Application Of Homomorphic Encryption Research Articles

Development Paillier's library of fully homomorphic encryption

One of the new areas of cryptography considered-homomorphic cryptography. The article presents the main areas of application of homomorphic encryption. An analysis of existing developments in the field of homomorphic encryption carried out. The analysis showed that existing library implementations only allow processing bits or arrays of bits and do not support division and subtraction operations. However, to solve applied problems, support for performing integer operations are necessary. Because of the analysis, the need to implement the homomorphic division and subtraction operations identified, as well as the relevance of developing our own implementation of a homomorphic encryption library over integers. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the areas of application of homomorphic encryption. A homomorphic division and subtraction methods proposed that allows the division operation performed on homomorphically encrypted data. An architecture for a library of fully homomorphic operations on integers is proposed. The library supports basic homomorphic operations on integers, as well as homomorphic division method. The article also provides measurements of the time required to perform certain operations on encrypted data and analyzes the efficiency of the developed implementation of the library.

Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring

Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to the jointly trained model. Application of homomorphic encryption (HE) in a secure FL framework prevents the central server from accessing plaintext models. Thus, it is no longer feasible to embed the watermark at the central server using existing watermarking schemes. In this article, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE. To the best of our knowledge, it is the first scheme to embed the watermark to models under a secure FL environment. We design a black-box watermarking scheme based on client-side backdooring to embed a pre-designed trigger set into an FL model by a gradient-enhanced embedding method. Additionally, we propose a trigger set construction mechanism to ensure that the watermark cannot be forged. Experimental results demonstrate that our proposed scheme delivers outstanding protection performance and robustness against various watermark removal attacks and ambiguity attack.

High-Speed VLSI Architectures for Modular Polynomial Multiplication via Fast Filtering and Applications to Lattice-Based Cryptography

This paper presents a low-latency hardware accelerator for modular polynomial multiplication for lattice-based post-quantum cryptography and homomorphic encryption applications. The proposed novel modular polynomial multiplier exploits the fast finite impulse response (FIR) filter architecture to reduce the computational complexity of the schoolbook modular polynomial multiplication. We also extend this structure to fast $M$-parallel architectures while achieving low-latency, high-speed, and full hardware utilization. We comprehensively evaluate the performance of the proposed architectures under various polynomial settings as well as in the Saber scheme for post-quantum cryptography as a case study. The experimental results show that our proposed modular polynomial multiplier reduces the computation time and area-time product, respectively, compared to the state-of-the-art designs.

Homomorphic Encryption and its Application to Blockchain

The concept, method, algorithm and application of the advanced field of cryptography, homomorphic encryption, as well as its application to the field of blockchain are discussed in this paper. There are various forms of homomorphic encryption and they can be classified into three categories: partially homomorphic encryption, hierarchical homomorphic encryption and fully homomorphic encryption. We explain the concept, method and algorithm for each category with down to earth examples. The milestone example of RSA algorithm is explained which is still widely used in the internet and in blockchain as well. Applications of homomorphic encryption depends on efficiency of implementation which has been in good progress in recent years.

Federated Learning for Intrusion Detection in the Critical Infrastructures: Vertically Partitioned Data Use Case

One of the challenges in the Internet of Things systems is the security of the critical data, for example, data used for intrusion detection. The paper research construction of an intrusion detection system that ensures the confidentiality of critical data at a given level of intrusion detection accuracy. For this goal, federated learning is used to train an intrusion detection model. Federated learning is a computational model for distributed machine learning that allows different collaborating entities to train one global model without sharing data. This paper considers the case when entities have data that are different in attributes. Authors believe that it is a common situation for the critical systems constructed using Internet of Things (IoT) technology, when industrial objects are monitored by different sets of sensors. To evaluate the applicability of the federated learning for this case, the authors developed an approach and an architecture of the intrusion detection system for vertically partitioned data that consider the principles of federated learning and conducted the series of experiments. To model vertically partitioned data, the authors used the Secure Water Treatment (SWaT) data set that describes the functioning of the water treatment facility. The conducted experiments demonstrate that the accuracy of the intrusion detection model trained using federated learning is compared with the accuracy of the intrusion detection model trained using the centralized machine learning model. However, the computational efficiency of the learning and inference process is currently extremely low. It is explained by the application of homomorphic encryption for input data protection from different data owners or data sources. This defines the necessity to elaborate techniques for generating attributes that could model horizontally partitioned data even for the cases when the collaborating entities share datasets that differ in their attributes.

