration in the dictionary. However, Rainbow Crack methods are based on generating a set of hashes from a set of characters to search for a hash using the resulting base. Besides, the MD5, like any hash function, has a concept such as collisions - it is about getting the same hashes for different characters' source strings. This paper will propose a method capable of increasing password storage security based on the random generation of an MD5 HASH and then use it to transform the original HASH according to pre-defined rules before storing it in the database.

As an extension of cloud computing, fog/edge computing migrates enormous computing and storage resources to the network edge, which forms an edge layer that is close to end devices. However, security concerns hinder the adoption of fog/edge computing. This book chapter researches the problem of secure storage and search services in fog/edge computing. From the aspect of storage security, we first propose a hierarchical-attribute-based encryption (HABE) scheme to efficiently achieve fine-grained access control in clouds, and then we combine HABE and Proxy Re-Encryption (PRE) by incorporating time concept into PRE to achieve user revocation automatically. From the aspect of search privacy, we propose a dynamic attribute-based keyword search (DABKS) scheme to achieve fine-grained search authorization in cloud computing. DABKS delegates the policy updating operations to the cloud, by incorporating proxy re-encryption (PRE) and a secret sharing scheme (SSS) into attribute-based keyword search (ABKS). The research results will play an important role in attribute-based encryption and searchable encryption, which could help create new situations for providing secure services in cloud computing and fog/edge computing.

Data integrity is one essential concern when it comes to using the store sensitive and valuable data over distributed storage units of cloud computing. This paper review existing approaches towards data integrity in cloud computing to find that the existing public verification scheme is mainly carried out assuming the pre-defined legitimate data auditor. However, if the data auditor goes rogue, it is quite a complex scenario process to ensure data integrity. Therefore, the proposed system offers a novel scheme that assesses the legitimacy of all the actors involved in the data integrity process that was not considered in existing approaches and was found to offers resistive against the majority of threats connected with data integrity. The simulated outcome of the study shows that the proposed system offers better response time, invalidating the test data with highly controlled memory consumption.

Summary Natural or induced fractures are typically present in subsurface geological formations, which are of interest for geo-energy production and storage as well as for long-term carbon dioxide storage. Instinctively, fracture networks may undermine storage security as they are highly conductive pathways which may lead to the leakage of CO2. In addition, they may act as flow barriers, causing significant pressure and stress gradients. Nevertheless, despite their high sensitivities, impacts of fractures on the full-cycle storage process have not been fully understood. In this study, a numerical model is used to examine the role of a single fracture on the flow and transport of CO2 plume in various scenarios. A unified multiphysics framework is developed to model the essential trapping mechanisms in a robust manner. Important is that the projection-based embedded discrete fracture model is incorporated into the framework to describe fractures with varying conductivities. In particular, we focus on the dissolution trapping. Impacts of fracture location, fracture inclination angle, as well as fracture-matrix permeability ratio are systemically studied. Results indicate that the fracture exhibits differing effects regarding the dissolution trapping, and therefore, accurate characterization of fracture geometry and its conductivity is essential for evaluation of CO2 storage in fractured formations.

A large number of educational resources are stored in major university databases, resources are not fully shared and resulting in waste of resources. In order to solve the above problem, a method of university education resource sharing based on blockchain and IPFS is proposed in this paper. Using the technology of smart contract and digital signature to authenticate the identity of universities and establish the alliance chain of universities. The design of IPFS storage combined with blockchain technology and university education resources are stored in IPFS distributed storage and the hash addresses and basic information are stored in the blockchain to achieve the protection and sharing of university education resources. The scheme proposed in this paper can not only improve the storage security of information resources, but also enhance the collaborative processing of educational resources among universities, which is conducive to the sharing of university educational resources.

