What a lovely hat

Blind rotation is the dominant computational bottleneck in bootstrapping for bitwise FHE schemes such as TFHE. Existing constructions typically evaluate $O(n)$ sequential external products for an LWE secret of dimension $n$, incurring substantial latency and a large number of NTT/iNTT operations. In this work, we present a new framework for NTRU-based bootstrapping that reduces the sequential complexity of blind rotation for sparse binary LWE secrets. Inspired by Jain et al. (CRYPTO 2026),...

Anonymous communication systems aim to hide which user sent which message. Existing designs span efficient mixnets that rely on at least one honest mix server and decentralized protocols such as Dining Cryptographers networks (DC-nets) or secure multi-party computation (MPC)-based shuffles, which typically require greater communication or interaction. We introduce \emph{verifiable self-mix} (VSM), an anonymity architecture for privately placing messages in a public bulletin-board...

Running Transformer inference and retrieval-augmented generation (RAG) over confidential data forces a choice: either expose prompts and documents to a cloud operator, or keep the data on-premises, which confines the deployment to weaker self-hosted models. Existing defenses span five mechanism families: secure computation (MPC and FHE), trusted execution environments (TEEs), static obfuscation, differential privacy, and hybrid TEE-and-obfuscation splits. No prior systematization compares...

Public blockchains expose all state and computation by default, which is incompatible with financial applications that require confidentiality. Solana achieves high throughput and sub-second confirmation, making it an attractive settlement layer, yet it offers no general mechanism for computing over encrypted state: fully homomorphic encryption (FHE) remains orders of magnitude too slow for interactive use, secure multi-party computation (MPC) incurs heavy communication, and Solana’s native...

Fully homomorphic encryption (FHE) enables privacy-preserving neural network inference but suffers from high overhead from homomorphic convolutions, polynomial activation approximations, and CKKS bootstrapping. This paper presents BootNet, a unified framework that fuses all three operations into a single bootstrapping invocation per CNN layer, achieving convolution, ReLU, and noise refresh simultaneously. Prior works are able to fuse convolution into bootstrapping using CinS encoding...

Although recent surveys on privacy-enhancing technologies concluded that FHE cannot feasibly evaluate non-linear activation functions in modern ML architectures, 20 CKKS-based frameworks have since demonstrated end-to-end private inference of LLMs with up to 8B parameters. However, as the field grows rapidly, the literature has become fragmented. Frameworks differ in ciphertext packing layouts, model fidelity, software and hardware stacks, and reported metrics, which hinder direct comparison...

In modern computing workloads, performance is increasingly constrained not by computation, but by the cost of moving data. This shift reflects both the scale and structure of contemporary applications, in which large data sets are subjected to repeated transformations across memory hierarchies, interconnects and distributed systems. A similar pattern appears across domains including fully homomorphic encryption, post-quantum cryptography and artificial intelligence: intermediate...

We describe THED, a threshold version of the Dilithium signature scheme (ML-DSA), whose issued signatures are valid for the genuine Dilithium verification algorithm. The signing protocol has two rounds of communication, one of which that lends itself to preprocessing. The scheme supports arbitrary number of users and threshold parameter. The construction consists in running Dilithium's signing algorithm under Threshold Fully Homomorphic Encryption (ThFHE), except for the computation of...

Fully Homomorphic Encryption (FHE) enables arbitrary computation on encrypted data without decryption, providing strong privacy guarantees for secure cloud computing, encrypted analytics, and privacy-preserving machine learning. However, practical deployment of FHE remains limited by the high computational cost of polynomial arithmetic over large modular rings. In particular, Number Theoretic Transform (NTT)–based polynomial multiplication dominates the execution time of modern lattice-based...

Fully Homomorphic Encryption (FHE) allows users to offload large computations to servers without revealing the underlying data. Due to this unique feature, it is applicable to a variety of domains, including privacy-preserving Machine Learning. However, all FHE schemes have two problems- slow encryption/decryption and substantial ciphertext expansion. Thus, despite its significant potential, the practical implementation of FHE faces considerable challenges due to massive computation and...

