What a lovely hat

The field of fully homomorphic encryption (FHE) has seen many theoretical and computational advances in recent years, bringing the technology closer to practicality than ever before. For this reason, practitioners in related fields, such as machine learning, are increasingly interested in using FHE to provide privacy to their applications. Despite this progress, selecting secure and efficient parameters for FHE remains a complex and challenging task due to the intricate interdependencies...

Zero-knowledge proof (ZKP) is a cryptographic primitive that enables one party to prove the validity of a statement to other parties without disclosing any secret information. With its widespread adoption in applications such as blockchain and verifiable machine learning, the demand for generating zero-knowledge proofs has increased dramatically. In recent years, considerable efforts have been directed toward developing GPU-accelerated systems for proof generation. However, these previous...

As privacy concerns have arisen in machine learning, privacy-preserving machine learning (PPML) has received significant attention. Fully homomorphic encryption (FHE) and secure multi-party computation (MPC) are representative building blocks for PPML. However, in PPML protocols based on FHE and MPC, interaction between the client (who provides encrypted input data) and the evaluator (who performs the computation) is essential to obtain the final result in plaintext. Functional encryption...

Modern micro-architectural attacks use a variety of building blocks chained to develop a final exploit. However, since in most cases, the footprint of such attacks is not visible architecturally (like, in the file-system), it becomes trickier to defend against these. In light of this, several automated defence mechanisms use Hardware Performance Counters (HPCs) detect when the micro-architectural elements are being misused for a potential attacks (like flush-reload, Spectre, Meltdown etc.)....

Pseudorandom codes are error-correcting codes with the property that no efficient adversary can distinguish encodings from uniformly random strings. They were recently introduced by Christ and Gunn [CRYPTO 2024] for the purpose of watermarking the outputs of randomized algorithms, such as generative AI models. Several constructions of pseudorandom codes have since been proposed, but none of them are robust to error channels that depend on previously seen codewords. This stronger kind of...

The meteoric rise in power and popularity of machine learning models dependent on valuable training data has reignited a basic tension between the power of running a program locally and the risk of exposing details of that program to the user. At the same time, fundamental properties of quantum states offer new solutions to data and program security that can require strikingly few quantum resources to exploit, and offer advantages outside of mere computational run time. In this work, we...

\textit{Federated Learning} (FL) is a distributed machine learning paradigm that allows multiple clients to train models collaboratively without sharing local data. Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. However, existing FL works still face critical issues. \romannumeral1) It is difficult to achieving \textit{poisoning robustness} and \textit{data privacy} while ensuring high \textit{model accuracy}....

Approximate nearest neighbor search (ANNS), also known as vector search, is an important building block for varies applications, such as databases, biometrics, and machine learning. In this work, we are interested in the private ANNS problem, where the client wants to learn (and can only learn) the ANNS results without revealing the query to the server. Previous private ANNS works either suffers from high communication cost (Chen et al., USENIX Security 2020) or works under a weaker...

Federated Learning (FL) has gained lots of traction recently, both in industry and academia. In FL, a machine learning model is trained using data from various end-users arranged in committees across several rounds. Since such data can often be sensitive, a primary challenge in FL is providing privacy while still retaining utility of the model. Differential Privacy (DP) has become the main measure of privacy in the FL setting. DP comes in two flavors: central and local. In the former, a...

We present $\textit{Rhombus}$, a new secure matrix-vector multiplication (MVM) protocol in the semi-honest two-party setting, which is able to be seamlessly integrated into existing privacy-preserving machine learning (PPML) frameworks and serve as the basis of secure computation in linear layers. $\textit{Rhombus}$ adopts RLWE-based homomorphic encryption (HE) with coefficient encoding, which allows messages to be chosen from not only a field $\mathbb{F}_p$ but also a ring...

We address the problem of detecting and punishing shareholder collusion in secret-sharing schemes. We do it in the recently proposed cryptographic model called individual cryptography (Dziembowski, Faust, and Lizurej, Crypto 2023), which assumes that there exist tasks that can be efficiently computed by a single machine but distributing this computation across multiple (mutually distrustful devices) is infeasible. Within this model, we introduce a novel primitive called secret sharing...

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...

In scenarios where a seller holds sensitive data $x$, like employee / patient records or ecological data, and a buyer seeks to obtain an evaluation of specific function $f$ on this data, solutions in trustless digital environments like blockchain-based Web3 systems typically fall into two categories: (1) Smart contract-powered solutions and (2) cryptographic solutions leveraging tools such as adaptor signatures. The former approach offers atomic transactions where the buyer learns the...

The SPDZ protocol family is a popular choice for secure multi-party computation (MPC) in a dishonest majority setting with active adversaries. Over the past decade, a series of studies have focused on improving its offline phase, where special additive shares, called authenticated triples, are generated. However, to accommodate recent demands for matrix operations in secure machine learning and big integer arithmetic in distributed RSA key generation, updates to the offline phase are...

