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

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

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

High-Performance VLSI Architectures for Lattice-Based Cryptography

Lattice-based cryptography is a cryptographic primitive built upon the hard problems on point lattices. Cryptosystems relying on lattice-based cryptography have attracted huge attention in the last decade since they have post-quantum-resistant security and the remarkable construction of the algorithm. In particular, homomorphic encryption (HE) and post-quantum cryptography (PQC) are the two main applications of lattice-based cryptography. Meanwhile, the efficient hardware implementations for these advanced cryptography schemes are demanding to achieve a high-performance implementation. This dissertation aims to investigate the novel and high-performance very large-scale integration (VLSI) architectures for lattice-based cryptography, including the HE and PQC schemes. This dissertation first presents different architectures for the number-theoretic transform (NTT)-based polynomial multiplication, one of the crucial parts of the fundamental arithmetic for lattice-based HE and PQC schemes. Then a high-speed modular integer multiplier is proposed, particularly for lattice-based cryptography. In addition, a novel modular polynomial multiplier is presented to exploit the fast finite impulse response (FIR) filter architecture to reduce the computational complexity of the schoolbook modular polynomial multiplication for lattice-based PQC scheme. Afterward, an NTT and Chinese remainder theorem (CRT)-based high-speed modular polynomial multiplier is presented for HE schemes whose moduli are large integers

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

SortingHat: Efficient Private Decision Tree Evaluation via Homomorphic Encryption and Transciphering

Machine learning as a service scenario typically requires the client to trust the server and provide sensitive data in plaintext. However, with the recent improvements in fully homomorphic encryption (FHE) schemes, many such applications can be designed in a privacy preserving way. In this work, we focus on such a problem, private decision tree evaluation (PDTE) --- where a server has a decision tree classification model, and a client wants to use the model to classify her private data without revealing the data or the classification result to the server. We present an efficient non-interactive design of PDTE, that we call SortingHat, based on FHE techniques. As part of our design, we solve multiple cryptographic problems related to FHE: (1) we propose a fast homomorphic comparison function where one input can be in plaintext format; (2) we design an efficient binary decision tree evaluation technique in the FHE setting, which we call homomorphic traversal, and apply it together with our homomorphic comparison to evaluate private decision tree classifiers, obtaining running times orders of magnitude faster than the state of the art; (3) we improve both the communication cost and the time complexity of transciphering, by applying our homomorphic comparison to the FiLIP stream cipher. Through a prototype implementation, we demonstrate that our improved transciphering solution runs around 400 times faster than previous works. We finally present a choice in terms of PDTE design: we present a version of SortingHat without transciphering that achieves significant improvement in terms of computation cost comparing to prior works; and another version with transciphering that has a communication cost about 20 thousand times smaller but comparable running time

FPT: a Fixed-Point Accelerator for Torus Fully Homomorphic Encryption

Fully Homomorphic Encryption is a technique that allows computation on encrypted data. It has the potential to change privacy considerations in the cloud, but computational and memory overheads are preventing its adoption. TFHE is a promising Torus-based FHE scheme that relies on bootstrapping, the noise-removal tool invoked after each encrypted logical/arithmetical operation. We present FPT, a Fixed-Point FPGA accelerator for TFHE bootstrapping. FPT is the first hardware accelerator to exploit the inherent noise present in FHE calculations. Instead of double or single-precision floating-point arithmetic, it implements TFHE bootstrapping entirely with approximate fixed-point arithmetic. Using an in-depth analysis of noise propagation in bootstrapping FFT computations, FPT is able to use noise-trimmed fixed-point representations that are up to 50% smaller than prior implementations. FPT is built as a streaming processor inspired by traditional streaming DSPs: it instantiates directly cascaded high-throughput computational stages, with minimal control logic and routing networks. We explore throughput-balanced compositions of streaming kernels with a user-configurable streaming width in order to construct a full bootstrapping pipeline. Our approach allows 100% utilization of arithmetic units and requires only a small bootstrapping key cache, enabling an entirely compute-bound bootstrapping throughput of 1 BS / 35us. This is in stark contrast to the classical CPU approach to FHE bootstrapping acceleration, which is typically constrained by memory and bandwidth. FPT is implemented and evaluated as a bootstrapping FPGA kernel for an Alveo U280 datacenter accelerator card. FPT achieves two to three orders of magnitude higher bootstrapping throughput than existing CPU-based implementations, and 2.5x higher throughput compared to recent ASIC emulation experiments.Comment: ACM CCS 202

