Homomorphic Encryption Algorithm Research Articles

Secured and cloud-based electronic health records by homomorphic encryption algorithm

This uses homomorphic encryption in cloud-based platforms to improve electronic health records (EHR) security and accessibility. Protecting sensitive medical data while enabling data processing and analysis is the main goal. The study examines how homomorphic encryption protects EHR data privacy and integrity. Its main purpose is to reduce risks of unauthorized access and data breaches to build trust between healthcare professionals and patients in digital healthcare. The research uses homomorphic encryption to safeguard cloud EHR storage and transmission. Results will highlight the algorithm's influence on data security and computing efficiency, revealing its potential use in healthcare to protect patient privacy and meet regulatory requirements. Results from dataset of patient health metrics show in the 1st instance sample data for 5 instances with ages between 57 to 88, blood pressure (BP) values from 33 to 85, glucose values from 5 to 99, and heart rate values from 24 to 88. In another study of 5 patients, cholesterol levels ranged from 10 to 80 mg/dL, body mass index (BMI) from 10 to 96 kg/m², smoking status from 14 to 79, and medication adherence from 6 to 78%.

Development of Heuristic Strategy With Hybrid Encryption for Energy Efficient and Secure Data Storage Scheme in Blockchain‐Based Mobile Edge Computing

ABSTRACTInternet of Things (IoT) devices is extensively employed to collect physiological health data and provide diverse services to end‐users. Nevertheless, in recent applications, cloud computing‐based IoT proves beneficial for standard data storage and ensuring high‐security information sharing. Due to limitations in battery capacity, storage, and computing power, IoT devices are often considered resource‐constrained. Consequently, data signing by IoT devices, aimed at ensuring data integrity and authentication, typically demands significant computational resources. Unsafe data storage and high latency are considered as the major issues in the IoT‐based data storage mechanism for duplicating and misusing the information while it is stored in the cloud database. Hence, blockchain technologies are needed to provide high security over the stored data. Hence, the research aimed to implement an efficient blockchain‐based data storage system in mobile edge computing, safeguarding data from unauthorized access. In this approach, it contains four layers that are cloud layer, the entity layer, the block‐chain layer, and the edge computing layer. The user's data are stored in the optimal location in the entity layer, where the data storing location is find out using the proposed Hybrid Battle Royale with Archimedes Optimization Algorithm (HBRAOA). In the edge computing layer, an optimal key‐based homomorphic encryption algorithm using Elliptic Curve Cryptography (ECC) is introduced to encrypt data with the most optimal key, ensuring secure storage. This encryption method leverages the same HBRAOA to enhance the efficiency. Next, the digital signature is demonstrated to give the authorization of the user, and it is distributed to the blockchain layer. Thus, the indexes of the shared data are stored in the blockchain layer to avoid fault tolerance and tamper‐proofing. Finally, the cloud layer receives the valuable encrypted data, and the authenticated users with known encrypted keys are able to access the data by decrypting them. The result analysis shows that the performance of the developed model, which attains 27%, 98%, 35%, and 18% enhanced than Particle Swarm Optimization (PSO)‐ECC, Black Widow Optimization (BWO)‐ECC, Battle Royale Optimization (BRO)‐ECC and Archimedes Optimization Algorithm (AOA)‐ECC. The efficiency of the proposed blockchain‐based mobile edge computing scheme with the optimization strategy is validated by conducting several similarity measures over the conventional methods.

Advanced Encryption Techniques for Database Security in Cloud Environments

This technical article explores advanced encryption techniques for enhancing database security in cloud environments, addressing the growing need for robust data protection as organizations increasingly migrate to cloud-based solutions. The article covers encryption standards such as AES-256, RSA, and Elliptic Curve Cryptography, as well as key management approaches using Hardware Security Modules (HSMs) and Key Management Services (KMS). It also delves into cutting-edge concepts like homomorphic encryption and quantum-resistant algorithms. The discussion encompasses performance optimization strategies and regulatory compliance considerations, providing a comprehensive overview of the current state and future trends in cloud database security.

