Private Function Evaluation using CKKS-based Homomorphic Encrypted LookUp Tables

As big data continues to proliferate at an unprecedented rate, cloud computing has emerged as a fundamental technology for managing, storing, and processing these vast datasets, with global data volumes projected to reach 175 zettabytes by 2025. Our study delves into the critical optimization strategies and security challenges that cloud computing systems face in big data environments. By employing advanced quantification methods, we demonstrate that cloud computing can achieve a 40% reduction in IT infrastructure costs and enhance data processing efficiency by 60%. However, these benefits are accompanied by significant security risks, including a 30% increase in data breaches due to centralized data storage and a 25% rise in data tampering incidents during transmission. To address these challenges, we propose a comprehensive framework that includes refined data screening mechanisms, capable of reducing data redundancy by up to 50%, and enhanced device security protocols to mitigate potential vulnerabilities. Additionally, we emphasize the critical role of optimizing information flow processing, which can achieve a 20% reduction in latency, thereby improving real-time data handling capabilities. Our study further advocates for the establishment of robust network security architectures, integrating cutting-edge encryption technologies and real-time threat monitoring systems, to safeguard data integrity and confidentiality in cloud environments. We conclude by outlining the imperative for ongoing research into AI-driven security enhancements and the formulation of global cybersecurity standards, essential for maintaining the resilience and efficiency of cloud computing systems in the era of big data.

Cloud computing is one of the trending technologies that provide boundless virtualized resources to the internet users as an important services through the internet, while providing the privacy and security. By using these cloud services, internet users get many parallel computing resources at low cost. It predicted that till 2016, revenues from the online business management spent $4 billion for data storage. Cloud is an open source platform structure, so it is having more chances to malicious attacks. Privacy, confidentiality, and security of stored data are primary security challenges in cloud computing. In cloud computing, ‘virtualization’ is one of the techniques dividing memory into different blocks. In most of the existing systems there is only single authority in the system to provide the encrypted keys. To fill the few security issues, this paper proposed a novel authenticated trust security model for secure virtualization system to encrypt the files. The proposed security model achieves the following functions: 1) allotting the VSM(VM Security Monitor) model for each virtual machine; 2) providing secret keys to encrypt and decrypt information by symmetric encryption.The contribution is a proposed architecture that provides a workable security that a cloud service provider can offer to its consumers. Detailed analysis and architecture design presented to elaborate security model.

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.

Cloud computing platform provides on-demand access of services, and these services can also be customised, which enables the customers to use these services as per their requirement and they also need to pay according to their uses. Recent advancements in the field of cloud computing and internet technologies have attracted the organisations to shift their businesses on cloud. However, moving data and applications to a third party services provider also raises critical concern for security and privacy. Availability of cloud services is one of the most important security issues which directly affect the business of the cloud service providers and also its customers. Distributed denial of service (DDoS) attack on cloud is the root cause that challenges the availability of cloud services. This paper presents taxonomy of security issues in cloud, taxonomy of DDoS attacks in cloud, and taxonomy of DDoS defence mechanisms in cloud environment. We have also discussed, analysed, and compared some well-known methods for DDoS attack defence in cloud on different parameters that will help the victim to understand the risk and also assists them to select the proper defence technique to protect their cloud system from DDoS attacks.

Many applications deployed to public clouds are concerned about the confidentiality of their outsourced data, such as financial services and electronic patient records. A plausible solution to this problem is homomorphic encryption (HE), which supports certain algebraic operations directly over the ciphertexts. The downside of HE schemes is their significant, if not prohibitive, performance overhead for data-intensive workloads that are very common for outsourced databases, or database-as-a-serve in cloud computing. The objective of this work is to mitigate the performance overhead incurred by the HE module in outsourced databases. To that end, this paper proposes a radix-based parallel caching optimization for accelerating the performance of homomorphic encryption (HE) of outsourced databases in cloud computing. The key insight of the proposed optimization is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. We design the radix HE algorithm and apply it to both batch- and incremental-HE schemes; we demonstrate the security of those radix-based HE schemes by showing that the problem of breaking them can be reduced to the problem of breaking their base HE schemes that are known IND-CPA (i.e. Indistinguishability under Chosen-Plaintext Attack). We implement the radix-based schemes as middleware of a 10-node Cassandra cluster on CloudLab; experiments on six workloads show that the proposed caching can boost state-of-the-art HE schemes, such as Paillier and Symmetria, by up to five orders of magnitude.

