Multi-Key Homomorphic Encryption Scheme with Multi-Output Programmable Bootstrapping

In cryptography, there has been tremendous success in building various two-party protocols with small communication complexity out of homomorphic semantically-secure encryption schemes, using their homomorphic properties in a black-box way. A few notable examples of such primitives include items like single database Private Information Retrieval (PIR) schemes (introduced in [15]) and private database update with small communication (introduced in [5]). In this paper, we illustrate a general methodology for determining what types of protocols can and cannot be implemented with small communication by using homomorphic encryption in a black-box way. We hope that this work will provide a simple litmus test of feasibility for black-box use of known homomorphic encryption schemes by other cryptographic researchers attempting to develop new protocols with low communication. Additionally, a precise mathematical language for reasoning about such problems is developed in this work, which may be of independent interest. We stress that the class of algebraic structures for which we prove communication complexity lower bounds is large, and covers practically all known semantically-secure homomorphic cryptosystems (including those based upon bilinear maps). Finally, we show the following equivalence which relates group homomorphic encryption and a major open question of designing a so-called fully-homomorphic cryptosystem: a fully homomorphic encryption scheme (over a non-zero ring) exists if and only if there exists homomorphic encryption over any finite non-abelian simple group. This result somewhat generalizes results of Barrington [1] (to any group containing a finite non-abelian simple subgroup) and of Maurer and Rhodes [18], and in fact gives a constructiveproof of the 1974 result Werner [28]. (This also answers an open question posed by Rappe in [23], who in 2004 proved a special case of this result.)

The ability to perform computations on ciphertexts without knowing any information about the plaintexts makes homomorphic encryption technique useful in a wide variety of confidentiality preserving protocols (e.g., e-voting, e-health, etc.). Unfortunately, the traditional encyption schemes support a limited number of homomorphic operations (usually addition or multiplication). Indeed, in 2009, Gentry proposed a fully homomorphic encryption scheme which supports both multiplicative and additive homomorphic operations. Since then, several fully homomorphic encryption schemes have been proposed. However, the fully homomorphic encryption schemes are still undergoing experimentation and improvement. The hybridization of homomorphic encryption schemes seems to be an effective way to overcome their limitations and to benefit from their resistance against the confidentiality attacks. In this paper, we will study the possibility to hybridize the homomorphic encryption schemes so as to support all homomorphic properties.

Homomorphic encryption (HE) schemes enable computing functions on encrypted data, by means of a public Eval procedure that can be applied to ciphertexts. But the evaluated ciphertexts so generated may differ from freshly encrypted ones. This brings up the question of whether one can keep computing on evaluated ciphertexts. An i-hop homomorphic encryption scheme is one where Eval can be called on its own output up to i times, while still being able to decrypt the result. A multi-hop homomorphic encryption is a scheme which is i-hop for all i. In this work we study i-hop and multi-hop schemes in conjunction with the properties of function-privacy (i.e., Eval's output hides the function) and compactness (i.e., the output of Eval is short). We provide formal definitions and describe several constructions. First, we observe that bootstrapping techniques can be used to convert any (1-hop) homomorphic encryption scheme into an i-hop scheme for any i, and the result inherits the function-privacy and/or compactness of the underlying scheme. However, if the underlying scheme is not compact (such as schemes derived from Yao circuits) then the complexity of the resulting i-hop scheme can be as high as nO(i). We then describe a specific DDH-based multi-hop homomorphic encryption scheme that does not suffer from this exponential blowup. Although not compact, this scheme has complexity linear in the size of the composed function, independently of the number of hops. The main technical ingredient in this solution is a re-randomizable variant of the Yao circuits. Namely, given a garbled circuit, anyone can re-garble it in such a way that even the party that generated the original garbled circuit cannot recognize it. This construction may be of independent interest.

With the wide adoption of cloud computing paradigm, it is important to develop appropriate techniques to protect client data privacy in the cloud. Encryption is one of the major techniques that could be used to achieve this goal. However, data encryption at the rest alone is insufficient for secure cloud computation environments. There is also the need for efficient techniques to carry out computation over encrypted data. Fully homomorphic encryption (FHE) and garbled circuits are naturally used to process encrypted data without leaking any information about the data. However, existing FHE schemes are inefficient for processing large amount of data in cloud and garbled circuits are one time programs and cannot be reused. Based on quaternion/octonion algebra and Jordan algebra over finite rings \(\mathbb {Z}_q\), this paper designs efficient fully homomorphic symmetric key encryption (FHE) schemes without bootstrapping (that is, noise-free FHE schemes) that are secure in the weak ciphertext-only security model assuming the hardness of solving multivariate quadratic equation systems and solving univariate high degree polynomial equation systems in \(\mathbb {Z}_q\). The FHE scheme designed in this paper is sufficient for privacy preserving computation in cloud.