Strengthening Cloud Security Through Advanced Encryption and Anomaly Detection Techniques for Secure Data Storage and Transmission

The paper makes sense to be concerned about data security and privacy as the dominance of the cloud in the data storage and management space begins. This paper presents a study on enhancing the robustness of the cloud by integrating advanced encryption algorithms with robust anomaly detection mechanisms in order to safeguard sensitive data stored in it. With traditional encryption methods, a strong foundation is laid to protect the data. However, due to the ever-evolving sophistication of cyberattacks, which require more dynamic protection methods, it is important to always look forward to new methods for protecting data from increasingly sophisticated cyberattacks. This paper combines state-of-the-art techniques in encryption, including homomorphic encryption and quantum-resistant algorithms, together with machine learning-based advanced anomaly detection techniques to detect suspicious activities in real-time. The framework uses real-time encryption mechanisms during data transmission together with anomaly detection systems that monitor the access and activity logs in real-time. These systems use deep learning algorithms such as convolutional neural networks and long short-term memory to detect unusual patterns so they can offer multi-layered security. The frameworks are evaluated using simulations on large datasets of cloud activities. Results show high accuracy in the anomaly detection task with minimal degradation in the need for computational efficiency to scale large cloud environments.

Blockchain‐Enabled Decentralized Healthcare Data Exchange: Leveraging Novel Encryption Scheme, Smart Contracts, and Ring Signatures for Enhanced Data Security and Patient Privacy

ABSTRACTThe healthcare industry has undergone a digital transformation in recent years, with the adoption of electronic health records (EHRs) becoming increasingly prevalent. While this digitization offers various advantages, concerns regarding the security and privacy of sensitive medical data have also intensified. Data breaches and cyber‐attacks targeting healthcare organizations have underscored the need for robust solutions to protect patient data. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature, which ensures secure and transparent data recording. This paper proposes a novel approach that combines blockchain with advanced encryption scheme and privacy protection technique to establish a secure and privacy protected medical data sharing environment. The proposed system consists of three phases such as initialization phase, data processing phase, and authentication phase. The hybrid Feistal‐Shannon homomorphic encryption algorithm (HFSHE) is proposed to encrypt the medical data to ensure data confidentiality, integrity, and availability. Ring signature is integrated to the system to provide additional anonymity and protect the identities of the participants involved in data transactions. In addition, the smart contract developed performs authentication checks on users, generates a time seal, and verifies the ring signature. Through this enhancement, the system becomes more resilient to both external and internal threats, enhancing overall security as well as privacy. A comprehensive security analysis is conducted to compare the proposed method's performance against existing techniques. The results demonstrate the effectiveness of the proposed approach in safeguarding sensitive medical information within the blockchain ecosystem.

Dynamic Range Query Privacy-Preserving Scheme for Blockchain-Enhanced Smart Grid Based on Lattice

Blockchain-enhanced Smart Grid has attracted a lot of attention from scholars in recent years, due to its excellent decentralization, anti-collusion and immutability. With the wide deployment of blockchain and the popularity of more accurate intelligent applications, its role in Smart Grid is becoming more and more irreplaceable. However, because of the threat of quantum computer, it still confronts the risk of privacy disclosure. In this paper, we propose a novel and dynamic range query privacy-preserving scheme for blockchain-enhanced Smart Grid based on lattice. In this scheme, lattice-based homomorphic encryption algorithm is designed to resist the attack of quantum computer and realizes data aggregation to improve efficiency. In particular, this article designs a dynamic range query method with the aid of consortium blockchain by using proxy re-encryption. This method avoids the communication pressure caused by repeated data collection and improves the query efficiency and user experience. In addition, dynamic ciphertext and users update are considered, which further improves the flexibility and feasibility of the scheme. Last but not least, security analysis, exhaustive performance analysis and experiments show that our proposed scheme meets the requirements of dynamic, privacy, security and low computational cost.

