Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. The users or service ...providers with the key have exclusive rights on the data. Especially with popular cloud services, control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed,
Homomorphic Encryption
(HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable
Fully Homomorphic Encryption
(FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. Therefore, this survey focuses on HE and FHE schemes. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars for achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes, are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, and extending the state-of-the-art HE, PHE, SWHE, and FHE systems.
Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free ...sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies.
This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced, and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain.
Problem : We consider the problem of privacy-preserving distributed deep learning where data privacy is protected by fully homomorphic encryption. Aim : The aim is to develop a method for practical ...and scalable distributed deep learning with fully homomorphic encrypted data. The method must address the issue arising from the large computational cost associated with fully homomorphic encrypted data to offer a practical and scalable solution. Methods : An approach that leverages fuzzy-based membership-mappings for data representation learning is considered for distributed deep learning with fully homomorphic encrypted data. The method introduces globally convergent and robust variational membership-mappings to build local deep models. The local models are combined in a robust and flexible manner by means of fuzzy attributes to build a global model such that the global model can be homomorphically evaluated in an efficient manner. Results : The membership-mappings based privacy-preserving distributed deep learning method is accurate, practical, and scalable. This is verified through numerous experiments which include demonstrations using MNIST and Freiburg Groceries datasets, and a biomedical application related to the detection of mental stress on individuals. Conclusion : The study develops globally convergent and robust variational membership-mappings for their application to accurate, practical, and scalable privacy-preserving distributed deep learning.
In this article, we develop distributed iterative algorithms that enable the components of a multicomponent system, each with some integer initial value, to asymptotically compute the average of ...their initial values, without having to reveal to other components the specific value they contribute to the average calculation. We assume a communication topology captured by an arbitrary strongly connected digraph, in which certain nodes (components) might be curious but not malicious (i.e., they execute the distributed protocol correctly, but try to identify the initial values of other nodes). We first develop a variation of the so-called ratio consensus algorithm that operates exclusively on integer values and can be used by the nodes to asymptotically obtain the average of their initial (integer) values, by taking the ratio of two integer values they maintain and iteratively update. Assuming the presence of a trusted node (i.e., a node that is not curious and can be trusted to set up a cryptosystem and not reveal any decrypted values of messages it receives), we describe how this algorithm can be adjusted using homomorphic encryption to allow the nodes to obtain the average of their initial values while ensuring their privacy (i.e., without having to reveal their initial value). We also extend the algorithm to handle situations where multiple nodes set up cryptosystems and privacy is preserved as long as one of these nodes can be trusted (i.e., the ratio of trusted nodes over the nodes that set up cryptosystems decreases).
In this work, we elaborate on our endeavors to design, implement, fabricate, and post-silicon validate CoFHEE (Nabeel et al., 2023), a co-processor for low-level polynomial operations targeting fully ...homomorphic encryption execution. With a compact design area of <inline-formula> <tex-math notation="LaTeX">12 {\mathrm{ mm}}^{2} </tex-math></inline-formula>, CoFHEE features ASIC implementations of fundamental polynomial operations, including polynomial addition and subtraction, Hadamard product, and number theoretic transform, which underlie most higher-level FHE primitives. CoFHEE is capable of natively supporting polynomial degrees of up to <inline-formula> <tex-math notation="LaTeX">n = 2^{14} </tex-math></inline-formula> with a coefficient size of 128 bits, and has been fabricated and silicon-verified using 55-nm CMOS technology. To evaluate it, we conduct performance and power experiments on our chip, and compare it to state-of-the-art software implementations and other ASIC designs.
The study of homomorphic encryption techniques has led to significant advancements in the computing domain, particularly in the sphere of cloud computing. Homomorphic encryption provides a means for ...securely transmitting and storing confidential information across and in a computer system. The aim of this paper is to discuss the concepts and significance of homomorphic encryption along with the subdivisions and limitations associated with this type of encryption scheme. Recent studies conducted on the topic of homomorphic encryption are highlighted and some customary models of homomorphism are demonstrated. We also developed a proof of concept algorithm that demonstrates a practical use for a homomorphic encryption technique, the results of our algorithm are provided. The applications of homomorphic encryption methods are vast outside of the computational realm, and its purpose in other fields will be explored.
•This work presented a novel homomorphic encryption framework over non-abelian rings (matrix-ring). It is one-way secure based on the Conjugacy Search Problem.•The scheme supports real numbers ...encryption and achieves fast ciphertexts homomorphic comparison without decrypting any ciphetexts operations’s intermediate result.•We use the scheme to realize privacy preservation for machine learning training and classification in data ciphertexts environment. The analysis shows that our proposed schemes are efficient for encryption/decryption and homomorphic operations.
In recent years, more and more machine learning algorithms depend on the cloud computing. When a machine learning system is trained or classified in the cloud environment, the cloud server obtains data from the user side. Then, the privacy of the data depends on the service provider, it is easy to induce the malicious acquisition and utilization of data. On the other hand, the attackers can detect the statistical characteristics of machine learning data and infer the parameters of machine learning model through reverse attacks. Therefore, it is urgent to design an effective encryption scheme to protect the data’s privacy without breaking the performance of machine learning.
In this paper, we propose a novel homomorphic encryption framework over non-abelian rings, and define the homomorphism operations in ciphertexts space. The scheme can achieve one-way security based on the Conjugacy Search Problem. After that, a homomorphic encryption was proposed over a matrix-ring. It supports real numbers encryption based on the homomorphism of 2-order displacement matrix coding function and achieves fast ciphertexts homomorphic comparison without decrypting any ciphetexts operations’ intermediate result. Furthermore, we use the scheme to realize privacy preservation for machine learning training and classification in data ciphertexts environment. The analysis shows that our proposed schemes are efficient for encryption/decryption and homomorphic operations.
PDF
