Blockchain technology ensures accountability, transparency, and redundancy in critical applications, includ- ing IoT with embedded systems. However, the reliance on public-key cryptography (PKC) makes blockchain vulnerable to quantum computing threats. This paper addresses the urgent need for quantum-safe blockchain solutions by integrating Post- Quantum Cryptography (PQC) into blockchain frameworks. Utilizing algorithms from the NIST PQC standardization pro- cess, we aim to fortify...

In crowdsourcing systems, requesters publish tasks, and interested workers provide answers to get rewards. Worker anonymity motivates participation since it protects their privacy. Anonymity with unlinkability is an enhanced version of anonymity because it makes it impossible to ``link'' workers across the tasks they participate in. Another core feature of crowdsourcing systems is worker quality which expresses a worker's trustworthiness and quantifies their historical performance. In this...

Coercion is a challenging and multi-faceted threat that prevents people from expressing their will freely. Similarly, vote-buying does to undermine the foundation of free democratic elections. These threats are especially dire for remote electronic voting, which relies on voters to express their political will freely but happens in an uncontrolled environment outside the polling station and the protection of the ballot booth. However, electronic voting in general, both in-booth and remote,...

Auditing throughout a fiscal year is integral to organizations with transactional activity. Organizations transact with each other and record the details for all their economical activities so that a regulatory committee can verify the lawfulness and legitimacy of their activity. However, it is computationally infeasible for the committee to perform all necessary checks for each organization. To overcome this, auditors assist in this process: organizations give access to all their internal...

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...

This paper reports the experimental results related to lineage event storage via smart contracts deployed on private and public blockchain. In our experiments we measure the following three metrics: the cost to deploy the storage smart contract on the blockchain, which measures the initial expenditure, typically in gas units, required to deploy the smart contract that facilitates lineage event storage, then the time and gas costs needed to store a lineage event. We investigated both single...

Online ride-sharing services (RSS) have become very popular owing to increased awareness of environmental concerns and as a response to increased traffic congestion. To request a ride, users submit their locations and route information for ride matching to a service provider (SP), leading to possible privacy concerns caused by leakage of users' location data. We propose QuickPool, an efficient SP-aided RSS solution that can obliviously match multiple riders and drivers simultaneously,...

Blockchain consensus, a.k.a. BFT SMR, are protocols enabling $n$ processes to decide on an ever-growing chain. The fastest known asynchronous one is called 2-chain VABA (PODC'21 and FC'22), and is used as fallback chain in Abraxas* (CCS'23). It has a claimed $9.5\delta$ expected latency when used for a single shot instance, a.k.a. an MVBA. We exhibit attacks breaking it. Hence, the title of the fastest asynchronous MVBA with quadratic messages complexity goes to sMVBA (CCS'22), with...

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...

Fully homomorphic encryption (FHE) enables arbitrary computation on encrypted data, but certain applications remain prohibitively expensive in the encrypted domain. As a case in point, comparing two encrypted sets of data is extremely computationally expensive due to the large number of comparison operators required. In this work, we propose a novel methodology for encrypted set similarity inspired by the MinHash algorithm and the CGGI FHE scheme. Doing comparisons in FHE requires...

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...

As the field of genomics continues to expand and more sequencing data is gathered, genome analysis becomes increasingly relevant for many users. For example, a common scenario entails users trying to determine if their DNA samples are similar to DNA sequences hosted in a larger remote repository. Nevertheless, end users may be reluctant to upload their DNA sequences, while the owners of remote genomics repositories are unwilling to openly share their database. To address this challenge, we...

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...

Cross-chain Decentralized Applications (dApps) are increasingly popular for their ability to handle complex tasks across various blockchains, extending beyond simple asset transfers or swaps. However, ensuring all dependent transactions execute correctly together, known as complete atomicity, remains a challenge. Existing works provide financial atomicity, protecting against monetary loss, but lack the ability to ensure correctness for complex tasks. In this paper, we introduce Avalon, a...