Research Review on the Application of Homomorphic Encryption in Database Privacy Protection

With the advent and development of database applications such as big data and data mining, how to ensure the availability of data without revealing sensitive information has been a significant problem for database privacy protection. As a critical technology to solve this problem, homomorphic encryption has become a hot research area in information security at home and abroad in recent years. The paper sorted out, analyzed, and summarized the research progress of homomorphic encryption technology in database privacy protection. Moreover, the application of three different types of homomorphic encryption technology in database privacy protection was introduced respectively, and the rationale and characteristics of each technique were analyzed and explained. Ultimately, this research summarized the challenges and development trends of homomorphic encryption technology in the application of database privacy protection, which provides a reference for future research.

Privacy Enhanced Cloud-Based Facial Recognition

Homomorphic encryption is a significant method to protect user privacy in cloud computing environment. Due to the computation efficiency issue, there is still not many homomorphic encryption applications for common users. In this paper,we try to use homomorphic encryption to enhance the privacy in cloud-based face recognition system. By balancing the workload between client and server,and reimplementing the similarity measurement function, our homomorphic encryption version’s performance is almost the same as the original version in terms of accuracy and time consumption. Our work is especially beneficial to many face recognition methods that are using Euclidian distance as their similarity metric.

Approaches to the application of homomorphic encryption in sensor networks

This paper describes the basic principles of the sensor network. The article characterizes the most common types of attacks on wireless sensor networks. The most commonly used protocols of sensor networks are presented. Special attention is paid to schemes of partially homomorphic encryption and fully homomorphic encryption schemes. An approach to the implementation of sensor network protection using the joint use of a fully homomorphic encryption algorithm and a symmetric block encryption algorithm is proposed.

МЕТОД РЕАЛИЗАЦИИ ГОМОМОРФНОГО ДЕЛЕНИЯ

The article deals with the problems of homomorphic cryptography. Homomorphic cryptographyis one of the young directions of cryptography. Its peculiarity lies in the fact that it is possibleto process encrypted data without preliminary decryption in such a way that the result of operationson encrypted data is equivalent, after decryption, to the result of operations on open data.The article provides a brief overview of the areas of application of homomorphic encryption. Tosolve various applied problems, support for all mathematical operations is required, including thedivision operation, and the ability to perform this operation homomorphically will expand thepossibilities of using homomorphic encryption. The paper proposes a method of homomorphicdivision based on an abstract representation of the ciphertext in the form of an ordinary fraction.The paper describes in detail the proposed method. In addition, the article contains an example ofthe practical implementation of the proposed method. It is proposed to divide the levels of dataprocessing into 2 levels – cryptographic and mathematical. At the cryptographic level, a completely homomorphic encryption algorithm is used and the basic homomorphic mathematical operationsare performed – addition, multiplication and difference. The mathematical level is a superstructureon top of the cryptographic level and expands its capabilities. At the mathematical level,the ciphertext is represented as a simple fraction and it becomes possible to perform thehomomorphic division operation. The paper also provides a practical example of applying thehomomorphic division method based on the Gentry algorithm for integers. Conclusions and possibleways of further development are given.

