Cloud Computing Security Using Blockchain Technology

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

Nowadays there are more key technologies, such as cloud computing, big data, blockchain, and different techniques that process the data via a well-established purpose. The security of data should be a significant aspect of any type of system, therefore the systems should implement security techniques that solve this aspect. A relatively new encryption technique is searchable encryption, which enables the user to submit search queries based on chosen criteria (for example, keywords) to the cloud server on which the encrypted data (in the form of encrypted documents) is stored. Another emerging technology is blockchain, a decentralized approach for data sharing, based on a direct communication between two peers. To protect the data and its integrity, blockchain uses cryptographic mechanisms and strong mathematics computations as challenges that need to be solved by the nodes that adheres to the blockchain network. In this paper, we propose a searchable encryption scheme, which can be used in the cloud environment, based on elliptic curves and symmetric bilinear mapping for its algorithms and blockchain to store the indexes structure and to process the search query. Using elliptic curves, the proposed scheme is efficient, as it is proven in the literature that elliptic curve cryptography is usually faster than other encryption systems with the same level of security.

Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats, such as adversarial machine learning, model poisoning, and API exploitation, while analyzing AI-powered cybersecurity techniques for threat detection, anomaly prediction, and zero-trust security. The study also examines the role of cryptographic solutions, including homomorphic encryption, federated learning security, and post-quantum cryptography, in safeguarding AI models and data integrity. By integrating AI with cutting-edge cybersecurity strategies, this research aims to enhance resilience, compliance, and trust in AI-driven infrastructures. Future advancements in AI security, blockchain-based authentication, and quantum-enhanced cryptographic solutions will be critical in securing next-generation AI applications in cloud and edge environments. Keywords: AI security, adversarial machine learning, cloud computing security, edge computing security, zero-trust AI, homomorphic encryption, federated learning security, post-quantum cryptography, blockchain for AI security, AI-driven threat detection, model poisoning attacks, anomaly prediction, cyber resilience, decentralized AI security, secure multi-party computation (SMPC).

Cloud is the next generation platform that affords distributed computing environments and dynamic resource pools. Cloud computing is used to crack the daily computing problems providing a full time solution. But the problem related with cloud computing is the data security and data integrity over the Network. This paper is dealt with data security in the cloud environment using RSA digital signatures and robust reversible watermarking technique. In this research paper, we have to tried to increase the MSE and PSNR value of the Decrypted and De-watermarked image in order to prevent more distortion and degradation. In cloud, the cloud service provider or some other person may illegally try to hack customer's information. So, in order to avoid that, customer can encrypt their data by using their own encryption key and watermark their information at the user end. Furthermore, RSA algorithm is the most powerful encryption method used in internet security and Robust Reversible Watermarking technique works good in both lossy and lossless environment. So this combination may generate high PSNR value with minimum mean square error.

Even though cloud computing has advanced over the years, the two major challenges currently faced by cloud computing are data security and trusted computing. In cloud computing applications, blockchain technology can enhance data security and trusted computing when paired with virtual machine agents and mobile agent technologies. A decentralized new framework for distributed computing is blockchain. A potential area of study is the integration of blockchain technology with cloud computing, leveraging the former's security mechanisms to enhance the latter's secure computing and storage capabilities. The usage of the proposed framework builds on the inherent security features of blockchain technology in an effort to secure data and information integrity, confidentiality and authentication in cloud environments. Through the introduction of distributed virtual machine agents, the framework increases the scalability and effectiveness of data protection mechanisms for virtual data environments that can be used to store information protected by cloud security measures. Thus, the proposed study substantiates the applicability and efficiency of the existing and emerging problems of cloud data security and this approach develops satisfactory data protection solutions.

In the contemporary world, the security of data and privacy policies are major concerns in cloud computing. Data stored on the cloud has been claimed to be unsafe and liable to be hacked. Users have found it difficult to trust their data in the cloud. Users want to know that their data is accessible from anywhere and that an unauthorized user will not be able to access it. Another area of concern is the authentication of users over the cloud. There are a number of security concerns with Cloud Computing which include Distributed Denial of Service, Data leakage, and many more, just to mention a few. In this paper, an Elliptic Curve Cryptography (ECC) algorithm is used for the encryption and decryption of the information stored on the cloud, so that if someone gains access to the server, it would be unable to access the original information. Performance evaluation of the developed system is performed by comparing ECC with the RSA algorithm. The results show that the ECC method is more efficient than RSA when used to secure information in the cloud.