Distributed training that enables multiple parties to jointly train a model on their respective datasets is a promising approach to address the challenges of large volumes of diverse data for training modern machine learning models. However, this approach immedi- ately raises security and privacy concerns; both about each party wishing to protect its data from other parties during training and preventing leakage of private information from the model after training through various...

True Random Number Generators (TRNGs) and Physically Unclonable Functions (PUFs) are critical hardware primitives for cryptographic systems, providing randomness and device-specific security. TRNGs require complete randomness, while PUFs rely on consistent, device-unique responses. In this work, both primitives are implemented on a System-on-Chip Field-Programmable Gate Array (SoC FPGA), leveraging the integrated Phase-Locked Loops (PLLs) for robust entropy generation in PLLbased TRNGs. A...

Physically (or Physical) Unclonable Functions (PUFs) are basic and useful primitives in designing cryptographic systems. PUFs are designed to facilitate device authentication, secure boot, firmware integrity, and secure communications. To achieve these objectives, PUFs must exhibit both consistent repeatability and instance-specific randomness. The Arbiter PUF (APUF), recognized as the first silicon PUF, is capable of generating a substantial number of secret keys instantaneously based on...

In Side-Channel Analysis (SCA), statistical or machine learning methods are employed to extract secret information from power or electromagnetic (EM) traces. In many practical scenarios, raw power/EM traces can span hundreds of thousands of features, with relevant leakages occurring over only a few small segments. Consequently, existing SCAs often select a small number of features before launching the attack, making their success highly dependent on the feasibility of feature selection....

We propose an efficient non-interactive privacy-preserving Transformer inference architecture called Powerformer. Since softmax is a non-algebraic operation, previous studies have attempted to modify it to be HE-friendly, but these methods have encountered issues with accuracy degradation or prolonged execution times due to the use of multiple bootstrappings. We propose replacing softmax with a new ReLU-based function called the \textit{Batch Rectifier-Power max} (BRPmax) function without...

Federated Learning (FL) enables multiple clients to collaboratively train a machine learning model while keeping their data private, eliminating the need for data sharing. Two common approaches to secure aggregation (SA) in FL are the single-aggregator and multiple-aggregator models. This work focuses on improving the multiple-aggregator model. Existing multiple-aggregator protocols such as Prio (NSDI 2017), Prio+ (SCN 2022), Elsa (S&P 2023) either offer robustness only in the...

The machine learning problem of extracting neural network parameters has been proposed for nearly three decades. Functionally equivalent extraction is a crucial goal for research on this problem. When the adversary has access to the raw output of neural networks, various attacks, including those presented at CRYPTO 2020 and EUROCRYPT 2024, have successfully achieved this goal. However, this goal is not achieved when neural networks operate under a hard-label setting where the raw output...

The see-in-the-middle (SITM) attack combines differential cryptanalysis and the ability to observe differential patterns in the side-channel leakage traces to reveal the secret key of SPN-based ciphers. While SITM presents a fresh perspective to side-channel analysis and allows attacks on deeper cipher rounds, there are practical difficulties that come with this method. First, one must realize a visual inspection of millions of power traces. Second, there is a strong requirement to reduce...

Privacy-Preserving Machine Learning is one of the most relevant use cases for Secure Multiparty Computation (MPC). While private training of large neural networks such as VGG-16 or ResNet-50 on state-of-the-art datasets such as Imagenet is still out of reach, given the performance overhead of MPC, private inference is starting to achieve practical runtimes. However, we show that in contrast to plaintext machine learning, the usage of GPU acceleration for both linear and nonlinear neural...

In recent years, ML based differential distinguishers have been explored and compared with the classical methods. Complexity of a key recovery attack on block ciphers is calculated using the probability of a differential distinguisher provided by classical methods. Since theoretical computations suffice to calculate the data complexity in these cases, so there seems no restrictions on the practical availability of computational resources to attack a block cipher using classical methods....

Homomorphic Encryption (HE) is a cutting-edge cryptographic technique that enables computations on encrypted data to be mirrored on the original data. This has quickly attracted substantial interest from the research community due to its extensive practical applications, such as in cloud computing and privacy-preserving machine learning. In addition to confidentiality, the importance of authenticity has emerged to ensure data integrity during transmission and evaluation. To address...

Lattice cryptography schemes based on the learning with errors (LWE) hardness assumption have been standardized by NIST for use as post-quantum cryptosystems, and by HomomorphicEncryption.org for encrypted compute on sensitive data. Thus, understanding their concrete security is critical. Most work on LWE security focuses on theoretical estimates of attack performance, which is important but may overlook attack nuances arising in real-world implementations. The sole existing concrete...