Federated Learning with Homomorphic Encryption for Ensuring Privacy in Medical Data

Federated Learning (FL) is a machine learning methodology that allows remote devices to collectively train a learning system without sharing their data. FL-based methods provide enhanced secure privacy by transmitting only localized model variables, learned with local information, from dispersed devices to a centralized controller. There is a potential for a centralized server or malicious individuals to deduce or get sensitive private data by analyzing the structure and variables of regional learning networks. This study incorporates the FL process into the deep learning process of medical prototypes in an Internet of Things (IoT)-based medical facility. A Secured Medical Homomorphic Encryption Algorithm (SMHEA) is proposed in this research to ensure medical data privacy. Cryptographic primitives, such as masking and homomorphic cryptography, are used to enhance the security of local modeling. This prevents adversaries from deducing confidential health information via assaults like model restoration or modeling inversion. The primary determinant for assessing the regional modeling's contributions to the universal model during each training stage is the quality of the databases possessed by various individuals rather than the typically used metric of database dimension in deep learning. A dropout-tolerant approach is suggested, where the FL procedure would continue as long as the total amount of online customers remains over a certain level. By doing a security evaluation, it is evident that the suggested approach effectively ensures data privacy. Theoretical analysis is conducted on accuracy, computation time, and communication error. An example of clinical applications is the categorization of skin lesions using training photos from the HAM10000 medical database. The experimental findings demonstrate that the suggested system had favorable performance and privacy preservation outcomes compared to current methods.

Similarity calculation based on homomorphic encryption

AbstractIn recent years, some homomorphic encryption algorithms have been proposed to provide additive homomorphic encryption and multiplicative homomorphic encryption. However, similarity measures are required for searches and queries under homomorphic encrypted ciphertexts. Therefore, this study considers cosine similarity, angular similarity, Tanimoto similarity, and soft cosine similarity and combines homomorphic encryption algorithms for similarity calculation to propose homomorphic encryption‐based cosine similarity (HE‐CS), homomorphic encryption‐based angular similarity (HE‐AS), homomorphic encryption‐based Tanimoto similarity (HE‐TS), and homomorphic encryption‐based soft cosine similarity (HE‐SCS). This study proposes mathematical models to prove the proposed homomorphic encryption‐based similarity calculation methods and gives practical cases to explain the feasibility of the proposed HE‐CS, HE‐AS, HE‐TS, and HE‐SCS. Furthermore, this study proposes normalized entropy and normalized Gini impurity as evaluation factors to measure the randomness and confusion of ciphertext. In experiments, the values of normalized entropy and normalized Gini impurity are higher than 0.999, which indicates significant differences between plaintexts and ciphertexts. Moreover, the encryption time and decryption time of the proposed homomorphic encryption‐based similarity calculation methods have been evaluated under different security strengths.

Optimized Homomorphic Encryption (OHE) algorithms for protecting sensitive image data in the cloud computing environment

Optimized Homomorphic Encryption (OHE) algorithms for protecting sensitive image data in the cloud computing environment

PDPHE: Personal Data Protection for Trans-Border Transmission Based on Homomorphic Encryption

In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing.

The Next Frontier of Security: Homomorphic Encryption in Action

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.

Enhanced Secure Storage of Big Data at Rest with Improved ECC and Paillier Homomorphic Encryption Algorithms

Enhanced Secure Storage of Big Data at Rest with Improved ECC and Paillier Homomorphic Encryption Algorithms

An SGX-based online voting protocol with maximum voter privacy

Electronic voting (E-voting), a crucial method in modern society, balances efficiency with the need for equity, reliability, and privacy to accurately record votes and maintain democracy. However, there is the challenge of potential leakage of voting information in scenarios where administrators of voting system may act maliciously. Moreover, contemporary collaborative counting methods face a significant increase in the cost of storage and heightened communication overhead in large-scale e-voting. In response, this paper proposes an innovative online voting protocol that integrates Intel SGX, homomorphic encryption, and zero-knowledge proof. This protocol aims to cut down the cost of communication and storage while maximizing the protection of voter privacy. The protocol relies on SGX to provide a trusted execution environment for the voting process, guarding against malicious attacks from external software or high-privileged administrators. In the proposed protocol, voters apply homomorphic encryption algorithms to locally encrypt their ballots, no longer relying on other entities (such as the voting system or SGX) for encryption. Then the encrypted ballots are submitted to SGX to mitigate the threat of data leakage in SGX. Through the adoption of zero-knowledge proof technology, the voting protocol can verify the legitimacy of votes without revealing their content. The proposed solution introduces the SGXVOT architecture, comprising two enclaves – Enclave V responsible for tallying individual encrypted votes, and Enclave T responsible for decrypting and publishing voting results. This design ensures that individual voters’ ballots remain in ciphertext, preserving the confidentiality of votes and voter privacy. Additionally, the protocol incorporates a “One-Time Pad” encryption communication protocol to guarantee the confidentiality of communication messages between Enclave V and Enclave T. The thorough security analysis and performance evaluation demonstrate the superior performance of the proposed solution in terms of security, practicality, and scalability.