Improved Filter Permutators: Combining Symmetric Encryption Design, Boolean Functions, Low Complexity Cryptography, and Homomorphic Encryption, for Private Delegation of Computations

Motivated by the application of delegating computation, we revisit the design of filter permutators as a general approach to build stream ciphers that can be efficiently evaluated in a fully homomorphic manner. We first introduce improved filter permutators that allow better security analyses, instances and implementations than the previously proposed FLIP family of stream ciphers. We also put forward the similarities between these improved constructions and a popular PRG design by Goldreich. Then, we exhibit the relevant cryptographic parameters of two families of Boolean functions, direct sums of monomials and XOR-MAJ functions, which give candidates to instantiate the improved filter permutator paradigm. We develop new Boolean functions techniques to study them, and refine Goldreich\u27s PRG locality bound for this purpose. We give an asymptotic analysis of the noise level of improved filter permutators instances using both kind of functions, and recommend them as good candidates for evaluation with a third-generation FHE scheme. Finally, we propose a methodology to evaluate the performance of such symmetric cipher designs in a FHE setting, which primarily focuses on the noise level of the symmetric ciphertexts (hence on the amount of operations on these ciphertextsthat can be homomorphically evaluated). Evaluations performed with HElib show that instances of improved filter permutators using direct sums of monomials as filter outperform all existing ciphers in the literature based on this criteria. We also discuss the (limited) overheads of these instances in terms of latency and throughput

Does Fully Homomorphic Encryption Need Compute Acceleration?

The emergence of cloud-computing has raised important privacy questions about the data that users share with remote servers. While data in transit is protected using standard techniques like Transport Layer Security (TLS), most cloud providers have unrestricted plaintext access to user data at the endpoint. Fully Homomorphic Encryption (FHE) offers one solution to this problem by allowing for arbitrarily complex computations on encrypted data without ever needing to decrypt it. Unfortunately, all known implementations of FHE require the addition of noise during encryption which grows during computation. As a result, sustaining deep computations requires a periodic noise reduction step known as bootstrapping. The cost of the bootstrapping operation is one of the primary barriers to the wide-spread adoption of FHE.In this paper, we present an in-depth architectural analysis of the bootstrapping step in FHE. First, we observe that se-cure implementations of bootstrapping exhibit a low arithmetic intensity (100MB) and as such, are heavily bound by the main memory bandwidth.Consequently, we demonstrate that existing workloads observe marginal performance gains from the design of bespoke high-throughput arithmetic units tailored to FHE. Secondly, we propose several cache-friendly algorithmic optimizations that improve the throughput in FHE bootstrapping by enabling upto3.2×higher arithmetic intensity and4.6×lower memory bandwidth. Our optimizations apply to a wide range of structurally similar computations such as private evaluation and training of machine learning models. Finally, we incorporate these optimizations into an architectural tool which, given a cache size, memory subsystem, the number of functional units and a desired security level, selects optimal cryptosystem parameters to maximize the bootstrapping throughput.Our optimized bootstrapping implementation represents a best-case scenario for compute acceleration of FHE. We show that despite these optimizations, bootstrapping (as well as other applications with similar computational structure) continue to remain bottlenecked by main memory bandwidth. We thus conclude that secure FHE implementations need to look beyond accelerated compute for further performance improvements and to that end, we propose new research directions to address the underlying memory bottleneck. In summary, our answer to the titular question is: yes, but only after addressing the memory bottleneck

Spiral: Fast, High-Rate Single-Server PIR via FHE Composition

We introduce the Spiral family of single-server private information retrieval (PIR) protocols. Spiral relies on a composition of two lattice-based homomorphic encryption schemes: the Regev encryption scheme and the Gentry-Sahai-Waters encryption scheme. We introduce new ciphertext translation techniques to convert between these two schemes and in doing so, enable new trade-offs in communication and computation. Across a broad range of database configurations, the basic version of Spiral simultaneously achieves at least a 4.5x reduction in query size, 1.5x reduction in response size, and 2x increase in server throughput compared to previous systems. A variant of our scheme, SpiralStreamPack, is optimized for the streaming setting and achieves a server throughput of 1.9 GB/s for databases with over a million records (compared to 200 MB/s for previous protocols) and a rate of 0.81 (compared to 0.24 for previous protocols). For streaming large records (e.g., a private video stream), we estimate the monetary cost of SpiralStreamPack to be only 1.9x greater than that of the no-privacy baseline where the client directly downloads the desired record