<span lang="EN-US">Cloud computing is a new technology which refers to an infrastructure where both software and hardware application are operate for the network with the help of internet. Cloud computing provide these services with the help of rule know as you pay as you go on. Internet of things (IoT) is a new technology which is growing rapidly in the field of telecommunications. The aim of IoT devices is to connect all things around us to the Internet and thus provide us with smarter cities, intelligent homes and generally more comfortable lives. The combation of cloud computing and IoT devices make rapid development of both technologies. In this paper, we present information about IoT and cloud computing with a focus on the security issues of both technologies. Concluding we present the contribution of cloud computing to the IoT technology. Thus, it shows how the cloud computing technology improves the function of the IoT. Finally present the security challenges of both technologies IoT and cloud computing.</span>

Day by day witnesses a wide development of information technology in the use of cloud computing, and its services shape its architecture. It is indeed, that replaces the former attempts to enrich the security strategy in cloud computing. Security is one of the major issues in the cloud computing environment. In this paper we investigate about security threads and possible solution for cloud. The paper is categorized as follows: Section I describes the cloud computing overview. Section II describes the security threats and challenges in cloud computing. Section III describes the Obstacles and vulnerabilities that can be carried out to cloud environments. Section IV describes the possible solution of the issues. Section V concludes the paper with a cloud computing security.

This paper sheds light over the security issues and challenges in cloud computing and puts emphasis on cloud computing service types and the different types of delivery along with their security challenges and threats. While the benefits of cloud computing are clear, there are several issues that keep enterprises from adopting it. Problems including account control, data control, multi-tenancy, data segregation and more need to be addressed and have made several propositions to reduce some of these issues and facilitate management of security in cloud computing. These propositions include biometric validations using facial recognition and fingerprint scan.

Federated Learning (FL) is a revolutionary distributed machine learning paradigm in which collaborative model training on decentralized devices is achieved without sharing raw data. However, FL is susceptible to poisoning attacks and model update-based leakage of privacy through exchange. We propose in this paper a secure FL framework with encryption based methods to ensure confidentiality and integrity of model updates on sending and aggregation. We compare the application of two cryptographic methods: Symmetric Encryption to offer lightweight, efficient security and Homomorphic Encryption (HE) for advanced privacy-preserving computation on encrypted data. We conduct extensive experiments to assess the trade-offs in security, performance, and accuracy. We find that symmetric encryption offers robust protection with low runtime overhead, and HE offers maximum security assurances at the expense of computational complexity. To evaluate their impact, we conduct simulation-based experiments on the MNIST dataset using the FedAvg aggregation algorithm under a cyclic label flipping attack, a form of data poisoning. This work aims to objectively explore how encryption affects federated learning and evaluates its significance in maintaining data privacy and efficient model performance.

In light of the emergence of privacy breaches in federated learning, secure aggregation protocols, which mainly adopt either homomorphic encryption or threshold secret sharing techniques, have been extensively developed to preserve the privacy of each client's local gradient. Nevertheless, many existing schemes suffer from either poor capability of privacy protection or expensive computational and communication overheads. Accordingly, in this paper, we propose an efficient and multi-private key secure aggregation scheme for federated learning. Specifically, we skillfully design a multi-private key secure aggregation algorithm that achieves homomorphic addition operation, with two important benefits: 1) both the server and each client can freely select public and private keys without introducing a trusted third party, and 2) the plaintext space is relatively large, making it more suitable for deep models. Besides, for dealing with the high dimensional deep model parameter, we introduce a super-increasing sequence to compress multi-dimensional data into one dimension, which greatly reduces encryption and decryption times as well as communication for ciphertext transmission. Detailed security analyses show that our proposed scheme can achieve semantic security of both individual local gradients and the aggregated result while achieving optimal robustness in tolerating client collusion. Extensive simulations demonstrate that the accuracy of our scheme is almost the same as the non-private approach, while the efficiency of our scheme is much better than the state-of-the-art baselines. More importantly, the efficiency advantages of our scheme will become increasingly prominent as the number of model parameters increases.

