Enabling identity-based cloud storage public auditing with quantum computers resistance

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

In the process of enterprise information, more and more enterprises begin to pay attention to the construction and implementation of digital archives system. At present, the storage of the digital archives system is local storage and local area network storage, it will lead to the isolation of the archives information, the high cost of construction and management, and the difficulties in the later maintenance. This paper studies and designs the digital archives cloud storage platform, the physical architecture design of the cloud platform, the architecture and planning of the cloud storage platform, the open interface and service access design, and the cloud storage security strategy. Based on the security requirements of digital archival cloud storage, a hybrid encryption mode based on symmetric encryption and asymmetric encryption is proposed, and a digital archives encryption algorithm based on RSA and AES encryption algorithm is implemented. The construction of safe digital archives cloud storage platform can popularize the consciousness of digital archives, reduce the cost of archives management, improve the level of archives management, and ultimately promote the level of modernization management.

A dynamic integrity verification scheme of cloud storage data based on lattice and Bloom filter

Microsoft Orleans is a popular open source distributed programming framework and platform which invented the virtual actor model, and has since evolved into an actor-oriented database system with the addition of database abstractions such as ACID transactions. Properties of Orleans' virtual actor model imply that any ACID transaction mechanism for operations spanning multiple actors must support distributed transactions on top of pluggable cloud storage drivers. Unfortunately, distributed transactions usually perform poorly in this environment, partly because of the high performance and contention overhead of performing two-phase commit (2PC) on slow cloud storage systems. In this paper we describe the design and implementation of ACID transactions in Orleans. The system uses two primary techniques to mask the high latency of cloud storage and enable high transaction throughput. First, Orleans pioneered the use of a distributed form of early lock release by releasing all of a transaction's locks during phase one of 2PC, and by tracking commit dependencies to implement cascading abort. This avoids blocking transactions while running 2PC and enables a distributed form of group commit. Second, Orleans leverages reconnaissance queries to prefetch the state of all actors involved in a transaction from cloud storage prior to running the transaction and acquiring any locks, thus ensuring no locks are held while blocking on high latency cloud storage in most cases.

Using cloud data clients may transfer data from their computer systems to cloud servers. Hence, user will not have any burden of maintenance and also he gets high quality data storage services. Many security issues are concerned with cloud storage. Cloud service providers or storage servers are not completely trustworthy. The user is concerned about the information stored on cloud are in place or not turns. This paper is based on homomorphic hash algorithm. Further this supports dynamic operations such as insert, update, delete and modify at block level, for data dynamics Merkle Hash Tree is used which helps to find the location of each dynamic operation. Third party auditor checks the user's data for correctness and gives the accuracy of the data that is stored in cloud server. The communication and computation overhead are reduced. Deduplication technique is used to check whether the file that user need to store in cloud storage is already exist at cloud server or not. This framework is effective and secure against replace attack launch by malicious server.

The rapid development of quantum computing constitutes a significant threat to modern Public-Key Cryptography (PKC). key, signature and Key Encapsulation Mechanism (KEM) sysThe use of Shor's algorithm [1] with potential powerful tem based on a double exponentiation with matrix exponents. quantum computers could easily break the two most widely In this paper, a high-throughput pipelined architecture of used public key cryptosystems, namely, RSA and Elliptic DME-(3,2,48), with $m =3 , n =2$, and $q = 2 ^{e}\,= 2 ^{48}$, has been Curve Cryptography (ECC), based on integer factorization and presented and hardware implementations over Xilinx FPGAs discrete logarithm problems. For this reason, Post-Quantum Artix-7 have been performed. These settings correspond with Cryptography (PQC) [2] based on alternative mathematical the reference implementation given in the NIST proposal [7], features has become a fundamental research topic due to its where the finite field $GF( 2 ^{48})$ is generated by the type I resistance against quantum computers. The National Institute of Standards and Technology (NIST) has even opened a call for proposals of quantum-resistant PKC algorithms in order to standardize one or more PQC algorithms. Cryptographic systems that appear to be extremely difficult to break with large quantum computers are hash-based cryptography, lattice-based cryptography, code-based cryptography, and multivariate-quadratic cryptography [3]. Furthermore, efficient hardware implementations are highly required for these alternative quantum-resistant cryptosystems [4].

