Advanced Cryptographic Protocols Using Homomorphic Encryption (original) (raw)

The Next Frontier of Security: Homomorphic Encryption in Action

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2024

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.

The Effectiveness of Homomorphic Encryption in Protecting Data Privacy.

International Journal of Research Publication and Reviews, Vol 5, no 11, pp 3235-3256 , 2024

As the use of digital services grows, protecting the privacy and integrity of sensitive data, especially in fields like healthcare, finance, and secure surveying, has become a critical concern. Homomorphic encryption (HE) offers a solution by allowing computations to be performed on encrypted data without revealing the original information. This paper examines the principles of homomorphic encryption and its applications in privacy-preserving tasks, focusing on its use in cloud computing, healthcare, and cybersecurity. Various types of HE schemes, including Fully Homomorphic Encryption (FHE), Partially Homomorphic Encryption (PHE), and Somewhat Homomorphic Encryption (SHE), are reviewed to assess their performance, efficiency, and real-world use. The paper also discusses the challenges of implementing HE, such as computational overhead and key management. It suggests directions for future research to improve the scalability and usability of HE in real-time applications. Addressing these challenges will make homomorphic encryption an essential tool for secure, privacy-preserving data processing and sharing in modern digital systems

A Survey on Implementations of Homomorphic Encryption Schemes

With the increased need for data confidentiality in various applications of our daily life, homomorphic encryption (HE) has emerged as a promising cryptographic topic. HE enables to perform computations directly on encrypted data (ciphertexts) without decryption in advance. Since the results of calculations remain encrypted and can only be decrypted by the data owner, confidentiality is guaranteed and any third party can operate on ciphertexts without access to decrypted data (plaintexts). Applying a homomorphic cryptosystem in a real-world application depends on its resource efficiency. Several works compared different HE schemes and gave the stakes of this research field. However, the existing works either do not deal with recently proposed HE schemes (such as CKKS) or focus only on one type of HE. In this paper, we conduct an extensive comparison and evaluation of homomorphic cryptosystems’ performance based on their experimental results. The study covers all three families of HE...

State Of Art in Homomorphic Encryption Schemes

2014

The demand for privacy of digital data and of algorithms for handling more complex structures have increased exponentially over the last decade. However, the critical problem arises when there is a requirement for publicly computing with private data or to modify functions or algorithms in such a way that they are still executable while their privacy is ensured. This is where homomorphic cryptosystems can be used since these systems enable computations with encrypted data. A fully homomorphic encryption scheme enables computation of arbitrary functions on encrypted data.. This enables a customer to generate a program that can be executed by a third party, without revealing the underlying algorithm or the processed data. We will take the reader through a journey of these developments and provide a glimpse of the exciting research directions that lie ahead. In this paper, we propose a selection of the most important available solutions, discussing their properties and limitations.

Practical Privacy-Preserving Data Science With Homomorphic Encryption: An Overview

2020 IEEE International Conference on Big Data (Big Data)

Privacy has gained a growing interest nowadays due to the increasing and unmanageable amount of produced confidential data. Concerns about the possibility of sharing data with third parties, to gain fruitful insights, beset enterprise environments; value not only resides in data but also in the intellectual property of algorithms and models that offer analysis results. This impasse locks both the availability of highperformance computing resources in the "as-a-service" paradigm and the exchange of knowledge with the scientific community in a collaborative view. Privacy-preserving data science enables the use of private data and algorithms without putting at risk their privacy. Conventional encryption schemes are not able to work on encrypted data without decrypting them first. Homomorphic Encryption (HE) is a form of encryption that allows the computation of encrypted data while preserving the features and the format of the plaintext. Against the background of interesting use cases for the Central Bank of Italy, this article focuses on how HE and data science can be leveraged for the design and development of privacy-preserving enterprise applications. We propose a survey of main Homomorphic Encryption techniques and recent advances in the conubium between data science and HE.

A Review on A lgorithms of Homomorphic Encryption

2021

The rapid evolution of technologies like Internet of Things (IoT) and Cloud computing which handle big-scale data through the operations operate, store and manage. Besides, the technology facilitates with operations related to safety and confidentiality concerns. Moreover, the increasingly accessible cryptosystems offer the security concerns by IoT and cloud computing at several segments. In addition, the provision of third-party cloud process and analytics service contributors creates a privacy challenges to the consumer data. The current evolutions in the homomorphic encryption permits the computations on the data even when its encrypted. Therefore, a substantial research has been conducted on homomorphic encryption since few decades. However, the requirement of realtime implementation is essential for homomorphic encryption methods for achieving better improvements. Thus, the survey introduces the innumerable schemes for homomorphic encryption, advancements, and developments for ...