The Electronic Medical Record (EMR) is a kind of data, which is highly sensitive and worthy of biomedical research. The storage security, integrity, and data sharing of EMRs need further research and discussion. Blockchain is a block-chain storage structure, which has the characteristics of tamper-proof, traceability, and high sharing. Although blockchain technology provides a new solution for the storage and data sharing of EMRs, the research on user-oriented data query is not comprehensive. Traditional SQL database opens its data access, so the data stored in it has easily been tampered with, which is not suitable for storing sensitive medical data. Based on the characteristics of blockchain and traditional databases, we design a model with high security and easy retrieval for the storing and querying the EMRs. In the case of ensuring the safety of medical data storage, it is convenient for users to query and retrieve.

Cloud-IoT data security and privacy have become a major problem due to its sensitivity, which curbs multiple cloud applications. In addition, if the encrypted data lives in one place, in many fields, such as the financial industry and government agencies, the man-in-the-middle-attack (MMA) and phishing attack (PA) may have chances of realising the extraction. The phishing goal is evaluated and predicted by most previous machine learning models through a discrete or continuous result. The current models lag in accurately determining both attacks because of this approach. We developed a three-step phishing detection (PD) framework inspired by machine learning and a secure storage distribution (SSD) for cloud to improve model accuracy and storage security. The partition-based selection of features is designed for phishing detection (PD) with a hybrid classifier approach and hyper-parameter classifier tuning. Initially, the entire data set is partitioned by entropy and is hybridised for each performing model partition. In order to reduce the complexity, the next entropy is applied to decrease the dimension of each partition. Finally, to improve precision, the performing model is optimised with hyper-parameter tuning. The partition-based feature choice with the hybrid classifier approach outperforms with 97.86% accuracy for both attack detection from the experimental and comparative results of SVM, LM, NN and RF. Atlast, SSD performance is evaluated against other storage models where SSD outperforms other models.

The storage and transportation of hazardous chemicals has always been a serious problem. In this paper a scheme for the safety monitoring and management of hazardous chemicals storage is proposed. Multiple sensors are used for real-time detection of external and internal environmental, and the data is transferred to the cloud. Further more the data processing in the cloud. Even if the corresponding early warning is made, it will help the storage security management of hazardous chemicals. For data in the cloud, BP neural network, and multi-particle swarm algorithm (PSO) are used to optimize the weights and thresholds of the network, and to avoid the shortcomings of using BP network alone, converge faster, and obtain the global optimal solution. Through the simulation study of hydrogen storage tank data, the results show that the results predicted by the BP-PSO neural network algorithm are more consistent with the actual situation, and are significantly better than the BP network results. This model can accurately make early warnings of hazardous chemicals and promptly help operators analyze existing security risks, which has a high reference value.

Banks in Europe must comply with new EU regulation and legislation. Recent legislation has focused on personal data, Know Your Customer (KYC), and anti-money laundering. As a result, the cost of KYC compliance is higher than ever, requiring time consuming work by both the banks and their customers in the form of document collection and verification. In this paper we detail a system designed to ease the burden of compliance for banks within the EU and save their customers time through the secure and permissioned sharing of digital KYC data. In order to share data, banks need a secure system capable of protecting the privacy of both them and their clients. We detail a system which uses blockchain technology and various privacy and security enhancing techniques to provide banks with a fast and secure way to share documents required for know your customer compliance. The system was built to be aligned with the GDPR, meaning each participating bank must have explicit permission for a customer to access one or more of their documents. These permissions are stored on a private blockchain shared by the banks. Moreover, we detail methods to anonymise on-chain data where necessary. The use of a private blockchain to achieve consensus on the veracity of customer-granted permissions to data enables participating banks to trust one another as each permission and request is observed, agreed upon, and stored on-chain. To the best of our knowledge we propose the first data sharing system under which there is no outsourcing of data storage. This allows the banks to retain full control of storage security and encryption.

With the increasing importance of data, the threat of malware which destroys data has been increasing. If malware acquires the highest software privilege, any attempt to detect and remove malware can be disabled. In this paper, we propose DISKSHIELD, a secure storage framework. DISKSHIELD uses Intel SGX to provide Trusted Execution Environment (TEE) to the host, implements the file system into SSD firmware that provides a Trusted Computing Base (TCB), and uses a two-way authentication mechanism to securely transfer data from the host TEE to the SSD TCB against data tampering attacks. This design frees DISKSHIELD from attacks to the kernel. To show the efficacy of DISKSHIELD, we prototyped a DISKSHIELD system by modifying Intel IPFS and developing a device file system on the Jasmine OpenSSD Platform in a Linux environment. Our results show that DISKSHIELD provides strong data tamper resistance the throughput of read and write is on average to 28%, 19% lower than IPFS.

