Homomorphic Encryption System Research Articles

Innovations in Cloud Database Security: Addressing Emerging Threats

This article examines the latest innovations in cloud database security, addressing the challenges posed by an increasingly sophisticated threat landscape. As organizations rapidly adopt cloud databases, they face expanding attack surfaces and evolving cyber threats, including AI-powered attacks and quantum computing risks. The article explores cutting-edge security measures such as quantum-resistant encryption, homomorphic encryption, zero-trust architectures, and AI-driven threat detection systems. It also discusses the integration of security into database development processes through DevSecOps practices. The article highlights the importance of a multi-faceted approach to cloud database security, combining advanced technologies with proactive strategies. By analyzing recent advancements and their impacts, this article provides insights into the future of cloud database security and offers guidance for organizations seeking to protect their sensitive data in complex cloud environments.

Cluster Heat Selection Optimization in Wsn Via Genetic Based Evolutionary Algorithm and Secure Data Transmission Using Paillier Homomorphic Cryptosystem

Introduction: Wireless Sensor Networks (WSNs) consist of sensor nodes requiring energy-saving measures to extend their lifespan. Traditional solutions often lead to premature node failure due to non-adaptive network setups. Differential Evolution (DE) and Genetic Algorithms (GA) are two key evolutionary algorithms used for optimizing cluster head (CH) selection in WSNs to enhance energy efficiency and prolong network lifetime.Methods: This study compares DE and GA for CH selection optimization, focusing on energy efficiency and network lifespan. It also introduces an improved decryption method for the Paillier homomorphic encryption system to reduce decryption time and computational cost.Results: Experiments show GA outperforms DE in the number of rounds for the first node to die (FND) and achieves a longer network lifespan, despite fewer rounds for the last node to die (LND). GA has slower fitness convergence but higher population fitness values and significantly faster decoding speeds.Conclusion: GA is more effective than DE for CH selection in WSNs, leading to an extended network lifespan and better energy efficiency. Despite slower fitness convergence, GA's higher fitness values and improved decoding speeds make it a superior choice. The enhancements to the Paillier encryption system further increase its efficiency, offering a robust solution for secure and efficient WSN operation

Experimental Quantum Homomorphic Encryption Using a Quantum Photonic Chip.

A fully homomorphic encryption system enables computation on encrypted data without the necessity for prior decryption. This facilitates the seamless establishment of a secure quantum channel, bridging the server and client components, and thereby providing the client with secure access to the server's substantial computational capacity for executing quantum operations. However, traditional homomorphic encryption systems lack scalability, programmability, and stability. In this Letter, we experimentally demonstrate a proof-of-concept implementation of a homomorphic encryption scheme on a compact quantum chip, verifying the feasibility of using photonic chips for quantum homomorphic encryption. Our work not only provides a solution for circuit expansion, addressing the longstanding challenge of scalability while significantly reducing the size of quantum network infrastructure, but also lays the groundwork for the development of highly sophisticated quantum fully homomorphic encryption systems.

Analysis on Privacy Preserving Clustering Methods for Big Datasets Using Random Number Generators

Due to the growing requirement for powerful processing capabilities, safeguarding and processing sensitive data in big data systems has become an increasingly prevalent issue. Additional effort is required to secure sensitive data on a system with numerous connections, each of which has its own privacy policy, and to exchange cryptographic keys in accordance with the appropriate protocols for each of these parties. Data encrypted using homomorphic encryption algorithms can undergo the same kinds of arithmetic operations as data encrypted using plaintext. This allows for the outsourcing of computations to cloud services while also providing data privacy by processing in the encrypted domain. Also, key exchange sessions become easier for everyone involved because of this. This study presents new privacy-preserving clustering algorithms and homomorphic encryption systems that may together operate on a shared HPC infrastructure, like the cloud. Therefore, it is not necessary for the participants in this system to have a lot of processing power, as the system would distribute the most power-intensive jobs to any provider of cloud computing. Multiple clustering techniques can have their distance matrices computed in a way that does not compromise user privacy using our technology.