This review chapter provides an in-depth exploration of various aspects of data security, ranging from foundational principles to advanced techniques. It sheds light on the importance of maintaining confidentiality, integrity, and availability of data and presents a comprehensive overview of technologies and methodologies that can be employed to achieve robust data security. In today's digital age, ensuring the security and protection of data has become paramount. This research chapter explores various topics related to data security, including confidentiality, integrity, and availability. It also delves into the role of blockchain technology, zero-trust architecture, secure coding practices, legal and ethical considerations, homomorphic encryption, secure multi-party computation, hash functions and digital signatures, secure communication protocols, cryptocurrency and blockchain security, as well as cryptanalysis and side-channel attacks.It highlights various techniques and technologies that can be employed to protect data confidentiality, including encryption and access control mechanisms. Blockchain technology has emerged as a groundbreaking solution for enhancing data security. Zero-trust architecture challenges traditional security models by adopting a holistic and dynamic approach to data protection. Secure coding practices and DevSecOps play a vital role in developing secure software applications. The chapter investigates the importance of integrating security measures throughout the software development lifecycle. It emphasizes the adoption of secure coding practices and the implementation of DevSecOps methodologies to build resilient and secure software systems. Legal and ethical considerations are crucial when addressing data security. Homomorphic encryption and secure multi-party computation are advanced cryptographic techniques that allow data to be processed securely while encrypted. The chapter examines the principles and applications of these techniques, highlighting their significance in scenarios where privacy is paramount. Hash functions and digital signatures form the backbone of data authentication and integrity. The chapter discusses the properties and applications of hash functions and digital signatures in verifying the integrity and authenticity of data. Secure communication protocols are essential for protecting data during transmission. The chapter explores various secure communication protocols, such as Transport Layer Security (TLS), and examines their role in ensuring data confidentiality, integrity, and authenticity. Cryptocurrency and blockchain security focus on the secure storage and transfer of digital assets. The research chapter investigates the security considerations specific to cryptocurrencies and explores how block chain technology can mitigate vulnerabilities and ensure the trustworthiness of transactions. Cryptanalysis and side-channel attacks are threats to cryptographic systems. The chapter discusses these attack techniques, highlighting their potential risks and the measures that can be taken to counter them.

With the fast development of cloud computing and its wide application, data security plays an important role in cloud computing. This paper brought up a novel data security strategy based on artificial immune algorithm on architecture of HDFS for cloud computing. Firstly, we explained the main factors influence data security in cloud environment. Then we introduce HDFS architecture, data security model and put forward an improved security model for cloud computing. In the third section, artificial immune algorithm related with negative selection and dynamic selection algorithm that adopted in our system and how they applied to cloud computing are depicted in detail. Finally simulations are taken by two steps. Former simulations are carried out to prove the performance of artificial immune algorithm brought up in this paper, the latter simulation are running on Cloudsim platform to testify that data security strategy based on artificial immune algorithm for cloud computing is efficient.

The Diffie-Hellman problem as a cryptographic primitive plays an important role in modern cryptology. The Bit Security or Hard-Core Bits of Diffie-Hellman problem in arbitrary finite cyclic group is a long-standing open problem in cryptography. Until now, only few groups have been studied. Hyperelliptic curve cryptography is an alternative to elliptic curve cryptography. Due to the recent cryptanalytic results that the best known algorithms to attack hyperelliptic curve cryptosystems of genus \(g<3\) are the generic methods and the recent implementation results that hyperelliptic curve cryptography in genus 2 has the potential to be competitive with its elliptic curve cryptography counterpart. In this paper, we generalize Boneh and Shparlinksi’s method and result about elliptic curve to the case of Jacobians of hyperelliptic curves. We prove that the least significant bit of each coordinate of hyperelliptic curves Diffie-Hellman secret value in genus 2 is hard as the entire Diffie-Hellman value, and then we also show that any bit is hard as the entire Diffie-Hellman value. Finally, we extend our techniques and results to hyperelliptic curves of any genus.