БИБЛИОТЕКА ПОЛНОСТЬЮГОМОМОРФНОГО ШИФРОВАНИЯ ЦЕЛЫХ ЧИСЕЛ

The article discusses one of the new directions of cryptography, a homomorphic cryptography.Its distinctive feature is that this type of cryptography allows you to process encrypted datawithout first decrypting it in such a way that the result of operations on encrypted data is equivalentafter decryption to the result of operations on open data. The paper describes the main areasof application of homomorphic encryption. The analysis of existing developments in the field ofhomomorphic encryption is performed. The analysis showed that existing library implementationsonly allow processing of bits or arrays of bits and do not support the division operation. However,to solve applied problems, support for performing integer operations is necessary. The analysisrevealed the need to implement the operation of homomorphic division, as well as the relevance ofdeveloping your own implementation of a library of homomorphic encryption over integers. Theability to perform four operations (addition, difference, multiplication and division) on encrypteddata will expand the field of application of homomorphic encryption. A method of homomorphicdivision is proposed, which allows performing the division operation on homomorphically encrypteddata. A library architecture of completely homomorphic operations on integers is proposed.The library supports the basic homomorphic operations on integers, as well as the divisionoperation, thanks to the method of homomorphic division. Based on the proposed method ofhomomorphic division and library architecture, a library of homomorphic operations on integerswas implemented. The article also provides measurements of the time required to perform certainoperations on encrypted data and analyzes the effectiveness of the developed library implementation.Conclusions and possible ways of further development are given.

Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption

Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data. Fully homomorphic encryption (FHE) allows data to be computed on whilst encrypted, which can provide a solution to the problem of data privacy. However, FHE is both slow and restrictive, so existing algorithms must be manipulated to make them work efficiently under the FHE paradigm. Some commonly used machine learning algorithms, such as Gaussian process regression, are poorly suited to FHE and cannot be manipulated to work both efficiently and accurately. In this paper, we show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data using a Gaussian process regression model built from another party's data, without either party gaining access to the other's data, in a way which is both accurate and efficient. This construction is, to our knowledge, the first example of an effectively encrypted Gaussian process.

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.

Application of Homomorphic Encryption on Neural Network in Prediction of Acute Lymphoid Leukemia

Machine learning is now becoming a widely used mechanism and applying it in certain sensitive fields like medical and financial data has only made things easier. Accurate Diagnosis of cancer is essential in treating it properly. Medical tests regarding cancer in recent times are quite expensive and not available in many parts of the world. CryptoNets, on the other hand, is an exhibit of the use of Neural-Networks over data encrypted with Homomorphic Encryption. This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions in case of Acute Lymphoid Leukemia (ALL). By using CryptoNets, the patients or doctors in need of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider (hospital or model owner). Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted all throughout the process and finally sending the prediction to the user who can decrypt the results. During the process the service provider (hospital or the model owner) gains no knowledge about the data that was used or the result since everything is encrypted throughout the process. Our work proposes a Neural Network model which will be able to predict ALL-Acute Lymphoid Leukemia with approximate 80% accuracy using the C_NMC Challenge dataset. Prior to building our own model, we used the dataset and pre-process it using a different approach. We then ran on different machine learning and Neural Network models like VGG16, SVM, AlexNet, ResNet50 and compared the validation accuracies of these models with our own model which lastly gives better accuracy than the rest of the models used. We then use our own pre-trained Neural Network to make predictions using CryptoNets. We were able to achieve an encrypted prediction of about 78% which is close to what we achieved when validating our own CNN model that has a validation accuracy of 80% for prediction of Acute Lymphoid Leukemia (ALL).

A systematic review on the status and progress of homomorphic encryption technologies

