Privacy-Preserving All Convolutional Net Based on Homomorphic Encryption

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.

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.

As huge data need to store on clouds for big data analytics and related features to analyze data computationally. Cloud computing serves many services to the client over their data such as data storage, analysis, and different resources. To avail such services over clouds, client needs to provide the access to cloud service providers (CSPs) over their data, which leaves data unprotected and confidentiality of their data is at stake. Different techniques are proposed to preserve the privacy and confidentiality of client’s data. One of the most common is, cryptography, which converts plaintext to unreadable form that is ciphertext. But almost all the existing cryptographic techniques are not up to mark and don’t have potential to perform the computation on the ciphertext. The recent trends in field of cryptography, the homomorphic encryption is emerged as a boom, which enhance the privacy of data to a large extent, without compromising the confidentiality of users’ data at cloud end. Homomorphic encryption provides the ability to perform computation on encrypted data, even when data is in use. Most of the existing homomorphic encryption algorithms are having high computational and time complexity. In this paper, an enhanced homomorphic encryption algorithm is proposed specifically for the cloud environments, having better performance. In this paper, an enhanced homomorphic encryption algorithm is proposed specifically for the cloud environments, may having better performance. The algorithm uses prime number-based matrix for encryption and decryption. The algorithm may give better performance in terms of encryption, decryption, and overall execution time.

We can say that we are surrounded by the computing world from every nook and corner. Cloud Computing is deemed as the most fundamentally changing phenomenon happening in Information Technology. But with great perks, comes great challenges as well, especially in the field of data security and privacy protection. The Cloud Service Providers take over chunks of private data from users for providing better functionalities. But for the sake of privacy and protection, this sensitive data should be encrypted and then outsourced to the cloud consoles. Since traditional cloud computing is performed on plaintext, many encryption algorithms were applied in the cloud for security purposes and ‘encrypted’ data was stored in the cloud from then on. This change gives fruition to a new type of encryption technique called Homomorphic Encryption. Primarily, the paper will focus on a subtype of Homomorphic Encryption called Fully Homomorphic Encryption. The objective of the paper is to propose a method to convert the sequentially processing Fully Homomorphic Encryption into parallel processing Fully Homomorphic Encryption using a Parallel Computing concept called Partitioning and thereby producing a better performing Fully Homomorphic Encryption.

Secure outsourcing of computation to an untrusted (cloud) service provider is becoming more and more important. Pure cryptographic solutions based on fully homomorphic and verifiable encryption, recently proposed, are promising but suffer from very high latency. Other proposals perform the whole computation on tamper-proof hardware and usually suffer from the the same problem. Trusted computing (TC) is another promising approach that uses trusted software and hardware components on computing platforms to provide useful mechanisms such as attestation allowing the data owner to verify the integrity of the cloud and its computation. However, on the one hand these solutions require trust in hardware (CPU, trusted computing modules) that are under the physical control of the cloud provider, and on the other hand they still have to face the challenge of run-time attestation.In this paper we focus on applications where the latency of the computation should be minimized, i.e., the time from submitting the query until receiving the outcome of the computation should be as small as possible. To achieve this we show how to combine a trusted hardware token (e.g., a cryptographic coprocessor or provided by the customer) with Secure Function Evaluation (SFE) to compute arbitrary functions on secret (encrypted) data where the computation leaks no information and is verifiable. The token is used in the setup phase only whereas in the time-critical online phase the cloud computes the encrypted function on encrypted data using symmetric encryption primitives only and without any interaction with other entities.

Homomorphic Encryption (HE) allows encrypted data to be processed without decryption, which could maximize the protection of user privacy without affecting the data utility. Thanks to strides made by cryptographers in the past few years, the efficiency of HE has been drastically improved, and machine learning on homomorphically encrypted data has become possible. Several works have explored machine learning based on HE, but most of them are restricted to the outsourced scenario, where all the data comes from a single data owner. We propose HomoPAI, an HE-based secure collaborative machine learning system, enabling a more promising scenario, where data from multiple data owners could be securely processed. Moreover, we integrate our system with the popular MPI framework to achieve parallel HE computations. Experiments show that our system can train a logistic regression model on millions of homomorphically encrypted data in less than two minutes.

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]

With the wide application of cloud computing in multi-tenant, heterogeneous nodes and high-concurrency environments, model parameters frequently interact during distributed training, which easily leads to privacy leakage, communication redundancy, and decreased aggregation efficiency. To realize the collaborative optimization of privacy protection and computing performance, this study proposes the Heterogeneous Federated Homomorphic Encryption Cloud (HFHE-Cloud) model, which integrates federated learning (FL) and homomorphic encryption and constructs a secure and efficient collaborative learning framework for cloud platforms. Under the condition of not exposing the original data, the model effectively reduces the performance bottleneck caused by encryption calculation and communication delay through hierarchical key mapping and dynamic scheduling mechanism of heterogeneous nodes. The experimental results show that HFHE-Cloud is significantly superior to Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Personalization (FedPer) and Federated Normalized Averaging (FedNova) in comprehensive performance, Homomorphically Encrypted Federated Averaging (HE-FedAvg) and other five baseline models. In the dimension of privacy protection, the global accuracy is up to 94.25%, and the Loss is stable within 0.09. In terms of computing performance, the encryption and decryption time is shortened by about one third, and the encryption overhead is controlled at 13%. In terms of distributed training efficiency, the number of communication rounds is reduced by about one fifth, and the node participation rate is stable at over 90%. The results verify the model's ability to achieve high security and high scalability in multi-tenant environment. This study aims to provide cloud service providers and enterprise data holders with a technical solution of high-intensity privacy protection and efficient collaborative training that can be deployed in real cloud platforms.