The Internet has plenty of images that are transformations (e.g., resize, blur) of confidential original images. Several scenarios (e.g., selling images over the Internet, fighting disinformation, detecting deep fakes) would highly benefit from systems allowing to verify that an image is the result of a transformation applied to a confidential authentic image. In this paper, we focus on systems for proving and verifying the correctness of transformations of authentic images guaranteeing: 1)...

Waku is a privacy-preserving, generalized, and decentralized messaging protocol suite. Waku uses GossipSub for message routing and Rate Limiting Nullifiers (RLN) for spam protection. GossipSub ensures fast and reliable peer-to-peer message delivery in a permissionless environment, while RLN enforces a common publishing rate limit using zero-knowledge proofs. This paper presents a practical evaluation of message propagation latency in Waku. First, we estimate latencies analytically,...

Remote attestation (RA) protocols have been widely used to evaluate the integrity of software on remote devices. Currently, the state-of-the-art RA protocols lack a crucial feature: transparency. This means that the details of the final attestation verification are not openly accessible or verifiable by the public. Furthermore, the interactivity of these protocols often limits attestation to trusted parties who possess privileged access to confidential device data, such as pre-shared...

Verifying image provenance has become an important topic, especially in the realm of news media. To address this issue, the Coalition for Content Provenance and Authenticity (C2PA) developed a standard to verify image provenance that relies on digital signatures produced by cameras. However, photos are usually edited before being published, and a signature on an original photo cannot be verified given only the published edited image. In this work, we describe VerITAS, a system that uses...

With the rise of generative AI technology, the media's credibility as a source of truth has been significantly compromised. This highlights the need to verify the authenticity of media and its originality. Ensuring the integrity of media during capture using the device itself presents a straightforward solution to this challenge. However, raw captured media often require certain refinements or redactions before publication. Zero-knowledge proofs (ZKP) offer a solution by allowing...

For primes $p$ with $p+1$ being smooth, the G-FFT from Li and Xing [LX23] is an algebraic FFT, which at first glance seems equivalent to the circle FFT from [IACR eprint 2024/278]: It also uses the circle curve over $\mathbb F_p$ (in other words the projective line) as underlying domain, and interpolates by low-degree functions with poles over the same set of points. However, their approach to control the degree of the FFT basis is fundamentally different. The G-FFT makes use of punctured...

Enhancing privacy in federal learning (FL) without considering robustness can create an open door for attacks such as poisoning attacks on the FL process. Thus, addressing both the privacy and security aspects simultaneously becomes vital. Although, there are a few solutions addressing both privacy and security in the literature in recent years, they have some drawbacks such as requiring two non-colluding servers, heavy cryptographic operations, or peer-to-peer communication topology. In...

Mathematically secured cryptographic implementations leak critical information in terms of power, EM emanations, etc. Several circuit-level countermeasures are proposed to hinder side channel leakage at the source. Circuit-level countermeasures (e.g., IVR, STELLAR, WDDL, etc) are often preferred as they are generic and have low overhead. They either dither the voltage randomly or attenuate the meaningful signature at $V_{DD}$ port. Although any digital implementation has two generic ports,...

Fully homomorphic encryption (FHE) enables secure data processing without compromising data access, but its computational cost and slower execution compared to plaintext operations pose challenges. The growing interest in FHE-based secure computation necessitates the acceleration of homomorphic computations. While existing research primarily targets the reduction of the multiplicative depth (MD) of homomorphic circuits, this paper addresses the trade-off between MD reduction and the increase...

We introduce ${zkVoting}$, a coercion-resistant e-voting system that utilizes a fake keys approach based on a novel nullifiable commitment scheme. This scheme allows voters to receive both real and fake commitment keys from a registrar. Each ballot includes this commitment, but only the tallier can efficiently discern the fake ballots, simplifying the tally process to $\mathcal{O}(n)$ and ensuring coercion resistance. ${zkVoting}$ also preserves voter anonymity by ensuring each ballot...