With the emergence of big data and the continued growth in cloud computing applications, serious security and privacy concerns emerged. Consequently, several researchers and cybersecurity experts have embarked on a quest to extend data encryption to big data systems and cloud computing applications. As most cloud users turn to using public cloud services, confidentiality becomes and even more complicated issue. Cloud clients storing their data on a public cloud always seek solutions to confidentiality problem. Homomorphic encryption emerged as a possible solution where client’s data is encrypted on the cloud in a way that allows some search and manipulation operations without proper decryption. In this paper, we present a systematic review of research paper published in the field of homomorphic encryption. This paper uses PRISMA checklist alongside some items of Cochrane’s Quality Assessment to review studies retrieved from various resources. It was highly noticeable in the reviewed papers that security in big data and cloud computing has received most attention. Most papers suggested the use of homomorphic encryption although the thematic analysis has identified other potential concerns. Regarding the quality of the articles, 38% of the articles failed to meet three checklist items, including explicit statement of research objectives, procedure recognition and sources of funding used in the study. The review also presented compendium textual analysis of different homomorphic encryption algorithms, application areas, and areas of future developments. Results of the evaluation through PRISMA and the Cochrane tool showed that a majority of research articles discussed the potential use and application of Homomorphic Encryption as a solution to the growing demands of big data and absence of security and privacy mechanisms therein. This was evident from 26 of the total 59 articles that met the inclusion criteria. The term Homomorphic Encryption appeared 1802 times in the word cloud derived from the selected articles, which speaks of its potential to ensure security and privacy, while also preserving the CIA triad in the context of big data and cloud computing.

Co-designed accelerator for homomorphic encryption applications

Fully Homomorphic Encryption (FHE) is considered as a key cryptographic tool in building a secure cloud computing environment since it allows computing arbitrary functions directly on encrypted data. However, existing FHE implementations remain impractical due to very high time and resource costs. These costs are essentially due to the computationally intensive modular polynomial multiplication. In this paper, we present a software/hardware co-designed modular polynomial multiplier in order to accelerate homomorphic schemes. The hardware part is implemented through a High-Level Synthesis (HLS) flow. Experimental results show competitive latencies when compared with hand-made designs, while maintaining large advantages on resources. Moreover, we show that our high-level description can be easily configured with different parameters and very large sizes in negligible time, generating new designs for numerous applications.

Stream Ciphers: A Practical Solution for Efficient Homomorphic-Ciphertext Compression

In typical applications of homomorphic encryption, the first step consists for Alice of en-crypting some plaintext m under Bob's public key pk and of sending the ciphertext c = HE pk (m) to some third-party evaluator Charlie. This paper specifically considers that first step, i.e. the problem of transmitting c as efficiently as possible from Alice to Charlie. As others suggested before, a form of compression is achieved using hybrid encryption. Given a symmetric encryption scheme E, Alice picks a random key k and sends a much smaller ciphertext c = (HE pk (k), E k (m)) that Charlie decompresses homomorphically into the original c using a decryption circuit C E −1. In this paper, we revisit that paradigm in light of its concrete implementation constraints; in particular E is chosen to be an additive IV-based stream cipher. We investigate the performances offered in this context by Trivium, which belongs to the eSTREAM portfolio, and we also propose a variant with 128-bit security: Kreyvium. We show that Trivium, whose security has been firmly established for over a decade, and the new variant Kreyvium have excellent performance. We also describe a second construction, based on exponentiation in binary fields, which is impractical but sets the lowest depth record to 8 for 128-bit security.

A Custom Accelerator for Homomorphic Encryption Applications

After the introduction of first fully homomorphic encryption scheme in 2009, numerous research work has been published aiming at making fully homomorphic encryption practical for daily use. The first fully functional scheme and a few others that have been introduced has been proven difficult to be utilized in practical applications, due to efficiency reasons. Here, we propose a custom hardware accelerator, which is optimized for a class of reconfigurable logic, for Lopez-Alt, Tromer and Vaikuntanathan's somewhat homomorphic encryption based schemes. Our design is working as a co-processor which enables the operating system to offload the most compute-heavy operations to this specialized hardware. The core of our design is an efficient hardware implementation of a polynomial multiplier as it is the most compute-heavy operation of our target scheme. The presented architecture can compute the product of very-large polynomials in under 6.25 ms which is 102 times faster than its software implementation. In case of accelerating homomorphic applications; we estimate the per block homomorphic AES as 442 ms which is 28.5 and 17 times faster than the CPU and GPU implementations, respectively. In evaluation of Prince block cipher homomorphically, we estimate the performance as 52 ms which is 66 times faster than the CPU implementation.