Privacy-preserving machine learning (PPML) enables multiple distrusting parties to jointly train ML models on their private data without revealing any information beyond the final trained models. In this work, we study the client-aided two-server setting where two non-colluding servers jointly train an ML model on the data held by a large number of clients. By involving the clients in the training process, we develop efficient protocols for training algorithms including linear regression,...

Computing the maximum from a list of secret inputs is a widely-used functionality that is employed ei- ther indirectly as a building block in secure computation frameworks, such as ABY (NDSS’15) or directly used in multiple applications that solve optimisation problems, such as secure machine learning or secure aggregation statistics. Incremental distributed point function (I-DPF) is a powerful primitive (IEEE S&P’21) that significantly reduces the client- to-server communication and are...

Deduplication is a vital preprocessing step that enhances machine learning model performance and saves training time and energy. However, enhancing federated learning through deduplication poses challenges, especially regarding scalability and potential privacy violations if deduplication involves sharing all clients’ data. In this paper, we address the problem of deduplication in a federated setup by introducing a pioneering protocol, Efficient Privacy-Preserving Multi-Party Deduplication...

Model quantization has become a common practice in machine learning (ML) to improve efficiency and reduce computational/communicational overhead. However, adopting quantization in privacy-preserving machine learning (PPML) remains challenging due to the complex internal structure of quantized operators, which leads to inefficient protocols under the existing PPML frameworks. In this work, we propose a new PPML paradigm that is tailor-made for and can benefit from quantized models. Our...

Recent advancements in transformers have revolutionized machine learning, forming the core of Large language models (LLMs). However, integrating these systems into everyday applications raises privacy concerns as client queries are exposed to model owners. Secure multiparty computation (MPC) allows parties to evaluate machine learning applications while keeping sensitive user inputs and proprietary models private. Due to inherent MPC costs, recent works introduce model-specific optimizations...

Given the recent progress in machine learning (ML), the cryptography community has started exploring the applicability of ML methods to the design of new cryptanalytic approaches. While current empirical results show promise, the extent to which such methods may outperform classical cryptanalytic approaches is still somewhat unclear. In this work, we initiate exploration of the theory of ML-based cryptanalytic techniques, in particular providing new results towards understanding whether...

With concerns about data privacy growing in a connected world, cryptography researchers have focused on fully homomorphic encryption (FHE) for promising machine learning as a service solutions. Recent advancements have lowered the computational cost by several orders of magnitude, but the latency of fully homomorphic neural networks remains a barrier to adoption. This work proposes using multi-exit neural networks (MENNs) to accelerate the FHE inference. MENNs are network architectures that...

Generative AI, a significant technological disruptor in recent years, has impacted domains like augmented reality, coding assistance, and text generation. However, use of these models requires users to trust the model owners with their sensitive data given as input to the model. Fully Homomorphic Encryption (FHE) offers a promising solution, and many earlier works have investigated the use this technology for machine learning as a service (MLaaS) applications. Still, these efforts do not...

Decision trees are an important class of supervised learning algorithms. When multiple entities contribute data to train a decision tree (e.g. for fraud detection in the financial sector), data privacy concerns necessitate the use of a privacy-enhancing technology such as secure multi-party computation (MPC) in order to secure the underlying training data. Prior state-of-the-art (Hamada et al.) construct an MPC protocol for decision tree training with a communication of $\mathcal{O}(hmN\log...

Researchers across various fields seek to understand causal relationships but often find controlled experiments impractical. To address this, statistical tools for causal discovery from naturally observed data have become crucial. Non-linear regression models, such as Gaussian process regression, are commonly used in causal inference but have limitations due to high costs when adapted for secure computation. Support vector regression (SVR) offers an alternative but remains costly in an...

We present a variant of Function Secret Sharing (FSS) schemes tailored for point, comparison, and interval functions, featuring compact key sizes at the expense of additional comparison. While existing FSS constructions are primarily geared towards $2$-party scenarios, exceptions such as the work by Boyle et al. (Eurocrypt 2015) and Riposte (S&P 2015) have introduced FSS schemes for $p$-party scenarios ($p \geq 3$). This paper aims to achieve the most compact $p$-party FSS key size to date....

Fully Homomorphic Encryption (FHE) allows computation on encrypted data. Various software libraries have implemented the approximate- arithmetic FHE scheme CKKS, which is highly useful for applications in machine learning and data analytics; each of these libraries have differing performance and features. It is useful for developers and researchers to learn details about these libraries’ performance and their differences. Some previous work has profiled FHE and CKKS implementations for...