Cloud computing is one of the most research hot topics in IT industry nowadays. A lot of startup organizations are adopting cloud eagerly due to massive cloud facilities available with minimal investment; but as every coin has two sides, so with cloud. In the cloud, the user data is stored at some off-site location. So cloud data security is one of the main concerns of any organizations, before shifting to the cloud. The data owners can ensure the data security at its premises using firewalls, VPN (Virtual Private Network) like most used security options. But as data owner stores their sensitive data to remote servers and users access required data from these remote cloud servers, which is not under their control. So storing data outside client premises, raises the issue of data security. Thus, one of the primary research areas in cloud computing is cloud data protection. In this research paper, strategies followed include categorization of the data on the basis of their sensitivity and importance, followed by the various cryptography techniques such as the AES (a Symmetric Cryptography technique), SHA-1 (a Hashing technique), and ECC (Elliptic curve Cryptography (an Asymmetric Cryptography technique). Till date, most of the authors were using a single key for both encryption and decryption which is a weak target of various identified malicious attacks. Hence, in the designed hybrid algorithm, two separate keys are used for each encryption and decryption. The cloud user who wants to access cloud data, need to first register with CSP and cloud owner. After registration, user login id, password and OTP (One Time Password) sent to the user registered mobile number, are required to access the encrypted cloud data.

In the modern era of the computing world, the data producing and using it is becoming large and instant at various places. For availing the data at different locations for processing we need to store it in the global platform. Cloud environment provides a best and easy way for this. Cloud computing is becoming as the essential thing for high-quality data services. However there are some potential problems with respect to data security. Here encryption techniques can be used for providing security, but with restricted efficiency. In this paper we propose a new encryption mechanism for providing data security in cloud environment. We propose a two round searchable encryption which supports multi keyword retrieval. Here we adapted a vector space model for improving search accuracy; the elgamal encryption technique allows users to involve in the ranking, while the essential key part of encryption will be done at the source itself. The proposed improves the data security and reduces data leakage.

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data. They have to be dependent on cloud service providers for assurance of the platform’s security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.

The volume of digital data has been increasing day by day. So, computer industries have to handle and process massive amounts of data in various formats. Today cloud computing paradigm reduces the development cost of IT infrastructure in IT industries and business organizations. In cloud computing, the business organizations store their data on remote servers and they have no control it. Data security and privacy protection are two important issues of user concern in a cloud environment and analysis the huge amount of customer details is difficult. Hence, the proposed system implements Signcryption with HECC and SHA-1 for data security and protection. For analyzing a large amount of data on the cloud environment, implements parallel apriori association rule mining on Amazon EMR. The HECC based on Hyperelliptic curve discrete logarithm problem. The attacker can't easily guess the password of the user. The results illustrate that Password guessing attack, Denial of Service (DoS) attack, Compromised key attack, and Eavesdropping and client spoofing attack is not feasible. It also can help developers to do secure parallel and distributed computation in a cloud environment.

This Nowadays system hear that CLOUD computing is ubiquitous. Most of tech giants have converted their owned in-house data centers to using CLOUD technology and outsourcing the part of their services to public cloud and to opt for hybrid cloud architecture. There is no doubt that CLOUDs have the potential for being the next generation model computational services, but with that potential comes the risk of theft and concerns related to security and privacy. A lot of development and progress has already been made in CLOUD technologies, there still remains a wide range of concerns this article system identify and classify the main security concerns and solutions in cloud computing, and propose a method of security in cloud environment. In this paper Row Column Diagonal (RCD) based engine has been proposed. Along with the cloud sub-cloud. Data Security using engine in Environment is also explained. Further, engine system has been proposed in Central Cloud and RCD Supervisor modes in environment along with the situation of uploading and downloading. Validation and analysis have been performed for Engine and indicates based engine is much powerful and secure. As Algorithm is much more robust and easy to implement with considering all the aspects of main and sub cloud using supervisor as the central body.

Cloud computing represents a significant challenge for securing data due to its inherently distributed model. Elliptic Curve Cryptography (ECC) will provide substantial security with smaller key sizes, creating a strong candidate for cloud-based environments. This review critically examines ECC based security architectures in cloud computing with the aim of reducing the cipher space for efficiency and scalability. A comprehensive examination of recent ECC implementations guided this review, including identity-based and lightweight ECC Architectures. Specific consideration was given to cipher space reduction techniques incorporating point compression and optimized scalar multiplication. The review provided evidence of a gap in the literature regarding the ability to counterbalance strength of security with the compactness of the cipher across a range of cloud scenarios and use cases. Most models considered provide little flexibility for real-time or post-quantum alternatives. None of the literature considered cipher space optimization for ECC to date. The ongoing optimization of cipher space is essential to progressing security based on ECC implementations for cloud scenarios. Future research efforts must centre on adaptive, efficient, quantum resilient ECC schemes, in ways that yield high security with minimum impacts on the operational scale, and the associated computational loads.