The widespread adoption of cloud infrastructures has revolutionized data storage and access. However, it has also raised concerns regarding the privacy of sensitive data. To address these concerns, encryption techniques have been widely used. However...
Homomorphic encryption is an important means for cloud computing to ensure information security when outsourcing data. Among them, threshold fully homomorphic encryption (ThFHE) is a key enabler for homomorphic encryption and, from a wider perspectiv...
Machine-learning applications are becoming increasingly widespread. However, machine learning is highly dependent on high-quality, large-scale training data. Due to the limitations of data privacy and security, in order to accept more user data, user...
Homomorphic encryption enables computations to be performed directly on encrypted data, ensuring data confidentiality even in untrusted or distributed environments. Although this approach provides strong theoretical security, its practical adoption r...
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 encr...
Homomorphic encryption enables secure cloud computing over the complete data lifecycle. As so-called in-use encryption methodology, it allows using encrypted data for, e.g., data analysis—in contrast to classic encryption methods. In-use encryp...
Homomorphic Encryption (HE) allows secure and privacy-protected computation on encrypted data without the need to decrypt it. Since Shor’s algorithm rendered prime factorisation and discrete logarithm-based ciphers insecure with quantum computa...
With the increasing number of edge devices connecting to the cloud for storage and analysis, concerns about security and data privacy have become more prominent. Homomorphic encryption (HE) provides a promising solution by not only preserving data pr...
Secure aggregation of local learning model parameters is crucial for achieving privacy-preserving federated learning. This paper presents a novel and practical aggregation method that effectively combines the advantages of masking-based aggregation w...
Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without f...
As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology—which requires a large amount of a...
Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated...
In actual operation, there are security risks to the data of the network control system, mainly in the form of possible eavesdropping of signals in the transmission channel and parameters in the controller leading to data leakage. In this paper, we p...
We propose a new framework for compile-time ciphertext synthesis in fully homomorphic encryption (FHE) systems. Instead of invoking encryption algorithms at runtime, our method synthesizes ciphertexts from precomputed encrypted basis vectors using on...
Multi-key Homomorphic Encryption (MKHE) scheme can homomorphically evaluate ciphertexts encrypted by different keys, which can effectively protect the privacy information of data holders in the joint computing of cloud services. Since the first full...
This study proposes a blockchain-based system that utilizes fully homomorphic encryption to provide data security and statistical privacy when data are shared with third parties for analysis or research purposes. The proposed system not only provides...
Aiming at the problems of large ciphertext size and low efficiency in the current secure multi-party computation (SMC) protocol based on fully homomorphic encryption (FHE), the paper proves that the fully homomorphic encryption scheme that supports m...
In most homomorphic encryption schemes based on RLWE, native plaintexts are represented as polynomials in a ring
Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of atten...
Performing smart computations in a context of cloud computing and big data is highly appreciated today. It allows customers to fully benefit from cloud computing capacities (such as processing or storage) without losing confidentiality of sensitive d...
IoT systems consist of Hardware/Software systems (e.g., sensors) that are embedded in a physical world, networked and that interact with complex software platforms. The validation of such systems is a challenge and currently mostly done by prototypes...
Healthcare applications store private user data on cloud servers and perform computation operations that support several patient diagnoses. Growing cyber-attacks on hospital systems result in user data being held at ransom. Furthermore, mathematical...
Ensuring data privacy and security in sensitive domains such as healthcare remains a critical challenge. Homomorphic Encryption (HE) offers a promising approach by enabling computations directly on encrypted data, but the diversity of available schem...
Fully homomorphic encryption allows users to use the computing resources of cloud servers for the computation of encrypted data without worrying about data leakage. The fully homomorphic encryption approach has problems with excessive noise and the e...
Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning...
Homomorphic encryption is well known to researchers, yet its application in image processing is scarce. The diversity of image processing algorithms makes homomorphic encryption implementation challenging. Current research often uses the CKKS algorit...