Lookup arguments allow an untrusted prover to commit to a vector $\vec f \in \mathbb{F}^n$ and show that its entries reside in a predetermined table $\vec t \in \mathbb{F}^N$. One of their key applications is to augment general-purpose SNARKs making them more efficient on subcomputations that are hard to arithmetize. In order for this "augmentation" to work out, a SNARK and a lookup argument should have some basic level of compatibility with respect to the commitment on $\vec f$. However,...

Machine learning is widely used for a range of applications and is increasingly offered as a service by major technology companies. However, the required massive data collection raises privacy concerns during both training and inference. Privacy-preserving machine learning aims to solve this problem. In this setting, a collection of servers secret share their data and use secure multi-party computation to train and evaluate models on the joint data. All prior work focused on the scenario...

Privacy is a major concern in large-scale digital applications, such as cloud-computing, machine learning services, and access control. Users want to protect not only their plain data but also their associated attributes (e.g., age, location, etc). Functional encryption (FE) is a cryptographic tool that allows fine-grained access control over encrypted data. However, existing FE fall short as they are either inefficient and far from reality or they leak sensitive user-specific...

Secure multi-party computation (MPC) in a three-party, honest majority scenario is currently the state-of-the-art for running machine learning algorithms in a privacy-preserving manner. For efficiency reasons, fixed-point arithmetic is widely used to approximate computation over decimal numbers. After multiplication in fixed-point arithmetic, truncation is required to keep the result's precision. In this paper, we present an efficient three-party truncation protocol secure in the presence of...

As deep learning is being widely adopted across various domains, ensuring the integrity of models has become increasingly crucial. Despite the recent advances in Zero-Knowledge Machine Learning (ZKML) techniques, proving the inference over large ML models is still prohibitive. To enable practical ZKML, model simplification techniques like pruning and quantization should be applied without hesitation. Contrary to conventional belief, recent development in ML space have demonstrated that these...

The field of Fully Homomorphic Encryption (FHE) has seen many theoretical and computational advances in recent years, bringing the technology closer to practicality than ever before. For this reason, practitioners from neighbouring fields such as machine learning have sought to understand FHE to provide privacy to their work. Unfortunately, selecting secure and efficient parameters in FHE is a daunting task due to the many interdependencies between the parameters involved. In this work, we...

Homomorphic encryption allows for computations on encrypted data without exposing the underlying plaintext, enabling secure and private data processing in various applications such as cloud computing and machine learning. This paper presents a comprehensive mathematical foundation for three prominent homomorphic encryption schemes: Brakerski-Gentry-Vaikuntanathan (BGV), Brakerski-Fan-Vercauteren (BFV), and Cheon-Kim-Kim-Song (CKKS), all based on the Ring Learning with Errors (RLWE) problem....

Collaborative graph learning represents a learning paradigm where multiple parties jointly train a graph neural network (GNN) using their own proprietary graph data. To honor the data privacy of all parties, existing solutions for collaborative graph learning are either based on federated learning (FL) or secure machine learning (SML). Although promising in terms of efficiency and scalability due to their distributed training scheme, FL-based approaches fall short in providing provable...

Secure equality testing and comparison are two important primitives that have been widely used in many secure computation scenarios, such as privacy-preserving machine learning, private set intersection, secure data mining, etc. In this work, we propose new constant-round two-party computation (2PC) protocols for secure equality testing and secure comparison. Our protocols are designed in the online/offline paradigm. Theoretically, for 32-bit integers, the online communication for our...

Secure Aggregation (SA) stands as a crucial component in modern Federated Learning (FL) systems, facilitating collaborative training of a global machine learning model while protecting the privacy of individual clients' local datasets. Many existing SA protocols described in the FL literature operate synchronously, leading to notable runtime slowdowns due to the presence of stragglers (i.e. late-arriving clients). To address this challenge, one common approach is to consider stragglers as...

In this research, we introduce MIND-Crypt, a novel attack framework that uses deep learning (DL) and transfer learning (TL) to challenge the indistinguishability of block ciphers, specifically SPECK32/64 encryption algorithm in CBC mode (Cipher Block Chaining) against Known Plaintext Attacks (KPA). Our methodology includes training a DL model with ciphertexts of two messages encrypted using the same key. The selected messages have the same byte-length and differ by only one bit at the binary...

The proliferation of artificial intelligence and big data has resulted in a surge in data demand and increased data dimensionality. This escalation has consequently heightened the costs associated with storage and processing. Concurrently, the confidential nature of data collected by various institutions, which cannot be disclosed due to personal privacy concerns, has exacerbated the challenges associated with data analysis and machine learning model training. Therefore, designing a secure...

Decentralized Multi-Client Functional Encryption (DMCFE) extends the basic functional encryption to multiple clients that do not trust each other. They can independently encrypt the multiple plaintext-inputs to be given for evaluation to the function embedded in the functional decryption key, defined by multiple parameter-inputs. And they keep control on these functions as they all have to contribute to the generation of the functional decryption keys. Tags can be used in the ciphertexts and...

