HEFactory: A symbolic execution compiler for privacy-preserving Deep Learning with Homomorphic Encryption

ReActHE: A homomorphic encryption friendly deep neural network for privacy-preserving biomedical prediction

Deep learning models, particularly those used for im-age classification, have become pervasive in today's technological landscape. However, the increasing demand for privacy, espe-cially when sharing data with external parties, poses significant challenges. Homomorphic encryption (HE) offers a compelling solution by enabling computations on encrypted data without compromising privacy. Despite its potential, the widespread adoption of HE remains limited due to its computational complexity. In this work, we propose an optimized approach to improve the efficiency of a fundamental building block in image classification models: the convolution operation. Our approach is based on the observation that multiple kernel evaluations in a convolution layer can be performed in parallel, provided that their receptive fields do not overlap. Using this property, we reorganize convolution computations into independent groups, significantly reducing redundant operations. This grouping reduces the number of multiplications by up to 75% compared to standard encrypted convolution techniques. Additionally, the approach eliminates the need for costly preprocessing, enabling direct processing of encrypted inputs. To further accelerate homomorphic operations, we leverage a GPU-accelerated HE library, enabling faster op-erations. These contributions make encrypted convolution signifi-cantly more practical for real-world deep learning applications.

Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However, this opened the door to new challenges associated with the privacy and security of data. The RS data used to train the DL algorithms have several privacy requirements. Some of them need a high level of confidentiality, such as satellite images related to public security with high spatial resolutions. Moreover, satellite images are usually protected by copyright, and the owner may strictly refuse to share them. Therefore, privacy-preserving deep learning (PPDL) techniques are a possible solution to this problem. PPDL enables training DL on encrypted data without revealing the original plaintext. This study proposes a hybrid PPDL approach for object classification for very-high-resolution satellite images. The proposed encryption scheme combines Paillier homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). This combination aims to enhance the encryption of satellite images while ensuring a good runtime and high object classification accuracy. The method proposed to encrypt images is maintained through the public keys of PHE and SHE. Experiments were conducted on real-world high-resolution satellite images acquired using the SPOT6 and SPOT7 satellites. Four different CNN architectures were considered, namely ResNet50, InceptionV3, DenseNet169, and MobileNetV2. The results showed that the loss in classification accuracy after applying the proposed encryption algorithm ranges from 2% to 3.5%, with the best validation accuracy on the encrypted dataset reaching 92%.

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.

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

Secure computation, a methodology of computing on encrypted data, has become a key factor in machine learning. Homomorphic encryption (HE) enables computation on encrypted data without leaking any information to untrusted servers. In machine learning, the model selection method is a crucial algorithm that determines the performance and reduces the fitting problem. Despite the importance of finding the optimal model, none of the previous studies have considered model selection when performing data analysis through the HE scheme. The HE-based model selection we proposed finds the optimal complexity that best describes given data that is encrypted and whose distribution is unknown. Since this process requires a matrix calculation, we constructed the matrix multiplication and inverse of the matrix based on the bitwise operation. Based on these, we designed the model selection of the HE cross-validation approach and the HE Bayesian approach for homomorphic machine learning. Our focus was on evidence approximation for linear models to find goodness-of-fit that maximizes the evidence. We conducted an experiment on a dataset of age and Body Mass Index (BMI) from Kaggle to compare the capabilities and our model showed that encrypted data can regress homomorphically without decrypting it.

Homomorphic encryption (HE) is the ultimate tool for performing secure computations even in untrusted environments. Application of HE for deep learning (DL) inference is an active area of research, given the fact that DL models are often deployed in untrusted environments (e.g., third-party servers) yet inferring on private data. However, existing HE libraries [somewhat (SWHE), leveled (LHE) or fully homomorphic (FHE)] suffer from extensive computational and memory overhead. Few performance optimized high-speed homomorphic libraries are either suffering from certain approximation issues leading to decryption errors or proven to be insecure according to recent published attacks. In this article, we propose architectural tricks to achieve performance speedup for encrypted DL inference developed with exact HE schemes without any approximation or decryption error in homomorphic computations. The main idea is to apply quantization and suitable data packing in the form of bitslicing to reduce the costly noise handling operation, Bootstrapping while achieving a functionally correct and highly parallel DL pipeline with a moderate memory footprint. Experimental evaluation on the MNIST dataset shows a significant ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$37\times$ </tex-math></inline-formula> ) speedup over the nonbitsliced versions of the same architecture. Low memory bandwidths (700 MB) of our design pipelines further highlight their promise toward scaling over larger gamut of Edge-AI analytics use cases.

Homomorphic Encryption (HE), which allows encrypted data to be handled, is becoming an important tool for Russia. It helps to comply with strict data localization laws Federal Law No. 242-FZ,which regulates the transfer and storage of information abroad. As global cybersecurity threats evolve, the need for secure and efficient encryption mechanisms grows, especially in sectors such as finance, healthcare, and government services. This article explores Russia’s strategic adoption of Homomorphic Encryption (HE) as a cornerstone technology to achieve IT sovereignty amid geopolitical tensions and economic sanctions. It highlights the critical need to protect sensitive data processed on foreign cloud infrastructures, which currently dominate Russian enterprises, by enabling secure computations on encrypted data without exposing the underlying information. The paper provides a detailed comparative analysis of popular HE schemes (CKKS, BGV, TFHE) and their adaptation to Russian cryptographic standards such as GOST R34.12-2015, emphasizing the balance between computational overhead and sovereignty gains. A case study of Rostelecom’s pioneering use of HE for cross-border voice analytics demonstrates practical implementation, showcasing acceptable latency, high accuracy, and manageable energy consumption when processing encrypted voice data for national security purposes. The article also benchmarks domestic hardware, notably the Elbrus-16S processor, against global standards, underscoring the need for optimization to close performance gaps while benefiting from energy efficiency. Further, it outlines a policy roadmap focusing on education and infrastructure development, including university programs, industry partnerships, hardware enhancements, and integration with legacy systems, to foster a robust ecosystem for HE deployment. The discussion culminates in advocating for a hybrid HE and Post-Quantum Cryptography framework to future-proof Russia’s cryptographic infrastructure against emerging threats, supported by international cooperation within BRICS to reduce dependency on Western technologies. Overall, the article positions Homomorphic Encryption as a transformative technology essential for safeguarding data privacy, advancing national security, and reinforcing Russia’s digital independence in an increasingly complex global IT landscape.

