Biblio

The paper presents an example Sensor-cloud architecture that integrates security as its native ingredient. It is based on the multi-layer client-server model with separation of physical and virtual instances of sensors, gateways, application servers and data storage. It proposes the application of virtualised sensor nodes as a prerequisite for increasing security, privacy, reliability and data protection. All main concerns in Sensor-Cloud security are addressed: from secure association, authentication and authorization to privacy and data integrity and protection. The main concept is that securing the virtual instances is easier to implement, manage and audit and the only bottleneck is the physical interaction between real sensor and its virtual reflection.

Open Science Big Data is emerging as an important area of research and software development. Although there are several high quality frameworks for Big Data, additional capabilities are needed for Open Science Big Data. These include data provenance, citable reusable data, data sources providing links to research literature, relationships to other data and theories, transparent analysis/reproducibility, data privacy, new optimizations/advanced algorithms, data curation, data storage and transfer. An important part of science is explanation of results, ideally leading to theory formation. In this paper, we examine means for supporting the use of theory in big data analytics as well as using big data to assist in theory formation. One approach is to fit data in a way that is compatible with some theory, existing or new. Functional Data Analysis allows precise fitting of data as well as penalties for lack of smoothness or even departure from theoretical expectations. This paper discusses principal differential analysis and related techniques for fitting data where, for example, a time-based process is governed by an ordinary differential equation. Automation in theory formation is also considered. Case studies in the fields of computational economics and finance are considered.

We propose a novel application of coded computing to the problem of the nearest neighbor estimation using MatDot Codes (Fahim et al., Allerton'17) that are known to be optimal for matrix multiplication in terms of recovery threshold under storage constraints. In approximate nearest neighbor algorithms, it is common to construct efficient in-memory indexes to improve query response time. One such strategy is Multiple Random Projection Trees (MRPT), which reduces the set of candidate points over which Euclidean distance calculations are performed. However, this may result in a high memory footprint and possibly paging penalties for large or high-dimensional data. Here we propose two techniques to parallelize MRPT that exploit data and model parallelism respectively by dividing both the data storage and the computation efforts among different nodes in a distributed computing cluster. This is especially critical when a single compute node cannot hold the complete dataset in memory. We also propose a novel coded computation strategy based on MatDot codes for the model-parallel architecture that, in a straggler-prone environment, achieves the storage-optimal recovery threshold, i.e., the number of nodes that are required to serve a query. We experimentally demonstrate that, in the absence of straggling, our distributed approaches require less query time than execution on a single processing node, providing near-linear speedups with respect to the number of worker nodes. Our experiments on real systems with simulated straggling, we also show that in a straggler-prone environment, our strategy achieves a faster query execution than the uncoded strategy.

With the widespread of cloud computing, the delegation of storage and computing is becoming a popular trend. Concerns on data integrity, security, user privacy as well as the correctness of execution are highlighted due to the untrusted remote data manipulation. Most of existing proposals solve the integrity checking and verifiable computation problems by challenge-response model, but are lack of scalability and reusability. Via blockchain, we achieve efficient and transparent public verifiable delegation for both storage and computing. Meanwhile, the smart contract provides API for request handling and secure data query. The security and privacy issues of data opening are settled by applying cryptographic algorithms all through the delegations. Additionally, any access to the outsourced data requires the owner's authentication, so that the dat transference and utilization are under control.

Cloud computing is an Internet-based technology that emerging rapidly in the last few years due to popular and demanded services required by various institutions, organizations, and individuals. structured, unstructured, semistructured data is transfer at a record pace on to the cloud server. These institutions, businesses, and organizations are shifting more and more increasing workloads on cloud server, due to high cost, space and maintenance issues from big data, cloud computing will become a potential choice for the storage of data. In Cloud Environment, It is obvious that data is not secure completely yet from inside and outside attacks and intrusions because cloud servers are under the control of a third party. The Security of data becomes an important aspect due to the storage of sensitive data in a cloud environment. In this paper, we give an overview of characteristics and state of art of big data and data security & privacy top threats, open issues and current challenges and their impact on business are discussed for future research perspective and review & analysis of previous and recent frameworks and architectures for data security that are continuously established against threats to enhance how to keep and store data in the cloud environment.