Generative pre-trained transformers (GPT's) are a type of large language machine learning model that are unusually adept at producing novel, and coherent, natural language. Notably, these technologies have also been extended to computer programming languages with great success. However, GPT model outputs in general are stochastic and not always correct. For programming languages, the exact specification of the computer code, syntactically and algorithmically, is strictly required in order to...

Certain applications such as FHE transciphering require randomness while operating over encrypted data. This randomness has to be obliviously generated in the encrypted domain and remain encrypted throughout the computation. Moreover, it should be guaranteed that independent-looking random coins can be obliviously generated for different computations. In this work, we consider the homomorphic evaluation of pseudorandom functions (PRFs) with a focus on practical lattice-based candidates....

Privacy-preserving decision tree evaluation (PDTE) allows a client that holds feature vectors to perform inferences against a decision tree model on the server side without revealing feature vectors to the server. Our work focuses on the non-interactive batched setting where the client sends a batch of encrypted feature vectors and then obtains classifications, without any additional interaction. This is useful in privacy-preserving credit scoring, biometric authentication, and many more...

Machine learning (ML) as a service has emerged as a rapidly expanding field across various industries like healthcare, finance, marketing, retail and e-commerce, Industry 4.0, etc where a huge amount of data is gen- erated. To handle this amount of data, huge computational power is required for which cloud computing used to be the first choice. However, there are several challenges in cloud computing like limitations of bandwidth, network connectivity, higher latency, etc. To address...

Machine-learning systems continue to advance at a rapid pace, demonstrating remarkable utility in various fields and disciplines. As these systems continue to grow in size and complexity, a nascent industry is emerging which aims to bring machine-learning-as-a-service (MLaaS) to market. Outsourcing the operation and training of these systems to powerful hardware carries numerous advantages, but challenges arise when privacy and the correctness of work carried out must be ensured. Recent...

Machine learning as a service requires the client to trust the server and provide its own private information to use this service. Usually, clients may worry that their private data is being collected by server without effective supervision, and the server also aims to ensure proper management of the user data to foster the advancement of its services. In this work, we focus on private decision tree evaluation (PDTE) which can alleviates such privacy concerns associated with classification...

Fully Homomorphic Encryption (FHE) is a powerful Privacy-Enhancing Technology (PET) that enables computations on encrypted data without having access to the secret key. While FHE holds immense potential for enhancing data privacy and security, creating its practical applications is associated with many difficulties. A significant barrier is the absence of easy-to-use, standardized components that developers can utilize as foundational building blocks. Addressing this gap requires...

Running machine learning algorithms on encrypted data is a way forward to marry functionality needs common in industry with the important concerns for privacy when working with potentially sensitive data. While there is already a growing field on this topic and a variety of protocols, mostly employing fully homomorphic encryption or performing secure multiparty computation (MPC), we are the first to propose a protocol that makes use of a specialized encryption scheme that allows to do secure...

Side-channel analysis is an important part of the security evaluations of hardware components and more specifically of those that include cryptographic algorithms. Profiling attacks are among the most powerful attacks as they assume the attacker has access to a clone device of the one under attack. Using the clone device allows the attacker to make a profile of physical leakages linked to the execution of algorithms. This work focuses on the characteristics of this profile and the...

At S$\&$P 2023, a family of secure three-party computing protocols called Bicoptor was proposed by Zhou et al., which is used to compute non-linear functions in privacy preserving machine learning. In these protocols, two parties $P_0, P_1$ respectively hold the corresponding shares of the secret, while a third party $P_2$ acts as an assistant. The authors claimed that neither party in the Bicoptor can independently compromise the confidentiality of the input, intermediate, or output. In...

With the growing adoption of cloud computing, the ability to store data and delegate computations to powerful and affordable cloud servers have become advantageous for both companies and individual users. However, the security of cloud computing has emerged as a significant concern. Particularly, Cloud Service Providers (CSPs) cannot assure data confidentiality and computations integrity in mission-critical applications. In this paper, we propose a confidential and verifiable delegation...

The Gradient Boosting Decision Tree (GBDT) is a well-known machine learning algorithm, which achieves high performance and outstanding interpretability in real-world scenes such as fraud detection, online marketing and risk management. Meanwhile, two data owners can jointly train a GBDT model without disclosing their private dataset by executing secure Multi-Party Computation (MPC) protocols. In this work, we propose NodeGuard, a highly efficient two party computation (2PC) framework for...

This paper introduces a new method for training decision trees and random forests using CKKS homomorphic encryption (HE) in cloud environments, enhancing data privacy from multiple sources. The innovative Homomorphic Binary Decision Tree (HBDT) method utilizes a modified Gini Impurity index (MGI) for node splitting in encrypted data scenarios. Notably, the proposed training approach operates in a single cloud security domain without the need for decryption, addressing key challenges in...

