Multi-Key Homomophic Encryption from TFHE

In this paper, we propose a Multi-Key Homomorphic Encryption (MKHE) scheme by generalizing the low-latency homomorphic encryption by Chillotti et al. (ASIACRYPT 2016). Our scheme can evaluate a binary gate on ciphertexts encrypted under different keys followed by a bootstrapping. The biggest challenge to meeting the goal is to design a multiplication between a bootstrapping key of a single party and a multi-key RLWE ciphertext. We propose two different algorithms for this hybrid product. Our first method improves the ciphertext extension by Mukherjee and Wichs (EUROCRYPT 2016) to provide better performance. The other one is a whole new approach which has advantages in storage, complexity, and noise growth. Compared to previous work, our construction is more efficient in terms of both asymptotic and concrete complexity. The length of ciphertexts and the computational costs of a binary gate grow linearly and quadratically on the number of parties, respectively. We provide experimental results demonstrating the running time of a homomorphic NAND gate with bootstrapping. To the best of our knowledge, this is the first attempt in the literature to implement an MKHE scheme

TFHE-rs: A library for safe and secure remote computing using fully homomorphic encryption and trusted execution environments

Fully Homomorphic Encryption (FHE) and Trusted Execution Environ-ments (TEEs) are complementing approaches that can both secure computa-tions running remotely on a public cloud. Existing FHE schemes are, however, malleable by design and lack integrity protection, making them susceptible to integrity breaches where an adversary could modify the data and corrupt the output. This paper describes how both confidentiality and integrity of remote compu-tations can be assured by combining FHE with hardware based secure enclave technologies. We provide a software library for performing FHE within the Intel SGX TEE, written in the memory-safe programming language Rust to strengthen the internal safety of software and reduce its attack surface. We evaluate a sample application written with our library. We demonstrate that we can feasibly combine these concepts and provide stronger security guar-antees with a minimal development effort

On the Security of Multikey Homomorphic Encryption

Multikey fully homomorphic encryption (MFHE) scheme enables homomorphic computation on data encrypted under different keys. To decrypt a result ciphertext, all the involved secret keys are required. For multi decryptor setting, decryption is a protocol with minimal interaction among parties. However, all prior schemes supporting the protocol are not secure in public channel against a passive external adversary who can see any public information not joining the protocol. Furthermore, the possible adversaries have not been defined clearly. In this paper, we revisit the security of MFHE and present a secure one-round decryption protocol. We apply it to one of existing schemes and prove the scheme is secure against possible static adversaries. As an application, we construct a two round multiparty computation without common random string

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application 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. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE

Survey on Fully Homomorphic Encryption, Theory, and Applications

Data privacy concerns are increasing significantly in the context of Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next generation networks. Homomorphic Encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption (FHE). It consequently covers design fundamentals and security properties of FHE and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof

Guide to Fully Homomorphic Encryption over the [Discretized] Torus

First posed as a challenge in 1978 by Rivest et al., fully homomorphic encryption—the ability to evaluate any function over encrypted data— was only solved in 2009 in a breakthrough result by Gentry (Commun. ACM, 2010). After a decade of intense research, practical solutions have emerged and are being pushed for standardization. This guide is intended to practitioners. It explains the inner-workings of TFHE, a torus-based fully homomorphic encryption scheme. More exactly, it describes its implementation on a discretized version of the torus. It also explains in detail the technique of the programmable bootstrapping

SoK: Fully Homomorphic Encryption over the [Discretized] Torus

First posed as a challenge in 1978 by Rivest et al., fully homomorphic encryption—the ability to evaluate any function over encrypted data—was only solved in 2009 in a breakthrough result by Gentry (Commun. ACM, 2010). After a decade of intense research, practical solutions have emerged and are being pushed for standardization. This paper explains the inner-workings of TFHE, a torus-based fully homomorphic encryption scheme. More exactly, it describes its implementation on a discretized version of the torus. It also explains in detail the technique of the programmable bootstrapping. Numerous examples are provided to illustrate the various concepts and definitions

Improving TFHE: faster packed homomorphic operations and efficient circuit bootstrapping

In this paper, we present several methods to improve the evaluation of homomorphic functions, both for fully and for leveled homomorphic encryption. We propose two packing methods, in order to decrease the expansion factor and optimize the evaluation of look-up tables and random functions in TRGSW-based homomorphic schemes. We also extend the automata logic, introduced in [19, 12], to the efficient leveled evaluation of weighted automata, and present a new homomorphic counter called TBSR, that supports all the elementary operations that occur in a multiplication. These improvements speed-up the evaluation of most arithmetic functions in a packed leveled mode, with a noise overhead that remains additive. We finally present a new circuit bootstrapping that converts TLWE into low-noise TRGSW ciphertexts in just 137ms, which makes the leveled mode of TFHE composable, and which is fast enough to speed-up arithmetic functions, compared to the gate-by-gate bootstrapping given in [12]. Finally, we propose concrete parameter sets and timing comparison for all our constructions

On the IND-CCA1 Security of FHE Schemes

Fully homomorphic encryption (FHE) is a powerful tool in cryptography that allows one to perform arbitrary computations on encrypted material without having to decrypt it first. There are numerous FHE schemes, all of which are expanded from somewhat homomorphic encryption (SHE) schemes, and some of which are considered viable in practice. However, while these FHE schemes are semantically (IND-CPA) secure, the question of their IND-CCA1 security is much less studied, and we therefore provide an overview of the IND-CCA1 security of all acknowledged FHE schemes in this paper. To give this overview, we grouped the SHE schemes into broad categories based on their similarities and underlying hardness problems. For each category, we show that the SHE schemes are susceptible to either known adaptive key recovery attacks, a natural extension of known attacks, or our proposed attacks. Finally, we discuss the known techniques to achieve IND-CCA1-secure FHE and SHE schemes. We concluded that none of the proposed schemes were IND-CCA1-secure and that the known general constructions all had their shortcomings.publishedVersio