Homomorphic Encryption (HE), which allows encrypted data to be handled, is becoming an important tool for Russia. It helps to comply with strict data localization laws Federal Law No. 242-FZ,which regulates the transfer and storage of information abroad. As global cybersecurity threats evolve, the need for secure and efficient encryption mechanisms grows, especially in sectors such as finance, healthcare, and government services. This article explores Russia’s strategic adoption of Homomorphic Encryption (HE) as a cornerstone technology to achieve IT sovereignty amid geopolitical tensions and economic sanctions. It highlights the critical need to protect sensitive data processed on foreign cloud infrastructures, which currently dominate Russian enterprises, by enabling secure computations on encrypted data without exposing the underlying information. The paper provides a detailed comparative analysis of popular HE schemes (CKKS, BGV, TFHE) and their adaptation to Russian cryptographic standards such as GOST R34.12-2015, emphasizing the balance between computational overhead and sovereignty gains. A case study of Rostelecom’s pioneering use of HE for cross-border voice analytics demonstrates practical implementation, showcasing acceptable latency, high accuracy, and manageable energy consumption when processing encrypted voice data for national security purposes. The article also benchmarks domestic hardware, notably the Elbrus-16S processor, against global standards, underscoring the need for optimization to close performance gaps while benefiting from energy efficiency. Further, it outlines a policy roadmap focusing on education and infrastructure development, including university programs, industry partnerships, hardware enhancements, and integration with legacy systems, to foster a robust ecosystem for HE deployment. The discussion culminates in advocating for a hybrid HE and Post-Quantum Cryptography framework to future-proof Russia’s cryptographic infrastructure against emerging threats, supported by international cooperation within BRICS to reduce dependency on Western technologies. Overall, the article positions Homomorphic Encryption as a transformative technology essential for safeguarding data privacy, advancing national security, and reinforcing Russia’s digital independence in an increasingly complex global IT landscape.

This article explores the application of homomorphic encryption (HE) in secure ad targeting, addressing the critical challenge of balancing personalized advertising with user privacy concerns in the digital advertising ecosystem. We examine the fundamentals of HE, its integration into ad targeting processes, and propose a privacy-preserving ad platform architecture. Through a comprehensive feasibility analysis and performance evaluation, we assess the technical challenges, computational overhead, and scalability issues associated with implementing HE in real-time ad serving. Our findings indicate that while HE offers strong privacy guarantees, it currently faces limitations in terms of latency and throughput compared to traditional ad targeting methods. We analyze the trade-offs between privacy protection and targeting effectiveness, highlighting the impact on ad relevance and personalization. The article also discusses future directions, including advancements in HE algorithms, integration with other privacy-enhancing technologies, and regulatory considerations. By synthesizing current research and experimental results, this work provides valuable insights into the potential of HE to revolutionize privacy-preserving ad targeting, paving the way for a more secure and privacy-conscious digital advertising future.

The advances in sensing capabilities of smartphones give rise to a variety of mobile participatory sensing applications that collect users' personal data. Because of the existence of both sensitive, private personal data, and untrusted aggregator, serious privacy concerns on users arise. Currently, existing privacy preserving data collection methods either require bidirectional communications between an untrusted aggregator and mobile users in every aggregation period, or have high computation or communication overhead. To address these problems, we propose an efficient data aggregation approach by which an untrusted aggregator in mobile sensing can collect the statistics over the data contributed by multiple mobile users, while supporting privacy preservation of each user and data integrity verification. In this approach, information hiding and homomorphic encryption are applied to guarantee the data privacy of mobile users. In detail, a breadth-first search tree is first constructed at the initial phase among the mobile users, and then the original datum of each user is perturbed among its neighbors in ciphertext space by using information hiding and homomorphic encryption. The evaluations of our approach show that our protocol requires lower communication and computation overhead and thus more feasible for the computation constrained mobile devices. Copyright © 2016 John Wiley & Sons, Ltd.

Multi-key homomorphic encryption (MKHE) is crucial for secure collaborative computing, yet it suffers from high multiplicative depth and computational overhead during Look-Up Table (LUT) evaluations, particularly for large input domains. To address these challenges, this paper proposes an optimized LUT evaluation method based on multivariate polynomial approximation. Specifically, we partition the high-dimensional input space into several lower-dimensional variables to design low-depth multivariate polynomials. By integrating blockwise encoding and tensor-based transformations, we construct a parallelizable evaluation framework that maps multivariate functions into a high-dimensional polynomial-coefficient space. This approach allows for efficient parallel processing and effective noise management. Theoretical analysis demonstrates that our method significantly reduces the multiplicative depth from O(l) to O(l/α), indicating its robustness and efficiency in large-scale LUT scenarios.

The purpose of this paper is to explore key concepts and ideas surrounding the security challenges of cloud computing, with particular reference to configuration having considerable impact on securing cloud services. It is critical that the concepts of exploitation and vulnerability, which evidence shows exist in Cloud use, are discussed and understood in order to enhance security protection and user awareness. There are considerable benefits of cloud computing systems, however this paper will primarily focus on the limitations that have emerged through inaccurate configuration setups of both the platforms and applications, and how these might be addressed. It also will highlight some research work in the area that offers possible solutions to the identified issues in Cloud security.