Analysis on Privacy Preserving Clustering Methods for Big Datasets Using Random Number Generators

Due to the growing requirement for powerful processing capabilities, safeguarding and processing sensitive data in big data systems has become an increasingly prevalent issue. Additional effort is required to secure sensitive data on a system with numerous connections, each of which has its own privacy policy, and to exchange cryptographic keys in accordance with the appropriate protocols for each of these parties. Data encrypted using homomorphic encryption algorithms can undergo the same kinds of arithmetic operations as data encrypted using plaintext. This allows for the outsourcing of computations to cloud services while also providing data privacy by processing in the encrypted domain. Also, key exchange sessions become easier for everyone involved because of this. This study presents new privacy-preserving clustering algorithms and homomorphic encryption systems that may together operate on a shared HPC infrastructure, like the cloud. Therefore, it is not necessary for the participants in this system to have a lot of processing power, as the system would distribute the most power-intensive jobs to any provider of cloud computing. Multiple clustering techniques can have their distance matrices computed in a way that does not compromise user privacy using our technology.

Homomorphic encryption domain asymmetric fingerprinting scheme for 3D models of oblique photography

AbstractExisting copyright protection schemes for 3D models of oblique photography predominantly utilize digital watermarking technology, most of which suffer from shortcomings such as the potential false accusation of legitimate users and the inability to effectively trace unauthorized distributors. In response to these challenges, this article proposes a homomorphic encryption domain asymmetric fingerprinting scheme for 3D models of oblique photography. Initially, the fingerprints to be embedded are collaboratively generated by the transacting parties, with the publisher remaining unaware of the user's actual fingerprint. This approach ensures the effective implementation of the asymmetric nature of the fingerprinting scheme. Subsequently, potential geometric and non‐geometric attacks are considered prior to fingerprint embedding. Robustness is enhanced for the proposed solution through relative coordinate transformations and normalization procedures. Ultimately, the embedding and extraction of fingerprints are achieved through the application of an effective bit parity check method, in conjunction with the vertex‐paradigm projection length mapping mechanism and the Paillier homomorphic encryption system, minimizing embedding distortion while ensuring data security. Experimental results demonstrate that the proposed solution exhibits satisfying robustness against common manipulations, such as cropping, simplification, and geometric attacks. Additionally, the scheme effectively safeguards legitimate users from false accusations and accurately traces maliciously authorized users.

Preserving Big Data Privacy in Cloud Environments Based on Homomorphic Encryption and Distributed Clustering

Cloud computing has grown in popularity in recent years because to its efficiency, flexibility, scalability, and the services it provides for data storage and processing. Still, big businesses and organizations have severe concerns about protecting privacy and data security while processing these massive volumes of data. This paper proposes approach that intends to enhance efficiency in delivering advanced data protection, hence filling security holes, by enhancing data protection from various big data sources. A partial homomorphic encryption system is used to encrypt data created by many sources or users and processed in the cloud without decrypting it, hence protecting data from attackers. Extremely Distributed Clustering (EDC) has also been applied to partition large datasets into many cloud computing node subsets. This method can ensure privacy and protect data while also enhancing the effectiveness and performance of big data analytics. According to the results, the proposed technique was faster and gave improved encryption performance by around 23-28%.

Algebraic Attacks on RAIN and AIM Using Equivalent Representations

Designing novel symmetric-key primitives for advanced protocols like secure multiparty computation (MPC), fully homomorphic encryption (FHE) and zero-knowledge proof systems (ZK), has been an important research topic in recent years. Many such existing primitives adopt quite different design strategies from conventional block ciphers. Notable features include that many of these ciphers are defined over a large finite field, and that a power map is commonly used to construct the nonlinear component due to its efficiency in these applications as well as its strong resistance against the differential and linear cryptanalysis. In this paper, we target the MPC-friendly ciphers AIM and RAIN used for the post-quantum signature schemes AIMer (CCS 2023 and NIST PQC Round 1 Additional Signatures) and Rainier (CCS 2022), respectively. Specifically, we can find equivalent representations of 2-round RAIN and full-round AIM, respectively, which make them vulnerable to either the polynomial method, or the crossbred algorithm, or the fast exhaustive search attack. Consequently, we can break 2-round RAIN with the 128/192/256-bit key in only 2111/2170/2225 bit operations. For full-round AIM with the 128/192/256-bit key, we could break them in 2136.2/2200.7/2265 bit operations, which are equivalent to about 2115/2178/2241 calls of the underlying primitives. In particular, our analysis indicates that AIM does not reach the required security levels by the NIST competition.