The cloud computing plays the prominent role in many organizations and researchers were focus on securing the cloud computing. The privacy preserving is the major challenge that grows exponentially with increases in user. In this paper, the depth survey is conducted on the recent methodologies of the cloud storage security related with the cloud computing. The overview of the cloud computing and security issues is analyzed in this paper. The key security requirements such as data integrity, availability and confidentiality. Security issues in the recent methodologies of cloud security is analyzed. The challenges in the cloud security is analyzed and possible future scope of the method is discussed. The paper involves in analyzing the state-of-art method to investigate the advantages and limitations.

Services are distributed among all servers and between the users and individuals in the cloud environment. Cloud providers have trouble guaranteeing file protection as security is the biggest issue in data handling and transfer as it can be accessed, misused and destroyed the original data form. Cloud security is a big concern in the cloud computing environment. To safeguard the cloud environment, many research works are being proposed. To overcome the security issue and achieve the CIA property (confidentiality, integrity and availability) the cryptography is used. Cryptography is the most useful technique to ensure a high level of data transfer and storage security. In traditional symmetric and asymmetric has some limitations. To solve this we are going to introducing a new hybrid technique to achieve high data security and confidentiality. In this article, we are combing ECC and Blowfish to implement a hybrid algorithm. The performance of the hybrid system is compared with the existing hybrid method and shows that the proposed method provides high security and confidentiality of patient data. The hybrid cryptography is used to defeat the inconveniences of both symmetric and asymmetric.

There are many reasons to implement data protection security strategies regardless of if the data is financial, personal or confidential, risks are a moving target. The purpose of this systematic literary review was to examine articles and documents pertaining to data and storage security and to compare with modern regulatory compliance requirements to determine if gaps exist within the datacenter. Both academic and applied IT security papers were used as well as online governmental and industry sources. As part of this research, the components of the CIA triad were used as a baseline which resulted in a granular model. The model was applied to both quantitative and qualitative data that exposed deficiencies in data and storage security.

The prime contribution of this work is the development of an integrated framework to create a single platform on consolidating the patient’s data as an Electronic Patient Record (EPR), improve the storage security of EPR over the network, and authenticate the retrieval of EPR using a biomedical key. An HL7 standard, OpenEMR has been customized to create an integrated single platform for EPR, which aids in faster access of patient medical data placed under a single unique identification number (ID). To address the privacy of the EPR, the work includes implementation of hybrid watermarking algorithm to embed the entire EPR inside the facial photograph of the patient. A 3D dynamic order template key built using demographic and diagnosis information of the patient addresses the security issue of the EPR. The augmentation index (AI) of the photoplethysmogram (PPG) signals and a homomorphic encryption mechanism doubles the security of the hidden EPR. The processing of PPG signals from 200 patients has revealed that AI is distinct, the peak signal-to-noise ratio (PSNR) is 86 dB, and the structural content of dewatermarked EPR is close to 1. The results have been validated from the doctors of Dental College and Hospital are promising for further treatment of the patients. It is concluded that rotation factor AI upholds the uniqueness of the authentication key.

Outsourcing data to the cloud has become a trend, and the geolocation of cloud data attracts public attention in recent years, which is relevant to data availability (e.g., disaster tolerant), data security and policies (e.g. USA Patrio Act). Unfortunately, cloud service providers are not fully trusted to the data owners. This is because the data owners lose the physical control over the cloud data, and cloud service providers have the ability and motivation to change the geolocation of cloud data between different data centers. Therefore, designing a scheme to determine the geolocation of cloud data for data owners is an urgent problem to be solved.In this paper, we propose Splitter, an efficient scheme to determine the geolocation of cloud data publicly. In Splitter, we first design a splitting method, which breaks up the challenge and proof, and only considers the response delay resulting from the general operations (i.e., addition and multiplication) to obtain the accurate response delay. Second, we combine random forest algorithm and improved triangulation method to determine the geolocation accurately. Third, we take a series of theoretical comparison and extensive experiments to evaluate our scheme. The results illustrate the efficiency and practicality of our scheme.