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.

Public key encryption schemes developed based on learning with error problem became popular for homomorphic encryption, and are proved as secured schemes based on the worst-case hardness of short vector problems. Homomorphic encryption allows computations over the cipher text without decryption. Implementation of the scheme is not considered to be practical because of its larger public keys and larger cipher texts. Large public key and cipher texts require huge space in the cloud storages. However, there are some approaches proposed to shorten the cipher texts and public keys of LWE-based homomorphic encryption scheme. The objective of the paper is to introduce an idea to shorten the public keys and cipher texts in the storage. Also, support homomorphic addition operation on reduced cipher texts. The aim of the paper is not only to give the practical implementation of standard LWE-based homomorphic encryption operation (Addition) on the reduced cipher texts and also the performance of the proposed scheme.

In homomorphic encryption schemes, anyone can perform homomorphic operations, and therefore, it is difficult to manage when, where and by whom they are performed. In addition, the property that anyone can ‘‘freely” perform the operation inevitably means that ciphertexts are malleable, and it is well-known that adaptive chosen ciphertext (CCA) security and the homomorphic property can never be achieved simultaneously. In this paper, we show that CCA security and the homomorphic property can be simultaneously handled in situations that the user(s) who can perform homomorphic operations on encrypted data should be controlled/limited, and propose a new concept of homomorphic public-key encryption, which we call keyed-homomorphic public-key encryption (KH-PKE). By introducing a secret key for homomorphic operations, we can control who is allowed to perform the homomorphic operation. To construct KH-PKE schemes, we introduce a new concept, a homomorphic transitional universal hash family, and present a number of KH-PKE schemes through hash proof systems. We also present a practical construction of KH-PKE from the DDH assumption. For ℓ-bit security, our DDH-based scheme yields only ℓ-bit longer ciphertext size than that of the Cramer-Shoup PKE scheme.Keywordshomomorphic public key encryptionCCA2 securityhash proof system

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.

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

Quantum computing was in its infancy while cloud adoption increased but secure data processing methods in distributed environments became more important. As cloud-based operations continue to expand, allowing computation to be performed directly on encrypted data without the need for exposing private keys will be an important use case of homomorphic encryption. Fully homomorphic encryption (FHE) encompasses both addition and multiplication, whereas partial homomorphic encryption (PHE) is limited to either type of operation, which can provide practical efficiency benefits for certain applications. In this research, LightPHE, a Python-based PHE framework is implemented along with the implementation performance and environmental sustainability evaluation. The evaluation scope extends beyond the typical fair assessment of cryptography to include energy consumption profile and carbon emissions. The framework combines proven PHE algorithms and stays true to modular design principles as the foundation for secure application development. Experimental evaluations were performed on several cloud platforms such as Google Colab (Normal, A100 GPU, L4 GPU, T4 High RAM, TPU2) and Microsoft Azure Spark. The performance evaluation has included key generations, encryption, decryption and homomorphic operation, also the energy consumption was based on computational resource utilization. The environmental impact was evaluated via thorough analysis of Scope 1-3 emissions, as well as standardized data center efficiency metrics based on regional carbon intensity data. The study’s results showed unique trends in the trade-offs between computational performance and energy efficiency. Rather than calculating emissions for every algorithm variant, the analysis focuses on low and mid impact cases such as 80-bit and 128-bit resource-intensive homomorphic operations—where environmental considerations are most critical with Colab L4 GPU. Since the comparison with all cpu and gpu types including all data centers that will lead to another research. The results suggested that in both high-performance configurations and distributed environments, different optimization characteristics emerged in the energy-energy view where a lower total energy consumption is observed even at the expense of higher instantaneous demand. LightPHE offered uniform security across configurations, but differing environmental impact. This work empirically demonstrates the environmental considerations inherent to cryptographic implementations and lays the groundwork for future work in quantifiably assessing both security and sustainability in cloud-based encryption systems. The findings of this research provide practical recommendations for organizations looking to deploy secure, efficient, and sustainable data processing systems in modern cloud settings.

Homomorphic cryptography has been one of the most interesting topics of mathematics and computer security since Gentry presented the first construction of a fully homomorphic encryption (FHE) scheme in 2009. Since then, a number of different schemes have been found, that follow the approach of bootstrapping a fully homomorphic scheme from a somewhat homomorphic foundation. All existing implementations of these systems clearly proved, that fully homomorphic encryption is not yet practical, due to significant performance limitations. However, there are many applications in the area of secure methods for cloud computing, distributed computing and delegation of computation in general, that can be implemented with homomorphic encryption schemes of limited depth. We discuss a simple algebraically homomorphic scheme over the integers that is based on the factorization of an approximate semiprime integer. We analyze the properties of the scheme and provide a couple of known protocols that can be implemented with it. We also provide a detailed discussion on searching with encrypted search terms and present implementations and performance figures for the solutions discussed in this paper.