Machine learning servers with mass storage and computing power is an ideal platform to store, manage, and analyze data and support decision-making. However, the main issue is providing security and privacy to the data, as the data is stored in a public way. Recently, homomorphic data encryption has been proposed as a solution due to its capabilities in performing computations over encrypted data. In this paper, we proposed an encrypted all convolutional net that transformed traditional all convolutional net into a net based on homomorphic encryption. This scheme allows different data holders to send their encrypted data to cloud service, complete predictions, and return them in encrypted form as the cloud service provider does not have a secret key. Therefore, the cloud service provider and others cannot get unencrypted raw data. When applied to the MNIST database, privacy-preserving all convolutional based on homomorphic encryption predict efficiently, accurately and with privacy protection.

Any privacy leakage of biometric data poses severe security risks given their sensitive nature. Biometric templates should thus be protected, storing irreversibly transformed or encrypted biometric signals, while preserving the unprotected system's performance. Based on the recent developments by Zhu et al . on privacy preserving similarity evaluation of time series data, we present a new biometric template protection scheme based on homomorphic probabilistic encryption, where only encrypted data is stored or exchanged. We then apply the proposed scheme to signature verification and show that all requirements described in the ISO/IEC 24745 standard are met with no performance degradation, using a publicly available database and a free implementation of the Paillier cryptosystem. Moreover, the proposed approach is robust to hill-climbing attacks.

Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data. We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models. Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation. Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.

Statistical confidentiality focuses on protecting data to preserve its analytical value while preventing identity exposure, ensuring privacy and security in any system handling sensitive information. Homomorphic encryption allows computations on encrypted data without revealing it to anyone other than an owner or an authorized collector. When combined with other techniques, homomorphic encryption offers an ideal solution for ensuring statistical confidentiality. TFHE (Fast Fully Homomorphic Encryption over the Torus) is a fully homomorphic encryption scheme that supports efficient homomorphic operations on Booleans and integers. Building on TFHE, Zama’s Concrete project offers an open-source compiler that translates high-level Python code (version 3.9 or higher) into secure homomorphic computations. This study examines the feasibility of the Concrete compiler to perform core statistical analyses on encrypted data. We implement traditional algorithms for core statistical measures including the mean, variance, and five-point summary on encrypted datasets. Additionally, we develop a bitonic sort implementation to support the five-point summary. All implementations are executed within the Concrete framework, leveraging its built-in optimizations. Their performance is systematically evaluated by measuring circuit complexity, programmable bootstrapping count (PBS), compilation time, and execution time. We compare these results to findings from previous studies wherever possible. The results show that the complexity of sorting and statistical computations on encrypted data with the Concrete implementation of TFHE increases rapidly, and the size and range of data that can be accommodated is small for most applications. Nevertheless, this work reinforces the theoretical promise of Fully Homomorphic Encryption (FHE) for statistical analysis and highlights a clear path forward: the development of optimized, FHE-compatible algorithms.

Although the emergence of massive data facilitates the applications of machine learning (ML) in precise biomedical-related prediction, there are growing concerns about the privacy and security of not only data but also ML models. It is still of urgent need to develop strategies to preserve privacy and protect the information security of data and models. Homomorphic encryption (HE) is one strategy that protects privacy of data and ML models by enabling direct computation over encrypted data, yet the computation burden of HE hinders its applications and the pre-processing involved in previous HE-based tasks poses the model to be vulnerable to the risk of membership inference attack. In this regard, we propose an integrated algorithm named Membership Inference Perturbation (MIP) - Sparse Homomorphic Encryption (SparseHE) method for privacy-preserving phenotype prediction. We design a novel sparse structure to reduce the computation redundancy of HE interface while being able to defend against membership inference attack via membership inference perturbation. We reduce the computation consumption by deploying a novel sparse homomorphic encryption interface on MIP structure. The theoretical results show that our SparseHE enables privacy-preserving healthcare prediction by tightening the generalization gap of model. Additionally, the proposed SparseHE accelerates the HE interface by reducing the computation redundancy. Our experimental results on four real healthcare datasets demonstrate that SparseHE can improve the ability of model to defend against MIA while reducing computation cost of HE prediction.

Cloud based private data analytic using secure computation over encrypted data

An encryption scheme is homomorphic if it supports operations on encrypted data. Homomorphic encryption allows a device to perform arbitrary computations on encrypted data without user secret key. Recently it is introduced new homomorphic encryption schemes with improved performance that can be implemented in IoT device in production environments. The IoT concept encompasses devices, sensors, and services existing within an interconnected infrastructure with an efficient access to sample computational facilities. In this paper we evaluated features of exact arithmetic homomorphic encryption mechanisms: BFV and BGV and approximate homomorphic encryption scheme: CKKS. In the paper we measured performances of operations of homomorphic encryption schemes: BGV, BFV and CKKS that are implemented in Raspberry Pi 4 IoT device.