Gas mixing in the subsurface could have crucial implications on CO2 storage capacity and security. This study illustrates the impact of gas mixing in the “Captain X” CO2 storage site, an open saline aquifer and subset of the greater Captain aquifer, located in the Moray Firth, North Sea. The storage site hosts several abandoned hydrocarbon fields where injected CO2 could interact and mix with any remaining hydrocarbon gas left in the depleted structures. For this study, compositional simulation of CO2 injection into the Captain X storage site reservoir model was conducted to quantify the impact of mixing. Results show mixing of CO2 with the remaining trapped hydrocarbon gas makes the plume considerably less dense and more mobile. This increases the buoyancy forces acting on the plume, causing it to migrate faster towards the shallower storage boundaries and therefore, reduces the storage capacity of the site. Mixing also compromises the storage security as it mobilises the structurally trapped hydrocarbon gas from within the abandoned fields. Informed injector placement helps to manage and reduce the impact of mixing. Correct assessment of mixing is also considerably dependent on the volume and property of the trapped hydrocarbon gas. To provide a correct long term understanding of storage capacity and security, the impact of mixing, therefore, needs to be correctly considered in all large-scale CO2 storage operations., Information taken from the Strategic UK CCS Storage Appraisal Project, funded by DECC, commissioned by the ETI and delivered by Pale Blue Dot Energy, Axis Well Technology and Costain [28]. Information contains copyright information licensed under the ETI Open Licence [28]. The ACT Acorn consortium was led by Pale Blue Dot Energy and includes Bellona Foundation, Heriot-Watt University, Radboud University, Scottish Carbon Capture and Storage (SCCS), University of Aberdeen, University of Edinburgh, and University of Liverpool. ACT Acorn, project 271500, has received funding from BEIS (UK), RCN (Norway) and RVO (Netherland), and is co-funded by the European Commission under the ERA-Net instrument of the Horizon 2020 programme. ACT Grant number 691712. Project ACT-Acorn is gratefully thanked for funding this study. Schlumberger is thanked for this use of Petrel, PVTi and Eclipse 300 software. S. Ghanbari is currently supported by the Energi Simulation. J. Alcalde is funded by MICINN (Juan de la Cierva fellowship - IJC2018-036074-I). Energi Simulation is thanked for funding the chair in reactive transport simulation held by E. Mackay.

Cloud computing is the next generation of IT organization. Cloud computing moves the software and databases to the large centers where the management of services and data may not be fully trusted. In this system, we focus on cloud data storage security, which has been an important aspect of quality of services. To ensure the correctness of user’s data in the cloud, we propose an effective scheme with Advanced Encryption Standard and other cryptographic algorithms. Extensive security and performance analysis show that the proposed scheme is highly efficient. Development of efficient parallel data processing in clouds has come into existence and research for parallel security has been a challenge from the past few years. Parallel security is the data processing framework to explicitly exploit the dynamic storage along with data security. In the generation of IT organization, service level agreement on the basis of security is not an important one. But now it has become a mandatory matter to secure the sensitive data and for corruption detection.

Storage security of multi-level classified data is a vital core link in the construction of a national information system. Given the current problems of relatively long time consumption for storage, high CPU occupancy, high congestion rate, and poor anti-attack performance in the application of Hadoop technology to store multi-level classified data, a secure storage system for multi-level classified data based on the combination of differential evolution algorithm is proposed and designed in this paper. The secure storage system for multi-level classified data, including a client, a relay control server, and a storage server is preliminarily designed. The security performance of the multi-level classified data such as integrity, confidentiality, authentication mechanism, and access control strategy, are analyzed in detail. The system performance test results suggest that the system designed can implement secure and rapid storage of multi-level classified data while reducing the CPU occupancy and system congestion rates at the same time.