This work builds approximate proximity searchable encryption. Secure biometric databases are the primary application. Prior work (Kuzu, Islam, and Kantarcioglu, ICDE 2012) combines locality-sensitive hashes, or LSHs, (Indyk, STOC ’98), and oblivious multimaps. The multimap associates LSH outputs as keywords to biometrics as values. When the desired result set is of size at most one, we show a new preprocessing technique and system called ProxCode that inserts shares of a linear secret...

The realm of digital health is experiencing a global surge, with mobile applications extending their reach into various facets of daily life. From tracking daily eating habits and vital functions to monitoring sleep patterns and even the menstrual cycle, these apps have become ubiquitous in their pursuit of comprehensive health insights. Many of these apps collect sensitive data and promise users to protect their privacy - often through pseudonymization. We analyze the real anonymity that...

In recent years, SNARKs have shown great promise as a tool for building trustless bridges to connect the heterogeneous ecosystem of blockchains. Unfortunately, the parameters hardwired for many of the widely used blockchains are incongruous with the conventional SNARKs, which results in unsatisfactory performance. This bottleneck necessitates new proof systems tailored for efficiency in these environments. The primary focus of this paper is on succinct bridges from Cosmos to...

Users increasingly store their data in the cloud, thereby benefiting from easy access, sharing, and redundancy. To additionally guarantee security of the outsourced data even against a server compromise, some service providers have started to offer end-to-end encrypted (E2EE) cloud storage. With this cryptographic protection, only legitimate owners can read or modify the data. However, recent attacks on the largest E2EE providers have highlighted the lack of solid foundations for this...

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...

Contemporary methods for hardware security verification struggle with adaptability, scalability, and availability due to the increasing complexity of the modern system-on-chips (SoCs). Large language models (LLMs) have emerged as a viable approach to address these shortcomings in security verification because of their natural language understanding, advanced reasoning, and knowledge transfer capabilities. However, their application to large designs is limited by inherent token limitation and...

This Systematization of Knowledge conducts a survey of contemporary distributed blockchain protocols, with the aim of identifying cryptographic and design techniques which practically enable both expressive programmability and user data confidentiality. To facilitate a framing which supports the comparison of concretely very different protocols, we define an epoch-based computational model in the form of a flexible UC-style ideal functionality which divides the operation of...

This paper presents a new architecture for metadata-private messaging that counters scalability challenges by offloading most computations to the clients. At the core of our design is a distributed private information retrieval (PIR) protocol, where the responder delegates its work to alleviate PIR's computational bottleneck and catches misbehaving delegates by efficiently verifying their results. We introduce DPIR, a messaging system that uses distributed PIR to let a server storing...

Polynomials play a central role in cryptography. In the context of Zero Knowledge Proofs (ZKPs), protocols can be exclusively expressed using polynomials, making them a powerful abstraction tool, as demonstrated in most ZK research papers. Our first contribution is a high-level framework that enables practitioners to implement ZKPs in a more natural way, based solely on polynomial primitives. ZK provers are considered computationally intensive algorithms with a high degree of...

ElectionGuard is a flexible set of open-source tools that---when used with traditional election systems---can produce end-to-end verifiable elections whose integrity can be verified by observers, candidates, media, and even voters themselves. ElectionGuard has been integrated into a variety of systems and used in actual public U.S. elections in Wisconsin, California, Idaho, Utah, and Maryland as well as in caucus elections in the U.S. Congress. It has also been used for civic voting in the...

A contingent payment protocol involves two mutually distrustful parties, a buyer and a seller, operating on the same blockchain, and a digital product, whose ownership is not tracked on a blockchain (e.g. a digital book, but not a NFT). The buyer holds coins on the blockchain and transfers them to the seller in exchange for the product. However, if the blockchain does not hide transaction details, any observer can learn that a buyer purchased some product from a seller. In this work, we...

Dynamic searchable encryption (DSE) with forward and backward privacy reduces leakages in early-stage schemes. Security enhancement comes with a price -- maintaining updatable keyword-wise state information. State information, if stored locally, incurs significant client-side storage overhead for keyword-rich datasets, potentially hindering real-world deployments. We propose DISCO, a simple and efficient framework for designing DSE schemes using constant client state. DISCO combines...