A certificateless multi-dimensional data aggregation scheme for smart grid

In the context of the smart grid, the use of smart meters to regularly collect electricity data from customers poses a significant risk of privacy disclosure. Furthermore, traditional data aggregation schemes assume a fully trusted authority, neglecting the possibility of malicious trusted third parties and collusive attacks from control centers and gateways, which raises privacy invasion concerns for users. To address this issue, this article proposes a multi-dimensional data aggregation scheme based on certificateless public key cryptography for smart grid, which utilizes Paillier homomorphic encryption system in a fog computing-based architecture and implements authentication and key negotiation among member users. This scheme is designed to resist collusive attacks from control centers (CC) and fog nodes (FNs), while also protecting users’ privacy even in the face of a malicious key generation center (KGC). Detailed security analysis demonstrates that the proposed scheme meets the security requirements of the smart grid. Additionally, Performance analysis shows that the proposed scheme can effectively reduce the computational overhead of both smart meters and aggregators compared to existing multidimensional data aggregation schemes.

BVOT: self-tallying boardroom voting with oblivious transfer

ABSTRACT A boardroom election is an election with a small number of voters carried out with public communications. We present BVOT, a self-tallying boardroom voting protocol with ballot secrecy, fairness, and dispute-freeness. BVOT works by using a multiparty threshold homomorphic encryption system in which each candidate is associated with a set of masked primes. Each voter engages in an oblivious transfer with an untrusted distributor: the voter selects the index of a prime associated with a candidate and receives the selected prime in masked form. The voter then casts their vote by encrypting their masked prime and broadcasting it to everyone. By hiding the mapping between primes and candidates, BVOT provides voters with insufficient information to carry out effective cheating. The threshold feature prevents anyone from computing any partial tally—until everyone has voted. In contrast to some existing boardroom voting protocols, BVOT does not rely on any zero-knowledge proof; instead, it uses oblivious transfer to assure ballot secrecy and correct vote casting. Also, BVOT can handle multiple candidates in one election. BVOT prevents cheating by hiding crucial information: an attempt to increase the tally of one candidate might increase the tally of another candidate.

Privacy-Preserving Online Medical Prediagnosis Training Model Based on Soft-Margin SVM

Online medical prediagnosis systems have already shown great achievement in providing the guidance of healthcare services with lower time and cost. Achieving a high-precision medical primary diagnosis system faces many severe challenges on the privacy of individual health information, the distributed storage of medical data and the diversity of the disease. In this paper, we propose an efficient and privacy-preserving framework for obtaining a pre-clinical guide model, which allows an authorized data analysis center to train a disease classifier using a combination of medical data gathered from different entities. Our proposed scheme is based on soft-margin support vector machine (SVM) which takes Taylor polynomial of exponential-loss as penalty. Our scheme achieves the following advantages: the trained model can tolerate some abnormal samples therefore has higher generalization ability, and the training process can constraint the inefficient operations in the encrypted domain thus leads to the availability of partial homomorphic encryption system. Lately, we prove that the proposed scheme achieves the goal of medical prediagnosis system construction and data without privacy leakage to data analysis center and model parameters without exposure to data providers, as well as demonstrating its utility and efficiency using real-world medical datasets.

A New Asymmetric Fully Homomorphic Encryption Scheme for Cloud Banking Data

Most banks in our time still use the common traditional systems of high cost and relatively slow, we are now in the era of speed and technology, and these systems do not keep pace with our current age, so saving cost and time will be considered a fantastic thing for banks. The way to that is to implement cloud computing strategies with Considering data security and protection when it comes to using the cloud. The best solution to protect data security on the cloud is fully homomorphic encryption systems. The time it takes to encrypt and decrypt data is one of the main barriers it faces. Our current research provides a new algorithm for a publicly-keyed encryption system to keep bank data from tampering and theft when stored on the cloud computing platform, and our new system achieves fully Homomorphic Encryption, which allows mathematical operations to be performed on the encrypted text without the need for the original text. The security of the new system depends on the issue of analyzing huge integers, which reach 2048 bits, to their prime factors, which are considered almost impossible or unsolvable. A banking application has also been created that encrypts the data and then stores it on the cloud. The application allows the user to create accounts and deposits, transfer and withdraw funds, and everything related to banking matters.

Threats to Wireless Sensor Networks and Approaches to Use Homomorphic Encryption to Secure Its Data

The article considers the basic principles of building modern wireless sensor networks. The general structures of such networks, their application spheres, their strengths and weaknesses are considered. Special attention is paid to the problem of security of processed and transmitted data, as well as existing approaches to providing reliable cryptographic protection of these data in wireless sensor networks. It is shown that at working out of mechanisms of maintenance of protection of the information in sensor networks first of all it is necessary to consider restrictions inherent in such networks on volume of memory, speed of used processors, and also power consumption. This leads to a more careful and weighted choice of encryption algorithms for use in sensor networks, both for direct encryption of data collected and transmitted by sensor networks nodes, and for auxiliary functions such as calculating the optimal network route by routers that are part of sensor networks. The article focuses on the possibility of using homomorphic encryption systems to protect data collected by sensors and transmitted via wireless connections. The advantages of full and partial homomorphic encryption systems in terms of their application to sensor networks with sensors measuring environmental parameters are shown. The analysis showed that using partial homomorphic encryption over the Paye Cryptosystem to hide data in sensor networks had reasonable grounds.