Knowing the geolocation of cloud data becomes an urgent problem, which relates to cloud user equity (e.g., service compliance), service performance (e.g., disaster tolerance) and government regulations (e.g., GDPR). Unfortunately, data owners lose physical control after outsourcing data to the cloud service providers, while cloud service providers have the motivation (reducing economic costs and maximizing profits) and ability to move the data to other data centers in different geolocations. As a consequence, verifying whether the cloud data are in a specific geolocation is worthy of concern.In this paper, we propose a novel cloud data public verification scheme, DPVGeo, which allows any entity to verify the actual geolocation of cloud data remotely. In DPVGeo, we first design an atomic proof method, which divides the proof into several minimum computation units (i.e., atomic proof), and subtly only considers the normal operations (i.e., addition and multiplication), ignoring the time-consuming exponentiation operations, to obtain accurate response delay. Second, we utilize a thresholdbased closest-shortest approach to verify the geolocation of cloud data based on the response delay with high accuracy. Besides, we select both blocks and sectors randomly during each challenge to defend against the potential attacks (e.g., outsourcing attack, generation attack and replay attack). Finally, we perform a series of prototype implementations in real network environment to validate the performance of our design. The experimental results and security analysis show that our scheme is efficient and secure against semi-honest cloud service providers.

Being distributed in nature, Cloud is an in-use technology over the last decade where the data is stored on the Cloud service provider’s site where the data owner does not have direct access to the storage where her data is stored. This leads to severe security issues. Out of various such issues, data correctness or integrity is one of the major aspects of Cloud data storage security. Researchers have been exploring the options of using blockchain technology to address the data storage correctness concern in Cloud computing. Blockchain technology has provided a new dimension to address the traditional security issues in computing technologies, where every transaction is treated as an atomic operation, and once performed, it becomes immutable. The transactions are stored in a block, and such blocks are linked together to form a chain through a cryptographic hash code. Such chains are distributed in nature in the sense that there is no single central administrative authority to manage them, and any node in the network can verify the correctness of data or transaction. In this paper, we intend to make an exhaustive study on various alternatives to solve the data integrity issues in Cloud computing using blockchain technology. Being distributed, immutable, transparent, cost effective and efficient to use, blockchain is definitely a solution to many issues including Cloud data storage security problems.

In the daily scientific research activities, the university will form a large number of files. The goal of data management of university scientific research activities is to ensure the availability, authenticity and validity of scientific research data, but the electronic data is more easily modified. There are problems in data security, storage security and utilization security of scientific research data. The blockchain technology is applied to the data management of scientific research activities, which can realize the management of the whole life cycle of scientific research data and ensure the effective use and security of scientific research data. SWOT analysis is helpful for university research managers to understand the real situation of research management. Therefore, this paper makes a detailed SWOT analysis of university scientific research data management, and proposes a data management system of university scientific research activities based on blockchain technology.

Owing to the highly distributed nature of the cloud storage system, it is one of the challenging tasks to incorporate a higher degree of security towards the vulnerable data. Apart from various security concerns, data privacy is still one of the unsolved problems in this regards. The prime reason is that existing approaches of data privacy doesn't offer data integrity and secure data deduplication process at the same time, which is highly essential to ensure a higher degree of resistance against all form of dynamic threats over cloud and internet systems. Therefore, data integrity, as well as data deduplication is such associated phenomena which influence data privacy. Therefore, this manuscript discusses the explicit research contribution toward data integrity, data privacy, and data deduplication. The manuscript also contributes towards highlighting the potential open research issues followed by a discussion of the possible future direction of work towards addressing the existing problems.

This book constitutes the refereed proceedings of the 7th International Symposium on Security in Computing and Communications, SSCC 2019, held in Trivandrum, India, in December 2019. The 22 revised full papers and 7 revised short papers presented were carefully reviewed and selected from 61 submissions. The papers cover wide research fields including cryptography, database and storage security, human and societal aspects of security and privacy.