Using the blockchain technology to store the privatedocuments of individuals will help make data more reliable and secure, preventing the loss of data and unauthorized access. The Consensus algorithm along with the hash algorithms maintains the integrity of data simultaneously providing authentication and authorization. The paper incorporates the block chain and the Identity Based Encryption management concept. The Identity based Management system allows the encryption of the user's data as well as their identity and thus preventing them from Identity theft and fraud. These two technologies combined will result in a more secure way of storing the data and protecting the privacy of the user.

The rapid development of blockchain application technology promotes continuous exploration in the field of computer application science. Although it is still in the initial stage of development, the technical features of blockchain technology such as decentralization, identity verification, tamper resistance, data integrity, and security are regarded as excellent solutions to today's computer security technical problems. In this paper, we will analyze and compare blockchain data storage and cloud data processing technologies, focusing on the concept and technology of blockchain distributed data storage technology, and analyze and summarize the key issues. The results of this paper will provide a useful reference for the application and research of blockchain technology in cloud storage security.

In the cyber crime huge log data, transactional data occurs which tends to plenty of data for storage and analyze them. It is difficult for forensic investigators to play plenty of time to find out clue and analyze those data. In network forensic analysis involves network traces and detection of attacks. The trace involves an Intrusion Detection System and firewall logs, logs generated by network services and applications, packet captures by sniffers. In network lots of data is generated in every event of action, so it is difficult for forensic investigators to find out clue and analyzing those data. In network forensics is deals with analysis, monitoring, capturing, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper focuses on data collection from the cyber system and web browser. The FTK 4.0 is discussing for memory forensic analysis and remote system forensic which is to be used as evidence for aiding investigation.
 

Now a day's cloud technology is a new example of computing that pays attention to more computer user, government agencies and business. Cloud technology brought more advantages particularly in every-present services where everyone can have a right to access cloud computing services by internet. With use of cloud computing, there is no requirement for physical servers or hardware that will help the computer system of company, networks and internet services. One of center services offered by cloud technology is storing the data in remote storage space. In the last few years, storage of data has been realized as important problems in information technology. In cloud computing data storage technology, there are some set of significant policy issues that includes privacy issues, anonymity, security, government surveillance, telecommunication capacity, liability, reliability and among others. Although cloud technology provides a lot of benefits, security is the significant issues between customer and cloud. Normally cloud computing technology has more customers like as academia, enterprises, and normal users who have various incentives to go to cloud. If the clients of cloud are academia, security result on computing performance and for this types of clients cloud provider's needs to discover a method to combine performance and security. In this research paper the more significant issue is security but with diverse vision. High performance might be not as dangerous for them as academia. In our paper, we design an efficient secure and verifiable outsourcing protocol for outsourcing data. We develop extended QP problem protocol for storing and outsourcing a data securely. To achieve the data security correctness, we validate the result returned through the cloud by Karush\_Kuhn\_Tucker conditions that are sufficient and necessary for the most favorable solution.

With the development of sensor technology, people put forward a higher level, more diversified demand for portable rangefinders. However, its data storage method has not been developed in a large scale and breakthrough. This paper studies the design of portable sensor data storage system based on homomorphic encryption algorithm, which aims to maintain the security of sensor data storage through homomorphic encryption algorithm. This paper analyzes the functional requirements of the sensor data storage system, puts forward the overall design scheme of the system, and explains in detail the requirements and indicators for the specific realization of each part of the function. Analyze the different technical resources currently used in the storage system field, and dig deep into the key technologies that match the portable sensor data storage system. This paper has changed the problem of cumbersome operation steps and inconvenient data recovery in the sensor data storage system. This paper mainly uses the method of control variables and data comparison to carry out the experiment. The experimental results show that the success rate of the sensor data storage system under the homomorphic encryption algorithm is infinitely close to 100% as the number of data blocks increases.