Semi-Parallel logistic regression for GWAS on encrypted data

BackgroundThe sharing of biomedical data is crucial to enable scientific discoveries across institutions and improve health care. For example, genome-wide association studies (GWAS) based on a large number of samples can identify disease-causing genetic variants. The privacy concern, however, has become a major hurdle for data management and utilization. Homomorphic encryption is one of the most powerful cryptographic primitives which can address the privacy and security issues. It supports the computation on encrypted data, so that we can aggregate data and perform an arbitrary computation on an untrusted cloud environment without the leakage of sensitive information.MethodsThis paper presents a secure outsourcing solution to assess logistic regression models for quantitative traits to test their associations with genotypes. We adapt the semi-parallel training method by Sikorska et al., which builds a logistic regression model for covariates, followed by one-step parallelizable regressions on all individual single nucleotide polymorphisms (SNPs). In addition, we modify our underlying approximate homomorphic encryption scheme for performance improvement.ResultsWe evaluated the performance of our solution through experiments on real-world dataset. It achieves the best performance of homomorphic encryption system for GWAS analysis in terms of both complexity and accuracy. For example, given a dataset consisting of 245 samples, each of which has 10643 SNPs and 3 covariates, our algorithm takes about 43 seconds to perform logistic regression based genome wide association analysis over encryption.ConclusionsWe demonstrate the feasibility and scalability of our solution.

New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations

Cloud computing is the on-request accessibility of computer system resources, specially data storage and computing power, without direct dynamic management by the client. In the simplest terms, cloud computing means storing and accessing data and programs over the Internet instead of your computer’s hard drive. Along the improvement of cloud computing, more and more applications are migrated into the cloud. A significant element of distributed computing is pay-more only as costs arise. Distributed computing gives strong computational capacity to the general public at diminished cost that empowers clients with least computational assets to redistribute their huge calculation outstanding burdens to the cloud, and monetarily appreciate the monstrous computational force, transmission capacity, stockpiling, and even reasonable programming that can be partaken in a compensation for each utilization way Tremendous bit of leeway is the essential objective that forestalls the wide scope of registering model for clients when their secret information are expended during the figuring procedure. Critical thinking is a system to arrive at the pragmatic objective of specific instruments that tackles the issues as well as shield from pernicious practices.. In this paper, we examine secure outsourcing for large-scale systems of linear equations, which are the most popular problems in various engineering disciplines. Linear programming is an operation research technique formulates private data by the customer for LP problem as a set of matrices and vectors, to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some arbitrary one while protecting sensitive input/output information. Identify that LP problem solving in Cloud component is efficient extra cost on cloud server. In this paper we are utilizing Homomorphic encryption system to increase the performance and time efficiency.

Experimental Demonstration of Quantum Fully Homomorphic Encryption with Application in a Two-Party Secure Protocol

A fully homomorphic encryption system hides data from unauthorized parties, while still allowing them to perform computations on the encrypted data. Aside from the straightforward benefit of allowing users to delegate computations to a more powerful server without revealing their inputs, a fully homomorphic cryptosystem can be used as a building block in the construction of a number of cryptographic functionalities. Designing such a scheme remained an open problem until 2009, decades after the idea was first conceived, and the past few years have seen the generalization of this functionality to the world of quantum machines. Quantum schemes prior to the one implemented here were able to replicate some features in particular use-cases often associated with homomorphic encryption but lacked other crucial properties, for example, relying on continual interaction to perform a computation or leaking information about the encrypted data. We present the first experimental realisation of a quantum fully homomorphic encryption scheme. We further present a toy two-party secure computation task enabled by our scheme. Finally, as part of our implementation, we also demonstrate a post-selective two-qubit linear optical controlled-phase gate with a much higher post-selection success probability (1/2) when compared to alternate implementations, e.g. with post-selective controlled-$Z$ or controlled-$X$ gates (1/9).