Nowadays Federated learning (FL) is established as one of the best techniques for collaborative machine learning. It allows a set of clients to train a common model without disclosing their sensitive and private dataset to a coordination server. The latter is in charge of the model aggregation. However, FL faces some problems, regarding the security of updates, integrity of computation and the availability of a server. In this paper, we combine some new ideas like clients’ reputation with...

Classifying images has become a straightforward and accessible task, thanks to the advent of Deep Neural Networks. Nevertheless, not much attention is given to the privacy concerns associated with sensitive data contained in images. In this study, we propose a solution to this issue by exploring an intersection between Machine Learning and cryptography. In particular, Fully Homomorphic Encryption (FHE) emerges as a promising solution, as it enables computations to be performed on encrypted...

With the ongoing advances in machine learning (ML), cybersecurity solutions and security primitives are becoming increasingly vulnerable to successful attacks. Strong physically unclonable functions (PUFs) are a potential solution for providing high resistance to such attacks. In this paper, we propose a generalized attack model that leverages multiple chips jointly to minimize the cloning error. Our analysis shows that the entropy rate over different chips is a relevant measure to the new...

Side-channel analysis has become a cornerstone of modern hardware security evaluation for cryptographic accelerators. Recently, these techniques are also being applied in fields such as AI and Machine Learning to investigate possible threats. Security evaluations are reliant on standard test setups including commercial and open-source evaluation boards such as, SASEBO/SAKURA and ChipWhisperer. However, with shrinking design footprints and overlapping tasks on the same platforms, the quality...

Approximate fully homomorphic encryption (FHE) schemes, such as the CKKS scheme (Cheon, Kim, Kim, Song, ASIACRYPT '17), are among the leading schemes in terms of efficiency and are particularly suitable for Machine Learning (ML) tasks. Although efficient, approximate FHE schemes have some inherent risks: Li and Micciancio (EUROCRYPT '21) demonstrated that while these schemes achieved the standard notion of CPA-security, they failed against a variant, $\mathsf{IND}\mbox{-}\mathsf{CPA}^D$, in...

This paper belongs to a sequence of manuscripts that discuss generic and easy-to-apply security metrics for Strong Physical Unclonable Functions (PUFs). These metrics cannot and shall not fully replace in-depth machine learning (ML) studies in the security assessment of Strong PUF candidates. But they can complement the latter, serve in initial complexity analyses, and allow simple iterative design optimization. Moreover, they are computationally more efficient and far easier to...

Garbled Circuit (GC) is a basic technique for practical secure computation. GC handles Boolean circuits; it consumes significant network bandwidth to transmit encoded gate truth tables, each of which scales with the computational security parameter $\kappa$. GC optimizations that reduce bandwidth consumption are valuable. It is natural to consider a generalization of Boolean two-input one-output gates (represented by $4$-row one-column lookup tables, LUTs) to arbitrary $N$-row...

We construct pseudorandom error-correcting codes (or simply pseudorandom codes), which are error-correcting codes with the property that any polynomial number of codewords are pseudorandom to any computationally-bounded adversary. Efficient decoding of corrupted codewords is possible with the help of a decoding key. We build pseudorandom codes that are robust to substitution and deletion errors, where pseudorandomness rests on standard cryptographic assumptions. Specifically,...

Fully Homomorphic Encryption (FHE) is a powerful tool for performing privacy-preserving analytics over encrypted data. A promising method for FHE over real and complex numbers is approximate homomorphic encryption, instantiated with the Cheon-Kim-Kim-Song (CKKS) scheme. The CKKS scheme enables efficient evaluation for many privacy-preserving machine learning applications. While the efficiency advantages of CKKS are clear, there is currently a lot of confusion on how to securely instantiate...

Kyber KEM, the NIST selected PQC standard for Public Key Encryption and Key Encapsulation Mechanisms (KEMs) has been subjected to a variety of side-channel attacks, through the course of the NIST PQC standardization process. However, all these attacks targeting the decapsulation procedure of Kyber KEM either require knowledge of the ciphertexts or require to control the value of ciphertexts for key recovery. However, there are no known attacks in a blind setting, where the attacker does not...

A zero-knowledge proof of training (zkPoT) enables a party to prove that they have correctly trained a committed model based on a committed dataset without revealing any additional information about the model or the dataset. An ideal zkPoT should offer provable security and privacy guarantees, succinct proof size and verifier runtime, and practical prover efficiency. In this work, we present \name, a zkPoT targeted for deep neural networks (DNNs) that achieves all these goals at once. Our...