Now a days, Cloud computing has brought a unbelievable change in companies, organizations, firm and institutions etc. IT industries is advantage with low investment in infrastructure and maintenance with the growth of cloud computing. The Virtualization technique is examine as the big thing in cloud computing. Even though, cloud computing has more benefits; the disadvantage of the cloud computing environment is ensuring security. Security means, the Cloud Service Provider to ensure the basic integrity, availability, privacy, confidentiality, authentication and authorization in data storage, virtual machine security etc. In this paper, we presented a Local outlier factors mechanism, which may be helpful for the detection of Distributed Denial of Service attack in a cloud computing environment. As DDoS attack becomes strong with the passing of time, and then the attack may be reduced, if it is detected at first. So we fully focused on detecting DDoS attack to secure the cloud environment. In addition, our scheme is able to identify their possible sources, giving important clues for cloud computing administrators to spot the outliers. By using WEKA (Waikato Environment for Knowledge Analysis) we have analyzed our scheme with other clustering algorithm on the basis of higher detection rates and lower false alarm rate. DR-LOF would serve as a better DDoS detection tool, which helps to improve security framework in cloud computing.

In our digital world internet is a widespread channel for transmission of information. Information that is transmitted can be in form of messages, images, audios and videos. Due to this escalating use of digital data exchange cryptography and network security has now become very important in modern digital communication network. Cryptography is a method of storing and transmitting data in a particular form so that only those for whom it is intended can read and process it. The term cryptography is most often associated with scrambling plaintext into ciphertext. This process is called as encryption. Today in industrial processes images are very frequently used, so it has become essential for us to protect the confidential image data from unauthorized access. In this paper Advanced Encryption Standard (AES) which is a symmetric algorithm is used for encryption and decryption of image. Performance of Advanced Encryption Standard algorithm is further enhanced by adding a key stream generator W7. NIOS II soft core processor is used for implementation of encryption and decryption algorithm. A system is designed with the help of SOPC (System on programmable chip) builder tool which is available in QUARTUS II (Version 10.1) environment using NIOS II soft core processor. Developed single core system is implemented using Altera DE2 FPGA board (Cyclone II EP2C35F672). Using MATLAB the image is read and then by using DWT (Discrete Wavelet Transform) the image is compressed. The image obtained after compression is now given as input to proposed AES encryption algorithm. The output of encryption algorithm is given as input to decryption algorithm in order to get back the original image. The implementation of which is done on the developed single core platform using NIOS II processor. Finally the output is analyzed in MATLAB by plotting histogram of original and encrypted image.

With the rapid development of Internet of things (IOT) and big data, the number of network terminal devices and big data transmission are increasing rapidly. Traditional cloud computing faces a great challenge in dealing with this massive amount of data. Fog computing which extends the computing at the edge of the network can provide computation and data storage. Attribute based-encryption can effectively achieve the fine-grained access control. However, the computational complexity of the encryption and decryption is growing linearly with the increase of the number of attributes. In order to reduce the computational cost and guarantee the confidentiality of data, distributed access control with outsourced computation in fog computing is proposed in this paper. In our proposed scheme, fog device takes most of computational cost in encryption and decryption phase. The computational cost of the receiver and sender can be reduced. Moreover, the private key of the user is generated by multi-authority which can enhance the security of data. The analysis of security and performance shows that our proposed scheme proves to be effective and secure.

Cloud computing has become an important and popular infrastructure for data storage and sharing. Typically, data owners outsource their massive data to a public cloud that will provide search services to authorized data users. With privacy concerns, the valuable outsourced data cannot be exposed directly, and should be encrypted before outsourcing to the public cloud. In this paper, we focus on k-Nearest Neighbor (k-NN) search over encrypted data. We propose efficient and secure k-NN search schemes based on matrix similarity to achieve efficient and secure query services in public cloud. In our basic scheme, we construct the traces of two diagonal multiplication matrices to denote the Euclidean distance of two data points, and perform secure k-NN search by comparing traces of corresponding similar matrices. In our enhanced scheme, we strengthen the security property by decomposing matrices based on our basic scheme. Security analysis shows that our schemes protect the data privacy and query privacy under attacking with different levels of background knowledge. Experimental evaluations show that both schemes are efficient in terms of computation complexity as well as computational cost.