This work presents the first provably secure protocol for Butterfly Key Expansion (BKE) -- a tripartite protocol for provisioning users with pseudonymous certificates -- based on post-quantum cryptographic schemes. Our work builds upon the CRYSTALS family of post-quantum algorithms that have been selected for standardization by NIST. We extend those schemes by imbuing them with the additional functionality of public key expansion: a process by which pseudonymous public keys can be derived by...

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...

The trading of data is becoming increasingly important as it holds substantial value. A blockchain-based data marketplace can provide a secure and transparent platform for data exchange. To facilitate this, developing a fair data exchange protocol for digital goods has garnered considerable attention in recent decades. The Zero Knowledge Contingent Payment (ZKCP) protocol enables trustless fair exchanges with the aid of blockchain and zero-knowledge proofs. However, applying this protocol in...

This paper introduces and develops the concept of ``ticketing'', through which atomic broadcasts are orchestrated by nodes in a distributed system. The paper studies different ticketing regimes that allow parallelism, yet prevent slow nodes from hampering overall progress. It introduces a hybrid scheme which combines managed and unmanaged ticketing regimes, striking a balance between adaptivity and resilience. The performance evaluation demonstrates how managed and unmanaged ticketing...

For more than forty years, two principal questions have been asked when designing verifiable election systems: how will the integrity of the results be demonstrated and how will the privacy of votes be preserved? Many approaches have been taken towards answering the first question such as use of MixNets and homomorphic tallying. But in the academic literature, the second question has always been answered in the same way: decryption capabilities are divided amongst multiple independent...

Motivated by the problem of detecting AI-generated text, we consider the problem of watermarking the output of language models with provable guarantees. We aim for watermarks which satisfy: (a) undetectability, a cryptographic notion introduced by Christ, Gunn & Zamir (2024) which stipulates that it is computationally hard to distinguish watermarked language model outputs from the model's actual output distribution; and (b) robustness to channels which introduce a constant fraction of...

Recent years have exhibited an increase in applications that distribute trust across $n$ servers to protect user data from a central point of attack. However, these deployments remain limited due to a core obstacle: establishing $n$ distinct trust domains. An application provider, a single trust domain, cannot directly deploy multiple trust domains. As a result, application providers forge business relationships to enlist third-parties as trust domains, which is a manual, lengthy, and...

In the field of Artificial Intelligence (AI), convolution operations have primarily been used in Convolutional Neural Networks (CNNs). However, its utility is increasing with the appearance of convolution integrated transformers or state space models where convolution is a constituent element. In the field of private AI, generalized algorithm, multiplexed parallel convolution was recently proposed to implement CNNs based on the Homomorphic Encryption scheme, residue number system variant...

This paper explores the problem of preventing length leakage in oblivious data access mechanisms with passive persistent adversaries. We show that designing mechanisms that prevent both length leakage and access pattern leakage requires navigating a three-way tradeoff between storage footprint, bandwidth footprint, and the information leaked to the adversary. We establish powerful lower bounds on achievable storage and bandwidth footprints for a variety of leakage profiles, and present...

In a federated learning (FL) system, decentralized data owners (clients) could upload their locally trained models to a central server, to jointly train a global model. Malicious clients may plant backdoors into the global model through uploading poisoned local models, causing misclassification to a target class when encountering attacker-defined triggers. Existing backdoor defenses show inconsistent performance under different system and adversarial settings, especially when the malicious...

The rise of Generative AI (GenAI) brings about transformative potential across sectors, but its dual-use nature also amplifies risks. Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety. China, the United States (US), and the European Union (EU) are at the forefront with initiatives like the Management of Algorithmic Recommendations, the Executive Order, and the AI Act, respectively. However, the rapid evolution of GenAI capabilities...

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...

The increasing importance of graph databases and cloud storage services prompts the study of private queries on graphs. We propose PathGES, a graph encryption scheme (GES) for single-pair shortest path queries. PathGES is efficient and mitigates the state-of-the-art attack by Falzon and Paterson (2022) on the GES by Ghosh, Kamara, and Tamassia (2021), while only incurring an additional logarithmic factor in storage overhead. PathGES leverages a novel data structure that minimizes leakage and...