Survey on Homomorphic Encryption and Address of New Trend

Encryption is the process of disguising text to ensure the confidentiality of data transmitted from one party to another. Homomorphic encryption is one of the most important encryption-related processes which allows performing operation over encrypted data. Using different public key algorithms, homo-morphic encryption can be implemented in any scheme. There are many encryption algorithms to secure operations and data stor-age, which after calculations can obtain the same results. While there is a considerable contribution and enhancement in the field of homomorphic encryption for various performance metrics, there is still an necessity to clarify the applications dealing with this technology. Recently, many distinguished research papers have been filed to address the need for various applications of homomorphic encryption. Recently, many distinguished research papers have been filed to address the need for various applications of homomorphic encryption. Example of these applications but not limited to : Vehicle to Vehicle (v2v) secure communication, cloud security, Vehicular ad-hoc networks (VANET), Blockchain, E-Voting, Data mining with privacy preserving and healthcare sector. This article aims to introduce a literature survey to close the gap in homomorphic encryption systems and their applications in the protection of privacy. We focus on above-mentioned applications and present our recommendations for future work.

Simulation of a Homomorphic Encryption System

Abstract Cryptology is defined as the science of making communication incomprehensible to third parties who have no right to read and understand the data or messages. Cryptology consists of two parts, namely, cryptography and cryptanalysis. Cryptography analyzes methods of encrypting messages, and cryptanalysis analyzes methods of decrypting encrypted messages. Encryption is the process of translating plaintext data into something that appears to be random and meaningless. Decryption is the process of converting this random text into plaintext. Cloud computing is the legal transfer of computing services over the Internet. Cloud services let individuals and businesses to use software and hardware resources at remote locations. Widespread use of cloud computing raises the question of whether it is possible to delegate the processing of data without giving access to it. However, homomorphic encryption allows performing computations on encrypted data without decryption. In homomorphic encryption, only the encrypted version of the data is given to the untrusted computer to process. The computer will perform the computation on this encrypted data, without knowing anything on its real value. Finally, it will send back the result, and whoever has the proper deciphering key can decrypt the cryptogram correctly. The decrypted result will be equal to the intended computed value. In this paper, homomorphic encryption and their types are reviewed. Also, a simulation of somewhat homomorphic encryption is examined.

HARDWARE IMPLEMENTATION OF AN ENCRYPTION FOR ENHANCEMENT DGHV

In constructing a secure and reliable cloud computing environment, a fully homomorphic encryption (FHE) scheme is conceived as a major cryptographic tool, as it enables arbitrary arithmetic evaluation of a cipher text without revealing the plaintext. However, due to very high of fully homomorphic encryption systems stays impractical and unfit for real-time applications One way to address this restriction is by using graphics processing unit (GPUs) and field programmable gate arrays (FPGAs) to produce homomorphic encryption schemes. This paper represents the hardware implementation of an encryption for enhancement van Dijk, Gentry, Halevi and Vaikuntanathan’s (DGHV) scheme over the integer (DGHV10) using FPGA technology for high speed computation and real time results. The proposed method was simulated via Vivado system generator tools. Then design systems of fully homomrphic encryption are implemented in an FPGA hardware successfully using NEXYS 4 DDR board with ARTIX 7 XC7A100T FPGA. The Experimental results show that the FPGA- based fully homomorphic encryption system is 63 times faster than the simulation based implementation.

О возможности стойкой обфускации программ в одной модели облачных вычислений

In this paper we study the possibility of using a certain cloud computing model supplied with cryptoservers to obfuscate software programs. Earlier, we proposed this cloud computing model in our study of some information security problems for multi-client distributed computing over encrypted data. Based on this model, we proposed a new approach involving the use of threshold homomorphic cryptosystems for program obfuscation. The main result of this paper is a new definition of the resistance of obfuscation of programs in the cloud computing model and a theorem proving the cryptographic strength of the proposed algorithm of obfuscation of programs under the assumption of the existence of cryptographically strong threshold homomorphic encryption systems. The paper is organized as follows. In the introducing section we discuss the main aspects of the information security problems for cloud computing systems. Section 2 provides a description of the obfuscation program objectives, as well as a brief overview of the main achievements in its study. The next section provides general information about homomorphic cryptosystems. Section 4 describes a more special class of homomorphic cryptosystems - threshold homomorphic encryption systems. Section 5 introduces the cloud computing model, which is used as framework for our program obfuscation techniques. For this computing environment, in Section 6, the definition of the cryptographic strength of program obfuscation is formulated, a new method of program obfuscation using threshold homomorphic cryptosystems is described, and the cryptographic strength of the proposed obfuscation algorithm is proved.