We propose Libra, a compiler framework that automates efficient code generation for cross-scheme fully homomorphic encryption (FHE) on highly parallel computing architectures. While it is known that leveraging multiple FHE schemes in a single application can improve the overall efficiency, the exact mapping of cross-scheme FHE operators onto high-performance architectures, such as general-purpose graphic processing units (GPGPUs), remains challenging. To address such challenge, Libra...

Homomorphic Encryption (HE) enables secure cloud computing through computations on encrypted data, but the physical execution of HE workloads on shared GPUs can still expose relevant metadata through microarchitectural behavior. Implementation-level irregularities in key switching, rounding, and modular correction may create observable hardware footprints even when cryptographic confidentiality remains intact. We present a case study of BFV relinearization in a state-of-the-art GPU HE...

Blockchains have achieved substantial progress in scalability and fault tolerance, yet they remain fundamentally limited in confidentiality, hindering adoption by businesses, communities, and individuals who require privacy-preserving computations. Existing zero-knowledge (ZK) solutions provide partial privacy guarantees but struggle with performance and composability, especially for multi-party computations over shared private state. In this work, we introduce gcVM, a novel extension to...

Deep neural networks (DNNs), a hot topic in this decade, are already solving many practical problems previously unchallenged. There are clear use cases of strong requirements for privacy protection in DNN models and input data. Fully Homomorphic Encryption (FHE) schemes provide privacy by enabling operations upon encrypted data with post-quantum security, at the expense of vast data size increase. Overwhelming execution times and memory sizes currently limit DNN inference with FHE to...

Ethereum has established itself as a world computer, enabling general-purpose, decentralized, and verifiable computation via smart contracts on a globally replicated state. However, because all computations and state are public by default, it is fundamentally unsuitable for confidential smart contracts that jointly process private data from multiple users. This motivates the notion of a private world computer: an ideal future form of Ethereum that preserves its integrity and availability...

Fully Homomorphic Encryption (FHE) and Post-Quantum Cryptography (PQC) involve polynomial multiplications, which are a common performance bottleneck. To resolve this bottleneck, polynomial multiplications are often accelerated in hardware using the Number-Theoretic Transformation (NTT) or the Fast Fourier Transformation (FFT). In particular, NTT operates over modular rings while FFT operates over complex numbers. NTT and FFT are widely deployed in applications with diverse parameter sets,...

Fully homomorphic encryption (FHE) enables computations over encrypted data without the need for decryption. Recently there has been an increased interest in developing FHE based algorithms to facilitate encrypted matrix multiplication (EMM) due to rising data security concerns surrounding cyber-physical systems, sensor processing, blockchain, and machine learning. Presently, FHE operations have a high computational overhead, resulting in an increased need for low operational complexity...

Fully Homomorphic Encryption (FHE) is a key technology to enable privacy-preserving computation. While optimized FHE implementations already exist, the inner workings of FHE are technically complex. This makes it challenging, especially for non-experts, to develop highly-efficient FHE programs that can exploit the advanced hardware of today. Although several compilers have emerged to help in this process, due to design choices, they are limited in terms of application support and the...

This work showcases Quatorze-bis, a state-of-the-art Number Theoretic Transform circuit for TFHE-like cryptosystems on FPGAs. It contains a novel modular multiplication design for modular multiplication with a constant for a constant modulus. This modular multiplication design does not require any DSP units or any dedicated multiplier unit, nor does it require extra logic when compared to the state-of-the-art modular multipliers. Furthermore, we present an implementation of a constant...

In this work, we report on the latest GPU implementations of the three well-known methods for the key switching operation, which is critical for Fully Homomorphic Encryption (FHE). Additionally, for the first time in the literature, we provide implementations of all three methods in GPU for leveled CKKS schemes. To ensure a fair comparison, we employ the most recent GPU implementation of the number-theoretic transform (NTT), which is the most time-consuming operation in key switching, and...