The application of information technologies such as big data, artificial intelligence, and cloud computing in the Internet environment makes protecting personal privacy and preventing data leakage an important issue. Homomorphic encryption (HE) supports the calculation and processing on ciphertexts and provides a technical guarantee for the security of personal information. Multi-key homomorphic encryption (MKHE) can perform cooperative calculation on ciphertexts under different keys, and is one of the solutions for secure computing in multi-user scenario. But the homomorphic multiplication after ciphertext extension makes the ciphertext dimension increase quadratically with respect to the number of users, so relinearization with noise is needed to reduce the dimension. In this paper, we propose an efficient multi-hop MKHE scheme with less relinearization noise under the common reference string (CRS) model, which supports real-time joining of new users. First, we propose an auxiliary coding scheme by introducing a random matrix instead of the encryption with noise. On this basis, we further propose how to construct the relinearization key, which can be pre-computed before the ciphertext input. In our MKHE scheme, the preprocessing of the ciphertext is cancelled to reduce the dimension of the ciphertext and the number of non-linear keys after homomorphic multiplication. In addition, we prove the semantic security of the scheme under the RLWE assumption, noise analysis and storage analysis also show that our scheme has smaller noise growth and storage space.KeywordsMulti-key homomorphic encryptionRelinearization keyCRS modelCiphertext extensionLess noise

Somewhat Homomorphic Encryption (SHE) schemes allow to carry out operations on data in the cipher domain. In a cloud computing scenario, personal information can be processed secretly, inferring a high level of confidentiality. Most practical Somewhat Homomorphic Encryption (SHE) schemes require the implementation of fast polynomial arithmetic, that is why hardware accelerators usually target the FFT/NTT algorithm. This paper proposes a co-design hardware/software approach to accelerate SHE using Karatsuba algorithm. Depending on the needs, Karatsuba algorithm allows to implement additional computations to the hardware in order to reduce software computation time. Our accelerator is designed to speed up arithmetic on degree 2560 polynomials with 125 bits coefficients. We provide 3 different approaches: An area efficient design, a balanced design, and a performance-oriented design. Our accelerator performs a polynomial multiplication in respectively 2.46 ms, 1.70 ms and 1.24 ms, and a relinearization operation in 2.28 ms, 1.53 ms and 1.1 ms, while a functionally equivalent design using the FFT [1] performs the multiplication in 1.96 ms and the relinearization in 4.79 ms for hardware resources consumption equivalent to the balanced design.

With the rapid development of Internet of Things, the demand of data sharing is not only to ensure the data integrity, but also to protect user's individual privacy. Secure multi-party computation, as a technology paradigm based on cryptography technology, enables privacy protection in data sharing among multiple parties, while the implement of secure multi-party computation is limited due to the inefficient, complex computation protocol and frequent interaction. Fortunately, the emergence of blockchain technology provides excellent properties of decentralization, verifiability and high reliability for secure multi-party computation. In this paper, we propose a blockchain-based secure multi-party computation architecture for data sharing, where we design an aggregator consortium for data storage, verification and joint computation. Moreover, we introduce a straightforward secure multi-party computation scheme based on homomorphic encryption. In addition, we analyze and discuss this scheme in terms of data security, access flexibility, attack resistance and potential applications.

The objective of this research is to develop a novel image encryption method that can be used to considerably increase the security of encrypted images. To solve this image security problem, we propose a distributed homomorphic image encryption scheme where the images of interest are those in the visible electromagnetic spectrum. In our encryption phase, a red green blue (RGB) image is first separated into its constituent channel images, and then the numerical intensity value of a pixel from each channel is written as a sum of smaller pixel intensity sub-values, leading to having several component images for each of the R, G, and B-channel images. A homomorphic encryption function is used to separately encrypted each of the pixel intensity sub-values in each component image using an encryption key, leading to a distributed image encryption approach. Each of the encrypted component images can be compressed before transmission and/or storage. In our decryption phase, each encrypted component image is decompressed if necessary, and then the homomorphic property of the encryption function is used to transform the product of individually encrypted pixel intensity sub-values in each encrypted component images, to the encryption of their sum, before applying the corresponding decryption function with a decryption key to recover the original pixel's intensity values for each channel image, and then recovering the original RGB image. Furthermore, a special case of an RGB image encryption and decryption where a pixel's intensity value from each channel is written as a sum of only two sub-values is implemented and simulated with a software. The resulting cipher-images are subject to a range of security tests and analyses. Results from these tests shown that our proposed homomorphic image encryption scheme is robust and can resist security attacks, as well as increases the security of the associated encrypted images. Our proposed homomorphic image encryption scheme has produced highly secure encrypted images.