Cloud storage provides great benefits and advantages like reduction in cost, increased flexibility, elasticity and the efficient use of resources. With these great advantages, there are concerns and problem for cloud users. Some obstacles in using cloud computing services are because of its distributed and untransparent nature. That's why users have no control over their data and information and are not sure of whether or not to trust cloud providers. Therefore, the objective of this research is to examine the influence of customer and provider characteristics on cloud service outcome, impact of Trust on cloud service outcome, and to examine if Trust mediate the relationship of customer characteristics and provider’s characteristics with cloud service outcome. In order to achieve the objectives of this research, the analyses of measurement model, factor analysis, assessment of structure model and regression analysis has been conducted. The Findings of this study shows that the existing threats in cloud service is one of the reason why users are not trusting cloud service providers. These threats which are privacy and security cause unwillingness to use cloud services. This study also found that Trust plays an enormous role for cloud service users and providers because it increases usage, willingness to use among customers, and reputation of cloud service providers. It also found that Outcome is really low if there is no trust in these services. This study will help cloud service providers/developers to know the reasons why some people and organizations are not willing to use their services.

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.

The integration of the Industrial Internet of Things (IIoT) with sixth-generation (6G) networks offers revolutionary connectivity and ultra-low latency, although it also increases cybersecurity vulnerabilities due to resource-limited devices and dynamic network structures. This research presents a novel privacy-preserving intrusion detection system (IDS) for IIoT in 6G settings, using Graph Neural Networks (GNNs) and Homomorphic Encryption (HE). The proposed system utilizes GNNs to represent intricate inter-device interactions and temporal traffic patterns, facilitating the effective identification of advanced threats, such as Advanced Persistent Threats (APTs), Distributed Denial of Service (DDoS) attacks, and Mirai botnet attacks. HE enables secure distributed training and inference by executing computations on encrypted data, thereby obviating the necessity for centralized data aggregation and assuring adherence to rigorous privacy standards like as GDPR. Experimental results indicate outstanding performance, with detection accuracies over 98%, low false positive rates, and little computing cost, rendering it appropriate for resource-constrained IIoT devices. The GNN + HE model surpasses state-of-the-art approaches in accuracy, scalability, and privacy protection, effectively tackling the dynamic difficulties of 6G-IIoT ecosystems. This study lays the groundwork for secure, scalable, and privacy-compliant IIoT systems, with future efforts directed towards adversarial robustness, real-time implementation, and integration with federated learning to improve resilience in highly interconnected industrial settings.

The preservation of privacy has become a widely discussed topic on the Internet. Encryption is an approach to privacy; however, to outsource computing to an cloud service without revealing private information over encrypted data is difficult. Homomorphic encryption can contribute to it but is based on complicated mathematical structures of abstract algebra. We propoase a new scheme for securely computing the similarity between binary vectors through a cloud server. The scheme is constructed from ciphertext policy attribute based encryption and garbled circuits rather than homomorphic encryption. Attribute based encryption provides the access power, which is a necessary primitive in our scheme. Moreover, for computing over encrypted data, we rely on garbled circuits to handle secure outsourcing and to avoid the use of homomorphic encryption.

In the Database-As-a-Service (DAS) model, clients store their database contents at servers belonging to potentially untrusted service providers. To maintain data confidentiality, clients need to outsource their data to servers in encrypted form. At the same time, clients must still be able to execute queries over encrypted data. One prominent and fairly effective technique for executing SQL-style range queries over encrypted data involves partitioning (or bucketization) of encrypted attributes.However, executing aggregation-type queries over encrypted data is a notoriously difficult problem. One well-known cryptographic tool often utilized to support encrypted aggregation is homomorphic encryption; it enables arithmetic operations over encrypted data. One technique based on a specific homomorphic encryption function was recently proposed in the context of the DAS model. Unfortunately, as shown in this paper, this technique is insecure against ciphertext-only attacks. We propose a simple alternative for handling encrypted aggregation queries and describe its implementation. We also consider a different flavor of the DAS model which involves mixed databases, where some attributes are encrypted and some are left in the clear. We show how range queries can be executed in this model.

Data security and privacy is a major concern for the users while using software services on the cloud. When users want to compute on a cloud service, traditional encryption schemes can be applied to encrypt and transfer the data to the cloud service. However, the service provider must decrypt the data for input into their computational model and thus the data content is exposed. If users do not want service providers to know what they are computing, then computing on encrypted data preserving privacy is an important issue. Homomorphic encryption is an encryption method where computations can be performed on the ciphertext, and the decrypted result of these computations is the same as if the computations were performed on the plaintext. However, the performance of this approach is currently inefficient. This paper presents an application of homomorphic encryption method on an open financial cloud framework (FinancialCloud) to perform calculations on encrypted data, therefore securing the data throughout the whole process. We demonstrate by example, showing that by applying improved algorithms can lessen the deficiencies induced by homomorphic encryptions.

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.