The purpose of this study is to provide an efficient search function over a large amount of encrypted data, where the bit length of each item is several tens of bits. For this purpose, we have improved the existing hybrid homomorphic encryption by en...
In the context of the increasing integration of Internet of Things technologies and the growing importance of data lakes, the need for robust cybersecurity measures to protect privacy without compromising data utility becomes key. Aiming to address t...
With the rapid development of the Internet of Things (IoT) technology, the security problems it faces are increasingly prominent and have attracted much attention in industry and the academy. Traditional IoT architecture comes with security risks. Il...
The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. To overcome the existing privacy issues, we can apply fully homomorphic encryption (FHE) to encrypt...
Active learning is a technique for maximizing performance of machine learning with minimal labeling effort and letting the machine automatically and adaptively select the most informative data for labeling. Since the labels on records may contain sen...
The lattice-based multi-identity fully homomorphic encryption scheme combines the quantum security of lattice cryptography with the advantage of identity-based encryption. However, existing schemes face challenges such as large key sizes, inefficient...
This study proposes an privacy-protection method for blockchain transactions based on lightweight homomorphic encryption, aiming to ensure the security of transaction data and user privacy, and improve transaction efficiency. We have built a blockcha...
With the advent of cloud computing and the era of big data, there is an increasing focus on privacy computing. Consequently, homomorphic encryption, being a primary technique for achieving privacy computing, is held in high regard. Nevertheless, the...
In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of dat...
The homomorphic proxy re-encryption scheme combines the characteristics of a homomorphic encryption scheme and proxy re-encryption scheme. The proxy can not only convert a ciphertext of the delegator into a ciphertext of the delegatee, but also can h...
Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, stati...
Today, the rapid development of deep learning has spread across all walks of life, and it can be seen in various fields such as image classification, automatic driving, and medical imaging diagnosis. Convolution Neural Networks (CNNs) are also widely...
Aiming at the problem that the fully homomorphic encryption scheme based on single identity cannot satisfy the homomorphic operation of ciphertext under different identities, as well as the inefficiency of trapdoor function and the complexity of samp...
With the growing scale and complexity of smart grids, ensuring both effective anomaly detection and robust privacy protection has become increasingly critical. This paper proposes a ciphertext-based anomaly detection model built upon a collaborative...
Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities du...
Federated learning avoids centralizing data in a central server by distributing the model training process across devices, thus protecting privacy to some extent. However, existing research shows that model updates (e.g., gradients or weights) exchan...
Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated...
Multi-group homomorphic encryption (MGHE) is a pivotal advance in secure multi-party computation, integrating merits of multi-party homomorphic encryption (MPHE) and multi-key homomorphic encryption (MKHE) to eliminate MPHE’s fixed-party limita...
Multi-key homomorphic encryption is widely applied into outsourced computing and privacy-preserving applications in multi-user scenarios. However, the existence of Common Random Strings (CRSs) weakens the ability of users to independently generate pu...
Privacy is a crucial issue for outsourcing computation, which means that clients utilize cloud infrastructure to perform online prediction without disclosing sensitive information. Homomorphic encryption (HE) is one of the promising cryptographic too...
This paper investigates whether ChatGPT, a large language model, can assist in the implementation of lattice-based cryptography and fully homomorphic encryption algorithms, specifically the Ring Learning-with-Errors encryption scheme and the Brakersk...
The increasing ubiquity of big data and cloud-based computing has led to increased concerns regarding the privacy and security of user data. In response, fully homomorphic encryption (FHE) was developed to address this issue by enabling arbitrary com...
Currently, most quantum homomorphic encryption (QHE) schemes only allow a single evaluator (server) to accomplish computation tasks on encrypted data shared by the data owner (user). In addition, the quantum computing capability of the evaluator and...
In this paper, we describe the use of homomorphic encryption techniques in order to not only ensure the data are transmitted in a confidential way, but also to use the encrypted data to provide the manager with statistics that allow them to detect th...
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