Homomorphic Encryption Algorithms and Schemes for Secure Computations in the Cloud

ICSCT 2016 - International Conference on Secure Computation and Technology, Virginia International University, Fairfax, VA , 2016

Although cloud computing continues to grow rapidly, shifting to Internet-based shared computing service has created new security challenge. Organizations move to the cloud technology looking for efficient and fast computing but data security remains their top concern. Confidential data are prone to leak because of modern trend to outsource computations to third-parties. Furthermore, the issue of data breaches can remove any benefits businesses make by moving to the cloud computing technology. Three important questions must be put into consideration: How to guarantee that the user's private data will always be kept safe and secure? Can the cloud service provider be reliable to store and process client's private data confidentially? Is it possible to ensure that even if the cloud provider have been attacked, client's confidential data will not be stolen or reused? To provide better data protection during the communication and storage process, many cryptographic algorithms have already been used, but these methods are practically inapplicable as they require that the data needs to be visible to the cloud provider, in order to do that, the private key has to be transmitted to the server to perform the operations required. In the past thirty years, privacy homomorphism has been used to solves this issue. Homomorphic encryption allows us to execute the arithmetical calculations directly on the ciphertext while keeping the secret key that is used to decrypt the result. In addition to preserve privacy, it provides the exact same result as if we perform the computations on the plaintext. So far, many fully homomorphic encryption (FHE) schemes which evaluate an arbitrary number of additions and multiplications are implemented but researches remains unable to design more secure and powerful schemes. In this paper, a detailed survey of homomorphic encryption using public key algorithms such as RSA, El-Gamal, and Paillier algorithms is given, then, FHE schemes are introduced as well. This work can be helpful as a guide of principles, properties of FHE as researchers believe in the possibility of advancement in the FHE area.

Application of homomorphic encryption in machine learning

Big data technologies, such as machine learning, have increased data utility exponentially. At the same time, the cloud has made the deployment of these technologies more accessible. However, computations of unencrypted sensitive data in a cloud environment may expose threats and cybersecurity attacks. We consider a class of innovative cryptographic techniques called privacy-preserving technologies (PPTs) to address this problem. That might help increase utility by taking more significant advantage of the cloud and machine learning technologies while preserving privacy. The first section provides a brief introduction to the so-called homomorphic encryption "HE" by giving an overview of the most promising schemes and then giving the current state of the art of HE tools such as libraries and compilers. This section aims to help non-cryptographer developers propose HE solutions by explaining what makes developing HE applications challenging. Then, we address the privacy-preserving in machine learning (PPML), an approach that allows to train and deploy ML models without exposing their private data. After exploring state of the art for the most used ML models in PPML, we will overview applications of homomorphic encryption in machine learning.

A survey on Fully Homomorphic Encryption

Cloud computing is an ever-growing field in today's era.With the accumulation of data and the advancement of technology,a large amount of data is generated everyday.Storage, availability and security of the data form major concerns in the field of cloud computing.This paper focuses on homomorphic encryption, which is largely used for security of data in the cloud.Homomorphic encryption is defined as the technique of encryption in which specific operations can be carried out on the encrypted data.The data is stored on a remote server.The task here is operating on the encrypted data.There are two types of homomorphic encryption, Fully homomorphic encryption and patially homomorphic encryption.Fully homomorphic encryption allow arbitrary computation on the ciphertext in a ring, while the partially homomorphic encryption is the one in which addition or multiplication operations can be carried out on the normal ciphertext.Homomorphic encryption plays a vital role in cloud computing as the encrypted data of companies is stored in a public cloud, thus taking advantage of the cloud provider's services.Various algorithms and methods of homomorphic encryption that have been proposed are discussed in this paper.

Processing Encrypted Data Using Homomorphic Encryption

2017

Fully Homomorphic Encryption (FHE) was initially introduced as a concept shortly after the development of the RSA cryptosystem, by Rivest et al. [54]. Although long sought after, the first functional scheme was only proposed over thirty years later by Gentry [34, 35] in 2009. The same blueprint to construct FHE has been followed in all subsequent work. First a scheme is constructed which can evaluate arithmetic circuits of a limited depth, a so-called Somewhat Homomorphic Encryption (SHE) scheme. If the complexity of the circuits which the SHE scheme can evaluate is slightly more than the complexity of the decryption circuit for the SHE scheme, then (by placing a SHE encryption of the scheme’s private key inside the public key) one can bootstrap the SHE scheme into a FHE scheme. This bootstrapping operation is obtained by homomorphically evaluating the decryption circuit on input of the ciphertext to be bootstrapped and the encryption of the secret key.