Storage density of the traditional cloud storage system is not big and the comprehensive storage efficiency is low. When the large amount of data increases dramatically, the storage efficiency of the system would be decreased seriously. Therefore, with the development of cloud storage technology, an application of Markov Decision Process in Scheduling Strategy on cloud storage (MDPSS) is proposed in this paper. MDPSS can predict periodically in stages by using the steady Markov decision process and select the optimal storage node easily. By scheduling the data to the optimal node in real time, MDPSS can enhance the flexibility of the data storage and reduce the storage cost. Simulation results show that MDPSS has the ability to predict accurately when the size of the data is different.

With the surge of the powerful quantum computer, lattice-based cryptography proliferated the latest cryptography hardware implementation due to its resistance against quantum computers. Among the computational blocks of lattice-based cryptography, the random errors produced by the sampler play a key role in ensuring the security of these schemes. This paper proposes an integral architecture for the sampler, which can reduce the overall resource consumption by reusing the multipliers and adders within the modular polynomial computation. For instance, our experimental results show that the proposed design can effectively reduce the discrete Ziggurat sampling method in DSP usage.

Cloud storage system enables storing of data in the cloud server efficiently and makes the user to work with the data without any trouble of the resources. In the existing system, the data are stored in the cloud using dynamic data operation with computation which makes the user need to make a copy for further updating and verification of the data loss. An efficient distributed storage auditing mechanism is planned which over comes the limitations in handling the data loss. In this paper the partitioning method is proposed for the data storage which avoids the local copy at the user side by using partitioning method. This method ensures high cloud storage integrity, enhanced error localization and easy identification of misbehaving server. To achieve this, remote data integrity checking concept is used to enhance the performance of cloud storage. In nature the data are dynamic in cloud; hence this work aims to store the data in reduced space with less time and computational cost.

Smart grid utilizes intelligent metering devices that transmit the energy usage data of an end user to a service provider. During the transmission’ an adversary could eavesdrop’ intercept’ modify and forge the data. Therefore’ it is critically important to make sure that such data is confidential and unforgeable. Signcryption is an effective technique to satisfy the requirements. However’ existing signcryption schemes are generally based on conventional hardness assumptions’ which are vulnerable to adversaries equipped with quantum computers in the near future. Additionally, they fail to consider identity leakage that allows an adversary to target a special end-user group of smart grid for attacks. To solve the problems’ an anonymous signcryption scheme for smart grid, dubbed AS4Smd, is proposed. AS4Smd is based on lattice cryptography’ which is post-quantum secure. In AS4Smd’ an end-user can sign and encrypt data in a logically single step to produce a signcryptext. Additionally’ the end-user’s identity is embedded in the signcryptext to accomplish end-user anonymity. The performance evaluation on AS4Smd shows that it is efficient in terms of computation and communication efficiency.

This article explores the emerging trends in cybersecurity in response to the advent of quantum computing, focusing on the development of quantum-safe encryption methods. It begins by examining the vulnerabilities of traditional encryption algorithms like RSA and ECC to quantum attacks, particularly through Shor's algorithm. The article then delves into post-quantum cryptography (PQC), discussing key approaches such as lattice-based cryptography, hash-based signatures, and code-based cryptography, along with their advantages and challenges. Quantum Key Distribution (QKD) is explored as an alternative method for secure communication, leveraging quantum mechanics principles. The article also covers the ongoing standardization efforts led by organizations like NIST, highlighting the progress made in establishing quantum-resistant algorithms and the challenges in their widespread adoption. Finally, it addresses the future-proofing of encryption systems, discussing the balance between quantum resistance and current security needs, the maintenance of confidentiality, integrity, and authentication, and potential hybrid approaches. Throughout, the article emphasizes the critical nature of these developments in ensuring long-term data security in a world where quantum computers may soon become a reality.

