Pyfhel

Ibarrondo, Alberto; Viand, Alexander

doi:10.1145/3474366.3486923

Cited by 34 publications

(13 citation statements)

References 11 publications

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“…Its clean and easy-to-understand syntax makes it a very beginner-friendly language for beginners just entering the world of programming. Python also provides many ready-to-use libraries and frameworks, such as NumPy, Pandas, and Django, which allow developers to speed up the application development process by using existing functions and features (Ibarrondo and Viand, 2021;Arifin and Muktyas, 2018). Apart from that, Python also has a strong and active ecosystem.…”

Section: Methodsmentioning

confidence: 99%

Application of Unimodular Hill Cipher and RSA Methods to Text Encryption Algorithms Using Python

Arifin,

Wijonarko,

Suwarno

et al. 2024

Journal of Computer Science

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Text encryption is one of the techniques used to maintain the confidentiality of information in digital communications. In this study, we propose to apply a combination of the Unimodular Hill Cipher and RSA methods to a text encryption algorithm using the Python programming language. The Unimodular Hill Cipher method uses an unimodular matrix to replace text characters with encrypted characters, while RSA (Rivest-Shamir-Adleman) is a public key encryption algorithm that relies on modulo arithmetic properties. The purpose of this research is to combine the strengths of the two methods and produce a more secure text encryption system. Unimodular Hill Cipher provides the advantage of randomizing text characters by using matrix modulo operations, while RSA provides a high level of security through the use of public and private key pairs. In this study, we explain in detail the basic theory and algorithms of the Unimodular Hill Cipher and RSA. We also describe the implementation steps of both methods in the Python programming language. The text data used in this study went through a preprocessing stage before being encrypted. We also analyze the results of the encryption using several statistical methods to measure how close the relationship between the original text and the result of the encryption is. In a comparative analysis with the previous paper, in this study, the use of the Unimodular Hill Cipher and RSA methods in the context of Python provides additional insight into the performance and level of security of both. The experimental results show that the combination of the Unimodular Hill Cipher and RSA methods can produce a higher level of security in text encryption. It is hoped that this research can contribute to the development of a more effective and secure text encryption algorithm.

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Section: Methodsmentioning

confidence: 99%

Application of Unimodular Hill Cipher and RSA Methods to Text Encryption Algorithms Using Python

Arifin,

Wijonarko,

Suwarno

et al. 2024

Journal of Computer Science

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“…Pyfhel HE, an open-source encryption library introduced by Ibarrondo et al, presents a Python implementation; although, FHE schemes are predominantly written in C++. It serves as a valuable tool for learning FHE, based on a Python wrapper for the C++ Microsoft SEAL Backend, implementing the BFV algorithm for integers and CKKS for operations using real numbers [19,20]. Microsoft SEAL, an open-source homomorphic encryption library, facilitates mathematical computations on encrypted data [21].…”

Section: Homomorphic Encryption (He)mentioning

confidence: 99%

“…Microsoft SEAL, an open-source homomorphic encryption library, facilitates mathematical computations on encrypted data [21]. Both Pyfhel and Microsoft SEAL enable unlimited addition and multiplication operations on encrypted data, controlling and observing noise to ensure accurate results upon decryption [22].…”

Section: Homomorphic Encryption (He)mentioning

confidence: 99%

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

Kishiyama,

Alsmadi

2024

IJSTS

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Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested.

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“…An overview of our protocol's procedural flow is depicted in Library versions. We implemented our proof of concept version of the protocol in python version 3.10.12 using Pyfhel version 3.4.1 [22] as the FHE library, sympy version 1.12 [25] for CRT and numpy version 1.24.3 [19] for matrix storage and operations.…”

Section: System Description and Empirical Evaluationmentioning

confidence: 99%

Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

Goldenberg,

Mualem,

Shahar

et al. 2024

Preprint

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DNA methylation data plays a crucial role in estimating chronological age in mammals, offering real-time insights into an individual’s aging process. The Epigenetic Pacemaker (EPM) model allows inference of the biological age as deviations from the population trend. Given the sensitivity of this data, it is essential to safeguard both inputs and outputs of the EPM model. In a recent study by Goldenberg et al., a privacy-preserving approach for EPM computation was introduced, utilizing Fully Homomorphic Encryption (FHE). However, their method had limitations, including having high communication complexity and being impractical for large datasets Our work presents a new privacy preserving protocol for EPM computation, analytically improving both privacy and complexity. Notably, we employ a single server for the secure computation phase while ensuring privacy even in the event of server corruption (compared to requiring two non-colluding servers in Goldenberg et al.). Using techniques from symbolic algebra and number theory, the new protocol eliminates the need for communication during secure computation, significantly improves asymptotic runtime and and offers better compatibility to parallel computing for further time complexity reduction. We have implemented our protocol, demonstrating its ability to produce results similar to the standard (insecure) EPM model with substantial performance improvement compared to Goldenberg et al. These findings hold promise for enhancing data security in medical applications where personal privacy is paramount. The generality of both the new approach and the EPM, suggests that this protocol may be useful to other uses employing similar expectation maximization techniques.

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Pyfhel

Cited by 34 publications

References 11 publications

Application of Unimodular Hill Cipher and RSA Methods to Text Encryption Algorithms Using Python

Application of Unimodular Hill Cipher and RSA Methods to Text Encryption Algorithms Using Python

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

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