Extensible Markup Language (XML) is one of the most popular serialization languages. Since many security protocols are built using XML, it also provides cryptographic functionality. A central framework in this area is the Security Assertion Markup Language (SAML). This standard is one of the most widely used options for implementing Single Sign-On (SSO), which allows users to authenticate to different service providers using the credentials from a single identity provider. Like all other...

Arma is a Byzantine Fault Tolerant (BFT) consensus system designed to achieve horizontal scalability across all hardware resources: network bandwidth, CPU, and disk I/O. As opposed to preceding BFT protocols, Arma separates the dissemination and validation of client transactions from the consensus process, restricting the latter to totally ordering only metadata of batches of transactions. This separation enables each party to distribute compute and storage resources for transaction...

We present a novel protocol for issuing and transferring tokens across blockchains without the need of a trusted third party or cross-chain bridge. In our scheme, the blockchain is used for double-spend protection only, while the authorisation of token transfers is performed off-chain. Due to the universality of our approach, it works in almost all blockchain settings. It can be implemented immediately on UTXO blockchains such as Bitcoin without modification, and on account-based blockchains...

We study watermarking schemes for language models with provable guarantees. As we show, prior works offer no robustness guarantees against adaptive prompting: when a user queries a language model more than once, as even benign users do. And with just a single exception (Christ and Gunn, 2024), prior works are restricted to zero-bit watermarking: machine-generated text can be detected as such, but no additional information can be extracted from the watermark. Unfortunately, merely detecting...

We propose Challenger a peer-to-peer blockchain-based middleware architecture for narrative games, and discuss its resilience to cheating attacks. Our architecture orchestrates nine services in a fully decentralized manner where nodes are not aware of the entire composition of the system nor its size. All these components are orchestrated together to obtain (strong) resilience to cheaters. The main contribution of the paper is to provide, for the first time, an architecture for narrative...

Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. In this work, we revisit the problem of secure aggregation in the single-server setting where a single evaluation server can securely aggregate client-held individual inputs. Our key contribution is One-shot Private Aggregation ($\mathsf{OPA}$) where clients speak only once (or even choose not to speak) per...

Provable privacy typically requires involved analysis and is often associated with unacceptable accuracy loss. While many empirical verification or approximation methods, such as Membership Inference Attacks (MIA) and Differential Privacy Auditing (DPA), have been proposed, these do not offer rigorous privacy guarantees. In this paper, we apply recently-proposed Probably Approximately Correct (PAC) Privacy to give formal, mechanized, simulation-based proofs for a range of practical,...

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...

We study selfish mining attacks in longest-chain blockchains like Bitcoin, but where the proof of work is replaced with efficient proof systems -- like proofs of stake or proofs of space -- and consider the problem of computing an optimal selfish mining attack which maximizes expected relative revenue of the adversary, thus minimizing the chain quality. To this end, we propose a novel selfish mining attack that aims to maximize this objective and formally model the attack as a Markov...

In recent years, cloud vendors have started to supply paid services for data analysis by providing interfaces of their well-trained neural network models. However, customers lack tools to verify whether outcomes supplied by cloud vendors are correct inferences from particular models, in the face of lazy or malicious vendors. The cryptographic primitive called zero-knowledge proof (ZKP) addresses this problem. It enables the outcomes to be verifiable without leaking information about the...

The TFHE cryptosystem only supports small plaintext space, up to 5 bits with usual parameters. However, one solution to circumvent this limitation is to decompose input messages into a basis B over multiple ciphertexts. In this work, we introduce B-gates, an extension of logic gates to non binary bases, to compute base B logic circuit. The flexibility introduced by our approach improves the speed performance over previous approaches such as the so called tree-based method which requires an...

Vertical Federated Learning (VFL) is becoming a standard collaborative learning paradigm with various practical applications. Randomness is essential to enhancing privacy in VFL, but introducing too much external randomness often leads to an intolerable performance loss. Instead, as it was demonstrated for other federated learning settings, leveraging internal randomness —as provided by variational autoencoders (VAEs) —can be beneficial. However, the resulting privacy has never been...