Matrix multiplication is a common operation in applications like machine learning and data analytics. To demonstrate the correctness of such an operation in a privacy-preserving manner, we propose zkMatrix, a zero-knowledge proof for the multiplication of committed matrices. Among the succinct non-interactive zero-knowledge protocols that have an $O(\log n)$ transcript size and $O(\log n)$ verifier time, zkMatrix stands out as the first to achieve $O(n^2)$ prover time and $O(n^2)$ RAM usage...

Learning with Errors (LWE) is a hard math problem underlying post-quantum cryptography (PQC) systems for key exchange and digital signatures, recently standardized by NIST. Prior work [Wenger et al., 2022; Li et al., 2023a;b] proposed new machine learning (ML)-based attacks on LWE problems with small, sparse secrets, but these attacks require millions of LWE samples to train on and take days to recover secrets. We propose three key methods—better pre-processing, angular embeddings and model...

The implementation security of post-quantum cryptography (PQC) algorithms has emerged as a critical concern with the PQC standardization process reaching its end. In a side-channel-assisted chosen-ciphertext attack, the attacker builds linear inequalities on secret key components and uses the belief propagation (BP) algorithm to solve. The number of inequalities leverages the query complexity of the attack, so the fewer the better. In this paper, we use the PQC standard algorithm...

The past years have witnessed a considerable increase in research efforts put into neural network-assisted profiled side-channel analysis (SCA). Studies have also identified challenges, e.g., closing the gap between metrics for machine learning (ML) classification and side-channel attack evaluation. In fact, in the context of NN-assisted SCA, the NN’s output distribution forms the basis for successful key recovery. In this respect, related work has covered various aspects of integrating...

Side-channel analysis based on machine learning, especially neural networks, has gained significant attention in recent years. However, many existing methods still suffer from certain limitations. Despite the inherent capability of neural networks to extract features, there remains a risk of extracting irrelevant information. The heavy reliance on profiled traces makes it challenging to adapt to remote attack scenarios with limited profiled traces. Besides, attack traces also contain...

We study a fundamental problem in Multi-Party Computation, which we call the Multiple Millionaires’ Problem (MMP). Given a set of private integer inputs, the problem is to identify the subset of inputs that equal the maximum (or minimum) of that set, without revealing any further information on the inputs beyond what is implied by the desired output. Such a problem is a natural extension of the Millionaires’ Problem, which is the very first Multi- Party Computation problem that was...

Artificial intelligence (AI) is a modern technology that allows plenty of advantages in daily life, such as predicting weather, finding directions, classifying images and videos, even automatically generating code, text, and videos. Other essential technologies such as blockchain and cybersecurity also benefit from AI. As a core component used in blockchain and cybersecurity, cryptography can benefit from AI in order to enhance the confidentiality and integrity of cyberspace. In this...

This paper introduces LERNA, a new framework for single-server secure aggregation. Our protocols are tailored to the setting where multiple consecutive aggregation phases are performed with the same set of clients, a fraction of which can drop out in some of the phases. We rely on an initial secret sharing setup among the clients which is generated once-and-for-all, and reused in all following aggregation phases. Compared to prior works [Bonawitz et al. CCS’17, Bell et al. CCS’20], the...

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...

Private computation of nonlinear functions, such as Rectified Linear Units (ReLUs) and max-pooling operations, in deep neural networks (DNNs) poses significant challenges in terms of storage, bandwidth, and time consumption. To address these challenges, there has been a growing interest in utilizing privacy-preserving techniques that leverage polynomial activation functions and kernelized convolutions as alternatives to traditional ReLUs. However, these alternative approaches often suffer...

The privacy-preserving machine learning (PPML) has gained growing importance over the last few years. One of the biggest challenges is to improve the efficiency of PPML so that the communication and computation costs of PPML are affordable for large machine learning models such as deep learning. As we know, linear algebra such as matrix multiplication occupies a significant part of the computation in deep learning such as deep convolutional neural networks (CNN). Thus, it is desirable to...

We introduce protocols for classical verification of quantum depth (CVQD). These protocols enable a classical verifier to differentiate between devices of varying quantum circuit depths, even in the presence of classical computation. The goal is to demonstrate that a classical verifier can reject a device with a quantum circuit depth of no more than $d$, even if the prover employs additional polynomial-time classical computation to deceive. Conversely, the verifier accepts a device with a...

The advent of transformers has brought about significant advancements in traditional machine learning tasks. However, their pervasive deployment has raised concerns about the potential leakage of sensitive information during inference. Existing approaches using secure multiparty computation (MPC) face limitations when applied to transformers due to the extensive model size and resource-intensive matrix-matrix multiplications. In this paper, we present BOLT, a privacy-preserving inference...

