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

Efficient Homomorphic Integer Polynomial Evaluation based on GSW FHE

We introduce new methods to evaluate integer polynomials with GSW FHE, which has much slower noise growth and per integer multiplication cost $O((\log k/k)^{4.7454}/n)$ times the original GSW, where $k$ is the input plaintext width, $n$ is the LWE dimention parameter. Basically we reduce the integer multiplication noise by performing the evaluation between two kinds of ciphertexts, one in $\mathbb{Z}_q$ and another in $\mathbb{F}_2^{\lceil \log q \rceil}$. The conversion between two ciphertexts can be achieved by the integer bootstrapping. We also propose to solve the ciphertext expansion problem by symmetric encryption with stream ciphers

Amortized Bootstrapping Revisited: Simpler, Asymptotically-faster, Implemented

Micciancio and Sorrel (ICALP 2018) proposed a bootstrapping algorithm that can refresh many messages at once with sublinearly many homomorphic operations per message. However, despite the attractive asymptotic cost, it is unclear if their algorithm could ever be practical, which reduces the impact of their results. In this work, we follow their general framework, but propose an amortized bootstrapping that is conceptually simpler and asymptotically cheaper. We reduce the number of homomorphic operations per refreshed message from $O(3^\rho \cdot n^{1/\rho} \cdot \log n)$ to $O(\rho \cdot n^{1/\rho})$, and the noise overhead from $\tilde{O}(n^{2 + 3 \cdot \rho})$ to $\tilde{O}(n^{1 + \rho})$. We also make it more general, by handling non-binary messages and applying programmable bootstrapping. To obtain a concrete instantiation of our bootstrapping algorithm, we propose a double-CRT (aka RNS) version of the GSW scheme, including a new operation, called shrinking, used to speed-up homomorphic operations by reducing the dimension and ciphertext modulus of the ciphertexts. We also provide a C++ implementation of our algorithm, thus showing for the first time the practicability of the amortized bootstrapping. Moreover, it is competitive with existing bootstrapping algorithms, being even around 3.4 times faster than an equivalent non-amortized version of our bootstrapping

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

Batched Multi-hop Multi-key FHE from ring-LWE with Compact Ciphertext Extension

Traditional fully homomorphic encryption (FHE) schemes support computation on data encrypted under a single key. In STOC 2012, López-Alt et al. introduced the notion of multi-key FHE (MKFHE), which allows homomorphic computation on ciphertexts encrypted under different keys. In this work, we focus on MKFHE constructions from standard assumptions and propose a new construction of ring-LWE-based multi-hop MKFHE scheme. Our work is based on Brakerski-Gentry-Vaikuntanathan (BGV) FHE scheme where, in contrast, all the previous works on multi-key FHE with standard assumptions were based on Gentry-Sahai-Waters (GSW) FHE scheme. Therefore, our construction can encrypt ring elements rather than a single bit and naturally inherits the advantages in aspects of the ciphertext/plaintext ratio and the complexity of homomorphic operations. Moveover, the proposed MKFHE scheme supports the Chinese Remainder Theorem (CRT)-based ciphertexts packing technique, achieves $poly\left(k,L,\log n\right)$ computation overhead for $k$ users, circuits with depth at most $L$ and an $n$ dimensional lattice, and gives the first batched MKFHE scheme based on standard assumptions to our knowledge. Furthermore, the ciphertext extension algorithms of previous schemes need to perform complex computation on each ciphertext, while our extension algorithm just needs to generate evaluation keys for the extended scheme. So the complexity of ciphertext extension is only dependent on the number of associated parities but not on the number of ciphertexts. Besides, our scheme also admits a threshold decryption protocol from which a generalized two-round MPC protocol can be similarly obtained as prior works

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

Vector Encoding over Lattices and Its Applications

In this work, we design a new lattice encoding structure for vectors. Our encoding can be used to achieve a packed FHE scheme that allows some SIMD operations and can be used to improve all the prior IBE schemes and signatures in the series. In particular, with respect to FHE setting, our method improves over the prior packed GSW structure of Hiromasa et al. (PKC \u2715), as we do not rely on a circular assumption as required in their work. Moreover, we can use the packing and unpacking method to extract each single element, so that the homomorphic operation supports element-wise and cross-element-wise computation as well. In the IBE scenario, we improves over previous constructions supporting $O(\Lambda)$-bit length identity from lattices substantially, such as Yamada (Eurocrypt \u2716), Katsumata, Yamada (Asiacrypt \u2716) and Yamada (Crypto \u2717), by shrinking the master public key to three matrices from standard Learning With Errors assumption. Additionally, our techniques from IBE can be adapted to construct a compact digital signature scheme, which achieves existential unforgeability under the standard Short Integer Solution (SIS) assumption with small polynomial parameters

NTRU-ν\nu-um: Secure Fully Homomorphic Encryption from NTRU with Small Modulus

NTRUEncrypt is one of the first lattice-based encryption schemes. Furthermore, the earliest fully homomorphic encryption (FHE) schemes rely on the NTRU problem. Currently, NTRU is one of the leading candidates in the NIST post-quantum standardization competition. What makes NTRU appealing is the age of the cryptosystem and relatively good performance. Unfortunately, FHE based on NTRU became impractical due to efficient attacks on NTRU instantiations with ``overstretched'' modulus. In particular, currently, NTRU-based FHE schemes to support a reasonable circuit depth require instantiating NTRU with a very large modulus. Breaking the NTRU problem for such large moduli turns out to be easy. Due to these attacks, any serious work on practical NTRU-based FHE essentially stopped. In this paper, we reactivate research on practical FHE that can be based on NTRU. We design an efficient bootstrapping scheme in which the noise growth is small enough to keep the modulus to dimension ratio relatively small, thus avoiding the negative consequences of ``overstretching'' the modulus. Our bootstrapping algorithm is an accumulator-type bootstrapping scheme analogous to AP/FHEW/TFHE. Finally, we show that we can use the bootstrapping procedure to compute any function over $\mathbb{Z}_t$. Consequently, we obtain one of the fastest FHE bootstrapping schemes able to compute any function over elements of a finite field alongside reducing the error