Homomorphic Encryption Algorithm Research Articles

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.

When cryptography stops data science: Strategies for resolving the conflicts between data scientists and cryptographers

The advent of the digital era and computer-based remote communications has significantly enhanced the applicability of various sciences over the past two decades, notably data science (DS) and cryptography (CG). Data science involves clustering and categorizing unstructured data, while cryptography ensures security and privacy aspects. Despite certain CG laws and requirements mandating fully randomized or pseudonoise outputs from CG primitives and schemes, it appears that CG policies might impede data scientists from working on ciphers or analyzing information systems supporting security and privacy services. However, this study posits that CG does not entirely preclude data scientists from operating in the presence of ciphers, as there are several examples of successful collaborations, including homomorphic encryption schemes, searchable encryption algorithms, secret-sharing protocols, and protocols offering conditional privacy. These instances, along with others, indicate numerous potential solutions for fostering collaboration between DS and CG. Therefore, this study classifies the challenges faced by DS and CG into three distinct groups: challenging problems (which can be conditionally solved and are currently available to use; e.g., using secret sharing protocols, zero-knowledge proofs, partial homomorphic encryption algorithms, etc.), open problems (where proofs to solve exist but remain unsolved and is now considered as open problems; e.g., proposing efficient functional encryption algorithm, fully homomorphic encryption scheme, etc.), and hard problems (infeasible to solve with current knowledge and tools). Ultimately, the paper will address specific solutions and outline future directions to tackle the challenges arising at the intersection of DS and CG, such as providing specific access for DS experts in secret-sharing algorithms, assigning data index dimensions to DS experts in ultra-dimension encryption algorithms, defining some functional keys in functional encryption schemes for DS experts, and giving limited shares of data to them for analytics.

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.

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.

Development of computer application system and database testing based on data encryption technology

Abstract In this paper, we use the MapReduce programming framework for the development of a computer application system and design the vision module, control module and transaction module of the system based on MVC architecture. Focusing on the encryption design of the database, the data stored in the database is chunked through the Map function, and all the chunking results are aggregated based on the Reduce function, and synchronized encryption of the database is combined with the Paillier homomorphic encryption algorithm. On this basis, the system environment is configured, and the application system developed in this paper is tested, focusing on exploring the stability of the database. The test results show that the decryption output time is 21s when the Paillier encryption algorithm is 200, and the first set of database peak is 76% of the server load, which is within the tolerable range. Security test on the database: the results show that the probability of an attack on all ports with a database security factor less than 6 is around 0.3. There is no significant difference, and the database test is good.

Ciphertext face recognition system based on secure inner product protocol

User privacy data leakage is a major weakness of current plaintext face recognition systems. This article combines Pallier homomorphic encryption algorithm with inner product protocol and face recognition to construct a ciphertext face recognition system based on inner product protocol. Firstly, we integrate the Pallier homomorphic encryption algorithm into the inner product protocol process and design a secure inner product protocol. Through simulation experiments, it can be seen that the time and communication costs of the secure inner product can be reduced to and respectively at the expense of a portion of the applicable scope, where is the vector length. Secondly, we provide specific steps for combining FaceNet face recognition algorithm with secure inner product protocol, then design a ciphertext face recognition system that does not require a trusted third party and analyze its correctness, security and time cost. Through experiments, it can be concluded that the accuracy of plaintext face recognition and ciphertext face recognition on the test set is 98.78% and 98.78%, respectively. The application of Pallier homomorphic encryption algorithm has not affected its recognition performance, which means that the system can provide high accuracy face recognition services while protecting user privacy.

Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection without the demand to modify the existing model structures or to share any private data. Here, we proposed PPPML-HMI, a novel open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were discussed simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing the novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the strong privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we demonstrated the strong generalizability of PPPML-HMI in privacy-preserving federated learning on heterogeneous medical images.

A Secure and Optimization Based Clustering for Vertical and Horizontal Fragmentation in Distributed Database Management System

Distributed database systems are increasingly important due to the massive data output, and their effectiveness is largely based on their design. Two key processes, fragmentation and allocation, are used to improve the efficiency and efficacy of these systems. Effective data fragmentation requires both horizontal and vertical categorization of tuples. Advanced optimization techniques are used for both fragmentations, such as the Enhanced Arithmetic Optimization (EAO) algorithm with Opposition-based Learning (OBL) and Levy Flight Distributer (LFD) for vertical fragmentation and the hybrid Aquila Optimizer (AO) with Artificial Rabbit Optimization (ARO) algorithm for horizontal fragmentation. The fragmented data is securely transmitted using the Fully Homomorphic Encryption (FHE) algorithm. The implementation is executed using the Python language, and the performance of the proposed algorithms is evaluated using different performance parameters. The execution time analysis shows that the proposed EAOA algorithm consumes 5.5 seconds for vertical fragmentation, while the hybrid AARO algorithm takes 5.9 seconds for horizontal fragmentation. The vertical fragmentation is found to be better than the horizontal one in DDBMS.

A Secure Multi-Party Computation Protocol for Graph Editing Distance against Malicious Attacks

The secure computation of the graph structure is an important element in the field of secure calculation of graphs, which is important in querying data in graphs, since there are no algorithms for the graph edit distance problem that can resist attacks by malicious adversaries. In this paper, for the problem of secure computation of similarity edit distance of graphs, firstly, the encoding method applicable to the Paillier encryption algorithm is proposed, and the XOR operation scheme is proposed according to the Paillier homomorphic encryption algorithm. Then, the security algorithm under the semi-honest model is designed, which adopts the new encoding method and the XOR operation scheme. Finally, for the malicious behaviors that may be implemented by malicious participants in the semi-honest algorithm, using the hash function, a algorithm for secure computation of graph editing distance under the malicious model is designed, and the security of the algorithm is proved, and the computational complexity and the communication complexity of the algorithm are analyzed, which is more efficient compared with the existing schemes, and has practical value. The algorithm designed in this paper fills the research gap in the existing literature on the problem of graph edit distance and contributes to solving the problem.

Digital marketing program design based on abnormal consumer behavior data classification and improved homomorphic encryption algorithm

This article endeavors to delve into the conceptualization of a digital marketing framework grounded in consumer data and homomorphic encryption. The methodology entails employing GridSearch to harmonize and store the leaf nodes acquired post-training of the CatBoost model. These leaf node data subsequently serve as inputs for the radial basis function (RBF) layer, facilitating the mapping of leaf nodes into the hidden layer space. This sequential process culminates in the classification of user online consumption data within the output layer. Furthermore, an enhancement is introduced to the conventional homomorphic encryption algorithm, bolstering privacy preservation throughout the processing of consumption data. This augmentation broadens the applicability of homomorphic encryption to encompass rational numbers. The integration of the Chinese Remainder Theorem is instrumental in the decryption of consumption-related information. Empirical findings unveil the exceptional generalization performance of the amalgamated model, exemplifying an AUC (area under the curve) value of 0.66, a classification accuracy of 98.56% for online consumption data, and an F1-score of 98.41. The enhanced homomorphic encryption algorithm boasts attributes of stability, security, and efficiency, thus fortifying our proposed solution in facilitating companies’ access to precise, real-time market insights. Consequently, this aids in the optimization of digital marketing strategies and enables pinpoint positioning within the target market.