Emerging cryptographic systems such as Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKP) are computation- and data-intensive. FHE and ZKP implementations in software and hardware largely rely on the von Neumann architecture, where a significant amount of energy is lost on data movements. A promising computing paradigm is computing in memory (CIM), which enables computations to occur directly within memory, thereby reducing data movements and energy consumption. However,...

Fully Homomorphic Encryption (FHE) enables privacy-preserving computation but imposes significant computational and communication overhead on the client for the public-key encryption. To alleviate this burden, previous works have introduced the Hybrid Homomorphic Encryption (HHE) paradigm, which combines symmetric encryption with homomorphic decryption to enhance performance for the FHE client. While early HHE schemes focused on binary data, modern versions now support integer prime fields,...

In this paper, we present a comprehensive analysis of various modular multiplication methods for Number Theoretic Transform (NTT) on FPGA. NTT is a critical and time-intensive component of Fully Homomorphic Encryption (FHE) applications while modular multiplication consumes a significant portion of the design resources in an NTT implementation. We study the existing modular reduction approaches from the literature, and implement particular methods on FPGA. Specifically Word-Level Montgomery...

FHE enables computations on encrypted data, proving itself to be an essential building block for privacy-preserving applications. However, it involves computationally demanding operations such as polynomial multiplication, with the NTT being the state-of-the-art solution to perform it. Considering that most FHE schemes operate over the negacyclic ring of polynomials, we introduce a novel formulation of the hierarchical Four-Step NTT approach for the negacyclic ring, eliminating the need for...

Modern cryptographic techniques such as fully homomorphic encryption (FHE) have recently gained broad attention. Most of these cryptosystems rely on lattice problems wherein polynomial multiplication forms the computational bottleneck. A popular method to accelerate these polynomial multiplications is the Number-Theoretic Transformation (NTT). Recent works aim to improve the practical deployability of NTT and propose toolchains supporting the NTT hardware accelerator design processes....

In aggregation queries, predicate parameters often reveal user intent. Protecting these parameters is critical for user privacy, regardless of whether the database is public or private. While most existing works focus on private data settings, we address a public data setting where the server has access to the database. Current solutions for this setting either require additional setups (e.g., noncolluding servers, hardware enclaves) or are inefficient for practical workloads. Furthermore,...

Speed efficiency, memory optimization, and quantum resistance are essential for safeguarding the performance and security of cloud computing environments. Fully Homomorphic Encryption (FHE) addresses this need by enabling computations on encrypted data without requiring decryption, thereby maintaining data privacy. Additionally, lattice-based FHE is quantum secure, providing defense against potential quantum computer attacks. However, the performance of current FHE schemes remains...

HEonGPU is a high-performance library designed to optimize Fully Homomorphic Encryption (FHE) operations on Graphics Processing Unit (GPU). By leveraging the parallel processing capac- ity of GPUs, HEonGPU significantly reduces the computational overhead typically associated with FHE by executing complex operation concurrently. This allows for faster execution of homomorphic computations on encrypted data, enabling real-time applications in privacy-preserving machine learn- ing and secure...

Making the most of TFHE programmable bootstrapping to evaluate functions or operators otherwise challenging to perform with only the native addition and multiplication of the scheme is a very active line of research. In this paper, we systematize this approach and apply it to build an 8-bit FHE processor abstraction, i.e., a software entity that works over FHE-encrypted 8-bit data and presents itself to the programmer by means of a conventional-looking assembly instruction set. In doing so,...

Fully homomorphic encryption (FHE) has become progressively more viable in the years since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in an efficient way for general computation remains prohibitively difficult for the average programmer. In this work, we introduce a new design for a fully homomorphic processor, dubbed Juliet, to enable faster operations on encrypted data using the state-of-the-art TFHE and cuFHE libraries for both CPU and GPU...