This paper studies the optimal transaction fee mechanisms for blockchains, focusing on the distinction between price-based ($\mathcal{P}$) and quantity-based ($\mathcal{Q}$) controls. By analyzing factors such as demand uncertainty, validator costs, cryptocurrency price fluctuations, price elasticity of demand, and levels of decentralization, we establish criteria that determine the selection of transaction fee mechanisms. We present a model framed around a Nash bargaining game, exploring...

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...

Topic modelling refers to a popular set of techniques used to discover hidden topics that occur in a collection of documents. These topics can, for example, be used to categorize documents or label text for further processing. One popular topic modelling technique is Latent Dirichlet Allocation (LDA). In topic modelling scenarios, the documents are often assumed to be in one, centralized dataset. However, sometimes documents are held by different parties, and contain privacy- or...

State-of-the-art asynchronous Byzantine Fault Tolerance (BFT) protocols integrate a partially-synchronous optimistic path. The holy grail in this paradigm is to match the performance of a partially-synchronous protocol in favorable situations and match the performance of a purely asynchronous protocol in unfavorable situations. Several prior works have made progress toward this goal by matching the efficiency of a partially-synchronous protocol in favorable conditions. However, their...

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 needing to ensure privacy and the correctness of work carried out by a...

Storage of sensitive multi-dimensional arrays must be secure and efficient in storage and processing time. Searchable encryption allows one to trade between security and efficiency. Searchable encryption design focuses on building indexes, overlooking the crucial aspect of record retrieval. Gui et al. (PoPETS 2023) showed that understanding the security and efficiency of record retrieval is critical to understand the overall system. A common technique for improving security is partitioning...

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...

This paper presents a novel blockchain-based decentralized identity system (DID), tailored for enhanced digital identity management in Internet of Things (IoT) and device-to-device (D2D) networks. The proposed system features a hierarchical structure that effectively merges a distributed ledger with a mobile D2D network, ensuring robust security while streamlining communication. Central to this design are the gateway nodes, which serve as intermediaries, facilitating DID registration and...

We construct a digital signature scheme for images that allows the image to be compressed without invalidating the signature. More specifically, given a JPEG image signed with our signature scheme, a third party can compress the image using JPEG compression, and, as long as the quantization tables only include powers of two, derive a valid signature for the compressed image, without access to the secret signing key, and without interaction with the signer. Our scheme is constructed using a...

Transformer neural networks have gained significant traction since their introduction, becoming pivotal across diverse domains. Particularly in large language models like Claude and ChatGPT, the transformer architecture has demonstrated remarkable efficacy. This paper provides a concise overview of transformer neural networks and delves into their security considerations, focusing on covert channel attacks and their implications for the safety of large language models. We present a covert...

Searchable Symmetric Encryption (SSE) has opened up an attractive avenue for privacy-preserved processing of outsourced data on the untrusted cloud infrastructure. SSE aims to support efficient Boolean query processing with optimal storage and search overhead over large real databases. However, current constructions in the literature lack the support for multi-client search and dynamic updates to the encrypted databases, which are essential requirements for the widespread deployment of SSE...

In this work-in-progress, we present a series of protocols to efficiently prove statements about strings in context-free grammars (CFGs). Our main protocol for proving proofs of correct parsing for strings in a CFG flexibly accommodates different instantiations of zero-knowledge proof systems as well as accumulation schemes. While improvements in the modular cryptographic primitives can be carried over for improvements in our protocols, even simpler proof systems, which do not support...

Fully Homomorphic Encryption (FHE) is a powerful tool that brings privacy and security to all sorts of applications by allowing us to perform additions and multiplications directly on ciphertexts without the need of the secret key. Some applications of FHE that were previously overlooked but have recently been gaining traction are data compression and image processing. Practically, FHE enables applications such as private satellite searching, private object recognition, or even encrypted...