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.

A Blockchain-based carbon emission security accounting scheme

To solve the problem of climate warming, countries around the world have paid special attention to the construction of carbon governance. Carbon emission accounting is an important policy tool to control the vented CO2. But at present, there are third-party agencies in carbon emission accounting that cannot ensure the fairness and impartiality of accounting, and there may be risks such as illegal use and leakage of sensitive information in the process of carbon emission data transmission. Therefore, We design the blockchain-based carbon emission security accounting scheme (BCESAS) and propose cross-chain verification contract to ensure the efficiency of cross-chain information accounting. In addition, bilinear pairing is used to ensure data integrity, and we encrypt private data using an improved and more secure homomorphic encryption algorithm to ensure that privacy is not leaked during the transfer of carbon emission data, which is more efficient than other homomorphic encryption algorithms. We also use reputation mechanism to regulate the behavior of carbon emission auditors. The theoretical and experimental analysis demonstrates that BCESAS can verify the integrity, correctness and privacy of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain.

Secure medical data on cloud storage via DNA homomorphic encryption technique

BackgroundCloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. MethodsHowever, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using Rider Optimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. ResultsThe efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10 %, 20 %, and 35 % than OHE, HE and ECC algorithms.

Homomorphic encryption algorithm providing security and privacy for IoT with optical fiber communication

Homomorphic encryption algorithm providing security and privacy for IoT with optical fiber communication

Preliminary Experiments of a Real-World Authentication Mechanism Based on Facial Recognition and Fully Homomorphic Encryption

In the current context in which user authentication is the first line of defense against emerging attacks and can be considered a defining element of any security infrastructure, the need to adopt alternative, non-invasive, contactless, and scalable authentication mechanisms is mandatory. This paper presents initial research on the design, implementation, and evaluation of a multi-factor authentication mechanism that combines facial recognition with a fully homomorphic encryption algorithm. The goal is to minimize the risk of unauthorized access and uphold user confidentiality and integrity. The proposed device is implemented on the latest version of the Raspberry Pi and Arduino ESP 32 modules, which are wirelessly connected to the computer system. Additionally, a comprehensive evaluation, utilizing various statistical parameters, demonstrates the performance, the limitations of the encryption algorithms proposed to secure the biometric database, and also the security implications over the system resources. The research results illustrate that the Brakerski–Gentry–Vaikuntanathan algorithm can achieve higher performance and efficiency when compared to the Brakerski–Fan–Vercauteren algorithm, and proved to be the best alternative for the designed mechanism because it effectively enhances the level of security in computer systems, showing promise for deployment and seamless integration into real-world scenarios of network architectures.

Construction of real-time sharing model of energy big data based on end cloud collaboration technology

A real-time sharing model of energy big data based on end cloud collaboration technology is built to safely and efficiently share energy big data in all fields. Through the collaboration between the client layer and the cloud platform layer in the end cloud collaboration module, combined with the vertical federation learning algorithm and the homomorphic encryption algorithm, the energy big data knowledge in various fields is extracted and encrypted, and the encrypted knowledge is stored in the cloud platform as shared data. After the blockchain module combines the smart contract identification coding and parsing of such shared ciphertext, the ciphertext key is provided to the data user, and the shared energy big data plaintext is obtained after decryption, so as to realize the real-time security sharing of energy big data. According to the result analysis, the model performs well in data knowledge extraction and encryption, and has a good effect in ensuring the security and reliability of energy big data sharing. At the same time, the identification coding and analysis time of shared data knowledge is relatively short, making energy big data can be shared in real time. These results demonstrate the potential and feasibility of the model in facilitating big data sharing in the energy sector.