One of the main issues to deal with for fully homomorphic encryption is the noise growth when operating on ciphertexts. To some extent, this can be controlled thanks to a so-called gadget decomposition. A gadget decomposition typically relies on radix- or CRT-based representations to split elements as vectors of smaller chunks whose inner products with the corresponding gadget vector rebuilds (an approximation of) the original elements. Radix-based gadget decompositions present the advantage...

Fully Homomorphic Encryption (FHE) is a transformative technology that enables computations on encrypted data without requiring decryption, promising enhanced data privacy. However, its adoption has been limited due to significant performance overheads. Recent advances include the proposal of domain-specific, highly-parallel hardware accelerators designed to overcome these limitations. This paper introduces PICA, a comprehensive compiler framework designed to simplify the programming of...

During the past few decades, the field of image processing has grown to cradle hundreds of applications, many of which are outsourced to be computed on trusted remote servers. More recently, Fully Homomorphic Encryption (FHE) has grown in parallel as a powerful tool enabling computation on encrypted data, and transitively on untrusted servers. As a result, new FHE-supported applications have emerged, but not all have reached practicality due to hardware, bandwidth or mathematical...

Recognizing the importance of a fast and resource-efficient polynomial multiplication in homomorphic encryption, in this paper, we design a multiplier-less number theoretic transform using a Fermat number as an auxiliary modulus. To make this algorithm scalable with the degree of polynomial, we apply a univariate to multivariate polynomial ring transformation. We develop an accelerator architecture for fully homomorphic encryption using these algorithmic techniques for efficient...

Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure and require a large (256-bit) field. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol...

Fully Homomorphic Encryption (FHE) enables computation on encrypted data, holding immense potential for enhancing data privacy and security in various applications. Presently, FHE adoption is hindered by slow computation times, caused by data being encrypted into large polynomials. Optimized FHE libraries and hardware acceleration are emerging to tackle this performance bottleneck. Often, these libraries implement the Number Theoretic Transform (NTT) algorithm for efficient polynomial...

In this work, we introduce FANNG-MPC, a versatile secure multi-party computation framework capable to offer active security for privacy preserving machine learning as a service (MLaaS). Derived from the now deprecated SCALE-MAMBA, FANNG is a data-oriented fork, featuring novel set of libraries and instructions for realizing private neural networks, effectively reviving the popular framework. To the best of our knowledge, FANNG is the first MPC framework to offer actively secure MLaaS in the...

Fully homomorphic encryption (FHE) can perform computations on encrypted data, allowing us to analyze sensitive data without losing its security. The main issue for FHE is its lower performance, especially for high-precision computations, compared to calculations on plaintext data. Making FHE viable for practical use requires both algorithmic improvements and hardware acceleration. Recently, Klemsa and Önen (CODASPY'22) presented fast homomorphic algorithms for high-precision integers,...

The number theoretic transform (NTT) permits a very efficient method to perform multiplication of very large degree polynomials, which is the most time-consuming operation in fully homomorphic encryption (FHE) schemes and a class of non-interactive succinct zero-knowledge proof systems such as zk-SNARK. Efficient modular arithmetic plays an important role in the performance of NTT, and therefore it is studied extensively. The access pattern to the memory, on the other hand, may play much...

Fully Homomorphic Encryption (FHE) enables privacy-preserving computation and has many applications. However, its practical implementation faces massive computation and memory overheads. To address this bottleneck, several Application-Specific Integrated Circuit (ASIC) FHE accelerators have been proposed. All these prior works put every component needed for FHE onto one chip (monolithic), hence offering high performance. However, they encounter common challenges associated with large-scale...