The SDN controller is the core of the software-defined network (SDN), which provides important network operations that needs to be protected from all type of threats. Many researches have been focusing on different layers of security regarding the SDN controller such as Anti-DDOS system or enforcement of TLS connection between the controller and the Open-vswitches. One of the major security threats targeting any program is the environment execution itself (e.g. Operating system and the hardware itself). Intel's Software Guard Extension (SGX) offers a sloid layer of security applied to applications by creating a Trusted execution environment. SDN controller relay on a storage module to keep sensitive data such as Flow Rules, users’ credentials and configuration files. Protecting this side of the SDN controller is a must in term of security. To date, no work has been conducted considering SDN controller storage security using Intel SGX. This paper introduces an SGX enabled SDN controller. The new controller ensures the integrity and the confidentiality in a trusted execution environment by leveraging a recent hardware technology called intel SGX. This technology provides a trusted and secure enclave. Enclaves are sealed and unsealed by intel SGX attestation mechanisms to protect the executed code and data inside live memory and disk from being altered by any unauthorized access. High privileged codes such as the OS itself is kept from altering data inside enclaves. We implemented the Intel SGX using the Floodlight SDN controller running a real enabled Intel SGX hardware. Our evaluation shows that the SGX enabled SDN controller introduces a slightly observable performance overhead to the floodlight controller compared to advantages in term of security.

Cloud computing is the modern technology, that facilitates to the user various resources, data and files to access from anywhere that a network is available, it is today’s modern computing where we distribute our resources and software from one place to another, but still the security of the data is a major problem in the cloud. However, there is need of processing a huge number of data and maintained the security over cloud. In this research we proposed a software framework (hadoop) with the cloud which helps us to process the huge number of data by using map-reduce model. To add security we use Kerberos authentication protocol which helps us to enhance security level over the cloud and also it is used to provide authentication to the users. We analyze and compare our technique with existing techniques, the results shows that our technique is much more secure and provide user authentication.

The integrity of data and efficiency of storage are the most important concerns in cloud computing. Signature generation and validation of signature are the methods to prove the integrity of data over the cloud. In addition, a third-party auditor (TPA) helps in validating the signature. Proposed auditing system helps in improving storage efficiency and remove duplication on the cloud server. The proposed scheme is efficient as communication is based on the need for integrity checking. The computational cost is calculated at the user side. Batch auditing is implemented in the scheme and it also supports the dynamic auditing. Results are analyzed and compared with some of the existing systems.

Cloud technology is emerging as a widely accepted technology in the recent years due to its robust nature. Cloud computation is basically developed on the fact that the resources can be shared between numerous devices to achieve efficient network operation among devices with minimized computation expenses. However, the sharing nature poses a security risk for the devices whose resources are being shared. Almost in all the existing works on cloud security, a single trusted third party (TTP) is used for key issue and authorization. However, using a single TTP may results in single-point failure and security risks. Most of the previous works on cloud security focus on storage security rather than considering the computation security. In order to solve these issues, in this paper, multi-authority-based file hierarchy hidden CP-ABE scheme for cloud security is proposed. In this scheme, the files are arranged in hierarchical order based on their attribute weights. Then when a cloud user needs certain resources, it requests the cloud service provider (CSP). The CSP links with the cloud owner to provide the requested file after encrypting it. The cloud server encrypts and places the encrypted file in CSP, which is later retrieved by cloud user. In this way, all the files that are being used are encrypted along with strict authentication to ensure cloud security.

The decentralized storage system has problems in terms of secure data and access control issues have an important role in storage security on the service computing system platform. New challenges in data storage provide an opportunity for technology blockchain to be able to solve them. At present, there are several methods in the blockchain that have been implemented. Each method used has different technical characteristics. In this paper, we discuss the implementation of blockchain technology to secure a service computing system platform. In this research, it can be used as an understanding to develop a service computing system platform to produce reliable security.. Ethereum blockchain platform is the most used choice in research.