Cloud computing belongs to distributed network technology for computing and storage capabilities purpose. It is a kind of cost-effective technology dedicated to information technology. Using the Internet, the accessibility and retrieving of cloud data have become much more accessible. The service providers can expand the storage space in a cloud environment. Security is well-thought-out to be the essential attribute in a distributed system. Cryptography can be described as a method of securing the data from attackers and eavesdroppers. Third Party Auditor is responsible for the authentication of secret files in cloud system on behalf of the data owner. The data auditability technique allows the user to make the data integrity check using a third party. Cloud computing offers unlimited data space for storage to its users and also serves sharing of data and planned use of heterogeneous resources in distributed systems. This paper describes privacy-preserving enabled public auditing method using cryptographic techniques for low-performance based end devices.

Nowadays cloud computing has become the crucial part of IT and most important thing is information security in cloud environment. Range of users can access the facilities and use cloud according to their feasibility. Cloud computing is utilized as safe storage of information but still data security is the biggest concern, for example, secrecy, data accessibility, data integrity is considerable factor for cloud storage. Cloud service providers provide the facility to clients that they can store the data on cloud remotely and access whenever required. Due to this facility, it gets necessary to shield or cover information from unapproved access, hackers or any sort of alteration and malevolent conduct. It is inexpensive approach to store the valuable information and doesn't require any hardware and software to hold the data. it gives excellent work experience but main measure is just security. In this work security strategies have been proposed for cloud data protection, capable to overpower the shortcomings of conventional data protection algorithms and enhancing security using steganography algorithm, encryption decryption techniques, compression and file splitting technique. These techniques are utilized for effective results in data protection, Client can easily access our developed desktop application and share the information in an effective and secured way.

The need for data exchange and storage is currently increasing. The increased need for data exchange and storage also increases the need for data exchange devices and media. One of the most commonly used media exchanges and data storage is the USB Flash Drive. USB Flash Drive are widely used because they are easy to carry and have a fairly large storage. Unfortunately, this increased need is not directly proportional to an increase in awareness of device security, both for USB flash drive devices and computer devices that are used as primary storage devices. This research shows the threats that can arise from the use of USB Flash Drive devices. The threat that is used in this research is the fork bomb implemented on an Arduino Pro Micro device that is converted to a USB Flash drive. The purpose of the Fork Bomb is to damage the memory performance of the affected devices. As a result, memory performance to execute the process will slow down. The use of a USB Flash drive as an attack vector with the fork bomb method causes users to not be able to access the operating system that was attacked. The results obtained indicate that the USB Flash Drive can be used as a medium of Fork Bomb attack on the Windows operating system.

It is a well-known fact that the use of Cloud Computing is becoming very common all over the world for data storage and analysis. But the proliferation of the threats in cloud is also their; threats like Information breaches, Data thrashing, Cloud account or Service traffic hijacking, Insecure APIs, Denial of Service, Malicious Insiders, Abuse of Cloud services, Insufficient due Diligence and Shared Technology Vulnerable. This paper tries to come up with the solution for the threat (Denial of Service) in cloud. We attempt to give our newly proposed model by the hybridization of Genetic algorithm and extension of Diffie Hellman algorithm and tries to make cloud transmission secure from upcoming intruders.

Data storage in cloud should come along with high safety and confidentiality. It is accountability of cloud service provider to guarantee the availability and security of client data. There exist various alternatives for storage services but confidentiality and complexity solutions for database as a service are still not satisfactory. Proposed system gives alternative solution for database as a service that integrates benefits of different services along with advance encryption techniques. It yields possibility of applying concurrency on encrypted data. This alternative provides supporting facility to connect dispersed clients with elimination of intermediate proxy by which simplicity can acquired. Performance of proposed system evaluated on basis of theoretical analyses.

Biometric methods are the most effective and accurate authentication methods. However, a significant drawback of such methods is the storage of authentication information in clear text. The article is devoted to solving this problem by means of symmetric encryption method and the method of dividing the memory space. The method of symmetric encryption ensures confidentiality during storage and transmission of biometric characteristics, the method of dividing the memory space provides an increase of information security level during processing of biometric characteristics.

In the past the security of building automation solely depended on the security of the devices inside or tightly connected to the building. In the last years more devices evolved using some kind of cloud service as a back-end or providers supplying some kind of device to the user. Also, the number of building automation systems connected to the Internet for management, control, and data storage increases every year. These developments cause the appearance of new threats on building automation. As Internet of Thing (IoT) and building automation intertwine more and more these threats are also valid for IoT installations. The paper presents new attack vectors and new threats using the threat model of Meyer et al.[1].