Privacy-preserving machine learning (PPML) techniques have gained significant popularity in the past years. Those protocols have been widely adopted in many real-world security-sensitive machine learning scenarios, e.g., medical care and finance. In this work, we introduce $\mathsf{Aegis}$~-- a high-performance PPML platform built on top of a maliciously secure 3-PC framework over ring $\mathbb{Z}_{2^\ell}$. In particular, we propose a novel 2-round secure comparison (a.k.a., sign bit...

Over years of the development of secure multi-party computation (MPC), many sophisticated functionalities have been made pratical and multi-dimensional operations occur more and more frequently in MPC protocols, especially in protocols involving datasets of vector elements, such as privacy-preserving biometric identification and privacy-preserving machine learning. In this paper, we introduce a new kind of correlation, called tensor triples, which is designed to make multi-dimensional MPC...

Explainable AI (XAI) refers to the development of AI systems and machine learning models in a way that humans can understand, interpret and trust the predictions, decisions and outputs of these models. A common approach to explainability is feature importance, that is, determining which input features of the model have the most significant impact on the model prediction. Two major techniques for computing feature importance are LIME (Local Interpretable Model-agnostic Explanations) and...

This study focuses on spotting and stopping new types of online threats by improving the UGRansome dataset to detect unusual activity in real-time. By blending different machine learning methods, like naïve tree-based ensemble learning and recursive feature elimination (RFE), the research achieves a high accuracy rate of 97%. Naïve Bayes (NB) stands out as the most effective classifier. The suggested setup, combining gradient boosting (GB) and random forest (RF) with NB, effectively...

CRYSTALS-Kyber is a key-encapsulation mechanism, whose security is based on the hardness of solving the learning-with-errors (LWE) problem over module lattices. As in its specification, Kyber prescribes the usage of the Number Theoretic Transform (NTT) for efficient polynomial multiplication. Side-channel assisted attacks against Post-Quantum Cryptography (PQC) algorithms like Kyber remain a concern in the ongoing standardization process of quantum-computer-resistant cryptosystems. Among the...

Secure multi-party computation (MPC) enables (joint) computations on sensitive data while maintaining privacy. In real-world scenarios, asymmetric trust assumptions are often most realistic, where one somewhat trustworthy entity interacts with smaller clients. We generalize previous two-party computation (2PC) protocols like MUSE (USENIX Security'21) and SIMC (USENIX Security'22) to the three-party setting (3PC) with one malicious party, avoiding the performance limitations of...

Secure Multiparty Computation (MPC) protocols enable secure evaluation of a circuit by several parties, even in the presence of an adversary who maliciously corrupts all but one of the parties. These MPC protocols are constructed using the well-known secret-sharing-based paradigm (SPDZ and SPD$\mathbb{Z}_{2^k}$), where the protocols ensure security against a malicious adversary by computing Message Authentication Code (MAC) tags on the input shares and then evaluating the circuit with these...

Computing the distance between two non-normalized vectors $\mathbfit{x}$ and $\mathbfit{y}$, represented by $\Delta(\mathbfit{x},\mathbfit{y})$ and comparing it to a predefined public threshold $\tau$ is an essential functionality used in privacy-sensitive applications such as biometric authentication, identification, machine learning algorithms ({\em e.g.,} linear regression, k-nearest neighbors, etc.), and typo-tolerant password-based authentication. Tackling a widely used distance...

Abstract—Large transformer-based models have realized state- of-the-art performance on lots of real-world tasks such as natural language processing and computer vision. However, with the increasing sensitivity of the data and tasks they handle, privacy has become a major concern during model deployment. In this work, we focus on private inference in two-party settings, where one party holds private inputs and the other holds the model. We introduce BumbleBee, a fast and...

Classic MLaaS solutions suffer from privacy-related risks since the user is required to send unencrypted data to the server hosting the MLaaS. To alleviate this problem, a thriving line of research has emerged called Privacy-Preserving Machine Learning (PPML) or secure MLaaS solutions that use cryptographic techniques to preserve the privacy of both the input of the client and the output of the server. However, these implementations do not take into consideration the possibility of...

We present a highly detectable, trustless watermarking scheme for LLMs: the detection algorithm contains no secret information, and it is executable by anyone. We embed a publicly-verifiable cryptographic signature into LLM output using rejection sampling. We prove that our scheme is cryptographically correct, sound, and distortion-free. We make novel uses of error-correction techniques to overcome periods of low entropy, a barrier for all prior watermarking schemes. We implement our scheme...

Domain Generation Algorithms (DGAs) are used by malware to generate pseudorandom domain names to establish communication between infected bots and Command and Control servers. While DGAs can be detected by machine learning (ML) models with great accuracy, offering DGA detection as a service raises privacy concerns when requiring network administrators to disclose their DNS traffic to the service provider. We propose the first end-to-end framework for privacy-preserving classification as a...