Ring-LWE based homomorphic encryption computations in large depth use a combination of two techniques: 1) decomposition of big numbers into small limbs/digits, and 2) efficient cyclotomic multiplications modulo $X^N + 1$. It was long believed that the two mechanisms had to be strongly related, like in the full-RNS setting that uses a CRT decomposition of big numbers over an NTT-friendly family of prime numbers, and NTT over the same primes for multiplications. However, in this setting, NTT...

Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees the privacy and security of user data during computation. FHE algorithms can perform unlimited arithmetic computations directly on encrypted data without decrypting it. Thus, even when processed by untrusted systems, confidential data is never exposed. In this work, we develop new techniques for accelerated encrypted execution and demonstrate the significant performance advantages of our approach. Our current focus...

The magic of Fully Homomorphic Encryption (FHE) is that it allows operations on encrypted data without decryption. Unfortunately, the slow computation time limits their adoption. The slow computation time results from the vast memory requirements (64Kbits per ciphertext), a bootstrapping key of 1.3 GB, and sizeable computational overhead (10240 NTTs, each NTT requiring 5120 32-bit multiplications). We accelerate the FHEW bootstrapping in hardware on a high-end U280 FPGA. To reduce the...

Implementation of Fully Homomorphic Encryption (FHE) is challenging. Especially when considering hardware acceleration, the major performance bottleneck is data transfer. Here we propose an algebraic framework called Heterogenous Lattice Graph (HLG) to build and process computing graphs in Residue Number System (RNS), which is the basis of high performance implementation of mainstream FHE algorithms. There are three main design goals for HLG framework: • Design a dedicated IR (HLG...

Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed. Traditional security techniques require data to be decrypted before performing any computation. When processed on untrusted systems the decrypted data is vulnerable to attacks to extract the sensitive information. To address these vulnerabilities Fully Homomorphic Encryption (FHE) keeps the data encrypted...

Privacy computing is a collection of a series of technical systems that intersect and integrate many disciplines such as cryptography, statistics, artificial intelligence, and computer hardware. On the premise of not exposing the original data, it can realize the fusion, sharing, circulation and calculation of data and its value in a manageable, controllable and measurable way. In the case of ensuring that the data is not leaked, it can achieve the purpose of making the data available and...

Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cryptography. While promising, these techniques have received limited use due to their extreme overheads of running on general-purpose machines. In this paper, we present a novel vector Instruction Set Architecture (ISA) and microarchitecture for accelerating the ring-based computations of RLWE. The ISA, named B512,...

This paper presents a correct-by-construction method of designing an FHE model based on the automated program verifier Dafny. We model FHE operations from the ground up, including fundamentals like GCD, coprimality, Montgomery multiplications, and polynomial operations, etc., and higher level optimizations such as Residue Number System (RNS) and Number Theoretic Transform (NTT). The fully formally verified FHE model serves as a reference design for both software stack development and...

Number Theoretic Transform (NTT) is a fundamental building block in emerging cryptographic constructions like fully homomorphic encryption, post-quantum cryptography and zero-knowledge proof. In this work, we introduce Proteus, an open-source parametric hardware to generate pipelined architectures for the NTT. For a given parameter set including the polynomial degree and size of the coefficient modulus, Proteus can generate Radix-2 NTT architectures using Single-path Delay Feedback (SDF) and...

Fully Homomorphic Encryption (FHE) is a technique that allows computation on encrypted data. It has the potential to drastically change privacy considerations in the cloud, but high computational and memory overheads are preventing its broad adoption. TFHE is a promising Torus-based FHE scheme that heavily 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...

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...

Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key. We introduce OpenFHE, a new open-source FHE software library that incorporates selected design ideas from prior FHE projects, such as PALISADE, HElib, and HEAAN, and includes several new design concepts and ideas. The main new design features can be summarized as follows: (1) we assume from the very beginning that all...

Fully Homomorphic Encryption (FHE) allows for secure computation on encrypted data. Unfortunately, huge memory size, computational cost and bandwidth requirements limit its practicality. We present BASALISC, an architecture family of hardware accelerators that aims to substantially accelerate FHE computations in the cloud. BASALISC is the first to implement the BGV scheme with fully-packed bootstrapping – the noise removal capability necessary for arbitrary-depth computation. It supports a...