Over the past year, quantum computing has seen many advancements due to contributions from UC Berkeley in collaboration with IBM’s laboratories. Although these breakthroughs seem promising, they introduce new security concerns regarding the potential for quantum attacks to become a major threat in future data breaches. This research investigates the efficiency of many quantum cryptographic techniques. These algorithms include Quantum Key Distribution (QKD), Lattice-Based Cryptography, and Multivariate Cryptography. Utilizing IBM’s Qiskit Python extensions, we were able to test these methods against quantum attack algorithms, including Shor's Algorithm, Grover's Algorithm, and the BB84 protocol. Our results indicate that, although current cryptographic methods provide some level of defense against quantum attacks, there needs to be advancements to protect our digital infrastructure for the future. This study puts emphasis on the need for more development in cryptographic algorithms to ensure proper security in the era of quantum computing.

This Research to Practice Full Paper discusses an approach to motivating graduate computer science and electrical engineering students in the study of quantum computing. The U.S. Congress and other governmental and industrial organizations have recognized that teaching Quantum Computing in university-level Computer Science and Electrical Engineering programs is strategically important for workforce development along with technology development. Success in this endeavor requires generating genuine interest in the topic and motivating potential students. Quantum Computing requires an understanding of relatively difficult related subjects including physics, math and computer science. Many computer science and electrical engineering students shy away from quantum computing because of its complexity. This paper discusses our success with generating interest in quantum computing among graduate students and motivating them through exploring, in related courses, some aspects of quantum computing that have the potential to generate discontinuities. The approach is also examined in terms of the Scientific Learning Cycle as discussed by Wankat & Oreovicz. Opportunities and challenges of adding Quantum Computing to Computer Science and Electrical Engineering programs were studied through a student small group project in a cybersecurity course. The work built on extensive research into the effectiveness of small group projects and the role of “play” in learning. Our small group project on quantum computing was introduced three years ago into a cybersecurity course which is required for all students in our university's Masters in Computer Science and Masters in Electrical Engineering programs. The basic goals of the project are to teach students some basics of quantum computing by allowing them to “play with it” and to alert students to the fact that quantum computing is a rapidly developing new field that they need to follow during at least the next decade. The small group project helped students to explore the potential of quantum computing to substantially undermine current Public Key Cryptography and, at the same time provide a new approach to encryption that is even more secure than Public Key Cryptography. A qualitative analysis of discussions and assigned tasks suggests that this strategy succeeded in generating significant interest in quantum computing among students. They became motivated and immersed in a difficult subject despite the requirement for investment of significant time and effort to understand a complex combination of physics, math and computer science.

The on-demand, pay-per-use, and scalable services provided in cloud model guarantee to reduce capital as well as running expenditures for both hardware and software. In cloud environment, users can remotely store their data and access them from a shared pool of configurable computing resources, without local data storage burden. We discuss various methods related to the security and privacy capabilities in cloud paradigm especially data storage in multi cloud environment. We provide three models in form of multicloud architectures which allow categorizing the schemes and analyze them according to their security benefits. The different methods include, resource replication, split application system into tiers based on PIR methods, split both application logic and data into segments. In addition, since the integrity protection of data is a fearsome task in Cloud computing for users with limited computing resources, vulnerabilities in user data privacy are also possible in third party auditing. So we propose a safe cloud storage methodology which supports privacy-preserving third party auditing. And we study the outcomes to perform audits concurrently for multiple users in an efficient manner. Experimental results show that the third party auditing computation time is better than existing approach.

Malware Detection and Analysis play a primary role in information access points such as workstations, servers, cloud storage, and mobile devices. Securing information from malware is a challenging task, and existing anti-malware applications suffer from detecting new types of attacks. In recent years, machine learning-based malware detection techniques have been applied for effective malware detection and analysis for existing and new types of malware with few drawbacks such as misclassification and increased complexity. To overcome this, the chapter focuses on adopting deep learning (DL) models to build an anti-malware application via modern Artificial Intelligence (AI) techniques. Quantum Machine Learning (QML) is a research area that explores the interplay of ideas from quantum computing and machine learning. The proposed framework is built with various DeepNet Models to improve predicted accuracy, which can carry out universal quantum computations. QML performs the information processing with quantum computers relies on substantially different laws of physics called quantum theory. Initially, executable files will be converted to binary files and forwarded to DeepNet Models to multi-classify the novel variants of malware. A comprehensive survey is carried out on the implementation of ML/DL models for malware detection and analysis by considering various factors.