Spreadsheets are widely used in industries for tabular data analysis, visualization and storage. Users often exchange spreadsheets' semi-structured data to collaborative analyze them. Recently, office suites integrated a software module that enables collaborative authoring of office files, including spreadsheets, to facilitate the sharing process. Typically spreadsheets collaborative authoring applications, like Google Sheets or Excel online, need to delocalize the entire file in public cloud storage servers. This choice is not secure for enterprise use because it exposes shared content to the risk of third party access. Moreover, available platforms usually provide coarse grained spreadsheet file sharing, where collaborators have access to all data stored inside a workbook and to all the spreadsheets' formulas used to manipulate those data. This approach limits users' possibilities to disclose only a small portion of tabular data and integrate data coming from different sources (spreadsheets or software platforms). For these reasons enterprise users prefer to control fine grained confidential data exchange and their updates manually through copy, paste, attach-to-email, extract-from-email operations. However unsupervised data sharing and circulation often leads to errors or, at the very least, to inconsistencies, data losses, and proliferation of multiple copies. We propose a model that gives business users a different level of spreadsheet data sharing control, privacy and management. Our approach enables collaborative analytics of tabular data focusing on fine grained spreadsheet data sharing instead of coarse grained file sharing. This solution works with a platform that implements an end to end encrypted protocol for sensitive data sharing that prevents third party access to confidential content. Data are never shared into public clouds but they are transferred encrypted among the administrative domains of collaborators. In this paper we describe the model and the implemented system that enable our solution. We focus on two enterprise use cases we implemented describing how we deployed our platform to speed up and optimize industry processes that involve spreadsheet usage.

Cloud computing, often referred to as simply “the cloud,” is the delivery of on-demand computing resources; everything from applications to data centers over the Internet. Cloud is used not only for storing data, but also the stored data can be shared by multiple users. Due to this, the integrity of cloud data is subject to doubt. Every time it is not possible for user to download all data and verify integrity, so proposed system contain Third Party Auditor (TPA) to verify the integrity of shared data. During auditing, the shared data is kept private from public verifiers, who are able to verify shared data integrity without downloading or retrieving the entire data file. Group signature is used to preserve identity privacy of group members from third party auditor. Privacy preserving is done to ensure that the TPA cannot derive user's data content from the information collected during the auditing process.

Recently, cloud computing has been spotlighted as a new paradigm of database management system. In this environment, databases are outsourced and deployed on a service provider in order to reduce cost for data storage and maintenance. However, the service provider might be untrusted so that the two issues of data security, including data confidentiality and query result integrity, become major concerns for users. Existing bucket-based data authentication methods have problem that the original spatial data distribution can be disclosed from data authentication index due to the unsophisticated data grouping strategies. In addition, the transmission overhead of verification object is high. In this paper, we propose a privacy-aware query authentication which guarantees data confidentiality and query result integrity for users. A periodic function-based data grouping scheme is designed to privately partition a spatial database into small groups for generating a signature of each group. The group signature is used to check the correctness and completeness of outsourced data when answering a range query to users. Through performance evaluation, it is shown that proposed method outperforms the existing method in terms of range query processing time up to 3 times.

Recently, cloud computing has been spotlighted as a new paradigm of database management system. In this environment, databases are outsourced and deployed on a service provider in order to reduce cost for data storage and maintenance. However, the service provider might be untrusted so that the two issues of data security, including data confidentiality and query result integrity, become major concerns for users. Existing bucket-based data authentication methods have problem that the original spatial data distribution can be disclosed from data authentication index due to the unsophisticated data grouping strategies. In addition, the transmission overhead of verification object is high. In this paper, we propose a privacy-aware query authentication which guarantees data confidentiality and query result integrity for users. A periodic function-based data grouping scheme is designed to privately partition a spatial database into small groups for generating a signature of each group. The group signature is used to check the correctness and completeness of outsourced data when answering a range query to users. Through performance evaluation, it is shown that proposed method outperforms the existing method in terms of range query processing time up to 3 times.