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....

A number of arithmetization-oriented ciphers emerge for use in advanced cryptographic protocols such as secure multi-party computation (MPC), fully homomorphic encryption (FHE) and zero-knowledge proofs (ZK) in recent years. The standard block ciphers like AES and the hash functions SHA2/SHA3 are proved to be efficient in software and hardware but not optimal to use in this field, for this reason, new kind of cryptographic primitives were proposed recently. However, unlike traditional ones,...

Machine learning as a service (MLaaS) has risen to become a prominent technology due to the large development time, amount of data, hardware costs, and level of expertise required to develop a machine learning model. However, privacy concerns prevent the adoption of MLaaS for applications with sensitive data. A promising privacy preserving solution is to use fully homomorphic encryption (FHE) to perform the ML computations. Recent advancements have lowered computational costs by several...

With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on...

We present a novel $\textit{secure search}$ protocol on data and queries encrypted with Fully Homomorphic Encryption (FHE). Our protocol enables organizations (client) to (1) securely upload an unsorted data array $x=(x[1],\ldots,x[n])$ to an untrusted honest-but-curious sever, where data may be uploaded over time and from multiple data-sources; and (2) securely issue repeated search queries $q$ for retrieving the first element $(i^*,x[i^*])$ satisfying an agreed matching criterion $i^* =...

The development of cloud computing services is restrained by privacy concerns. Centralized medical services for instance, require a guarantee of confidentiality when using outsourced computation platforms. Fully Homomorphic Encryption is an intuitive solution to address such issue, but until 2009, existing schemes were only able to evaluate a reduced number of operations (Partially Homomorphic Encryption). In 2009, C. Gentry proposed a blueprint to construct FHE schemes from SHE...

After being introduced in 2009, the first fully homomorphic encryption (FHE) scheme has created significant excitement in academia and industry. Despite rapid advances in the last 6 years, FHE schemes are still not ready for deployment due to an efficiency bottleneck. Here we introduce a custom hardware accelerator optimized for a class of reconfigurable logic to bring LTV based somewhat homomorphic encryption (SWHE) schemes one step closer to deployment in real-life applications. The...

This paper proposes a new hardware architecture of Integer-FFT multiplier for super-size integer multiplications. Firstly, a basic hardware archi-tecture, with the feature of low hardware cost, of the Integer-FFT multiplication algorithm using the serial FFT architecture, is proposed. Next, a modified hardware architecture with a shorter multiplication latency than the basic archi-tecture is presented. Thirdly, both architectures are implemented, verified and compared on the Xilinx Virtex-7...

A fully homomorphic encryption (FHE) scheme is envisioned as being a key cryptographic tool in building a secure and reliable cloud computing environment, as it allows arbitrarily evaluation of a ciphertext without revealing the plaintext. However, existing FHE implementations remain impractical due to their very high time and resource costs. Of the proposed schemes that can perform FHE to date, a scheme known as FHE over the integers has the ad-vantage of comparatively simpler theory, as...

Code obfuscation is one of the most powerful concepts in cryptography. It could yield functional encryption, digital rights management, and maybe even secure cloud computing. However, general code obfuscation has been proven impossible and the research then focused on obfuscating very specific functions, studying weaker security definitions for obfuscation, and using tamper-proof hardware tokens to achieve general code obfuscation. Following this last line this work presents the first scheme...

Cloud computing denotes an architectural shift toward thin clients and conveniently centralized provision of computing resources. Clients’ lack of direct resource control in the cloud prompts concern about the potential for data privacy violations, particularly abuse or leakage of sensitive information by service providers. Cryptography is an oft-touted remedy. Among its most powerful primitives is fully homomorphic encryption (FHE), dubbed by some the field’s “Holy Grail,” and recently...