Analyzing user data while protecting the privacy of individuals remains a big challenge. Trusted execution environments (TEEs) are a possible solution as they protect processes and Virtual Machines (VMs) against malicious hosts. However, TEEs can leak access patterns to code and to the data being processed. Furthermore, when data is stored in a TEE database, the data volume required to answer a query is another unwanted side channel that contains sensitive information. Both types of...

This paper presents an in-depth exploration of the development and deployment of a Layer 1 (L1) blockchain designed to underpin metaverse experiences. As the digital and physical realms become increasingly intertwined, the metaverse emerges as a frontier for innovation, demanding robust, scalable, and secure infrastructure. The core of our investigation centers around the challenges and insights gained from constructing a blockchain framework capable of supporting the vast, dynamic...

We study the possibility of algorithm substitution attacks (ASAs) on functions with no secret-key material, such as hash functions, and verification algorithms of signature schemes and proof systems. We consider big-brother's goal to be three-fold: It desires utility (it can break the scheme), exclusivity (nobody else can) and undetectability (outsiders can't detect its presence). We start with a general setting in which big-brother is aiming to subvert an arbitrary public function. We give,...

We reflect on our experiences analysing cryptography deployed “in the wild” and give recommendations to fellow researchers about this process.

Searchable symmetric encryption (SSE) schemes provide users with the ability to perform keyword searches on encrypted databases without the need for decryption. While this functionality is advantageous, it introduces the potential for inadvertent information disclosure, thereby exposing SSE systems to various types of attacks. In this work, we introduce a new inference attack aimed at enhancing the query recovery accuracy of RefScore (presented at USENIX 2021). The proposed approach...

Searchable symmetric encryption has been vulnerable to inference attacks that rely on uniqueness in leakage patterns. However, many keywords in datasets lack distinctive leakage patterns, limiting the effectiveness of such attacks. The file injection attacks, initially proposed by Cash et al. (CCS 2015), have shown impressive performance with 100% accuracy and no prior knowledge requirement. Nevertheless, this attack fails to recover queries with underlying keywords not present in the...

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...

In [1], two generic constructions for biometric-based non-transferable Attribute Based Credentials (biometric ABC) are presented, which offer different trade-offs between efficiency and trust assumptions. In this paper, we focus on the second scheme denoted as BioABC-ZK that tries to remove the strong (and unrealistic) trust assumption on the Reader R, and show that BioABC-ZK has a security flaw for a colluding R and Verifier V. Besides, BioABC-ZK lacks GDPR-compliance, which requires secure...

In the common random string model, the parties executing a protocol have access to a uniformly random bit string. It is known that under standard intractability assumptions, we can realize any ideal functionality with universally composable (UC) security if a trusted common random string (CrS) setup is available. It was always a question of where this CrS should come from since the parties provably could not compute it themselves. Trust assumptions are required, so minimizing the level of...

Random number generators (RNGs) are notoriously hard to build and test, especially in a cryptographic setting. Although one cannot conclusively determine the quality of an RNG by testing the statistical properties of its output alone, running numerical tests is both a powerful verification tool and the only universally applicable method. In this work, we present and make available a comprehensive statistical testing environment (STE) that is based on existing statistical test suites. The STE...

Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. Notably, recent studies are increasingly exploring LLMs applied to the context of blockchain security (BS). However, there remains a gap in a comprehensive understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we undertake a literature review focusing on the studies that apply LLMs in blockchain...

In federated learning, secure aggregation (SA) protocols like Flamingo (S\&P'23) and LERNA (ASIACRYPT'23) have achieved efficient multi-round SA in the malicious model. However, each round of their aggregation requires at least three client-server round-trip communications and lacks support for aggregation result verification. Verifiable SA schemes, such as VerSA (TDSC'21) and Eltaras et al.(TIFS'23), provide verifiable aggregation results under the security assumption that the server does...

To resist the regimes of ubiquitous surveillance imposed upon us in every facet of modern life, we need technological tools that subvert surveillance systems. Unfortunately, while cryptographic tools frequently demonstrate how we can construct systems that safeguard user privacy, there is limited motivation for corporate entities engaged in surveillance to adopt these tools, as they often clash with profit incentives. This paper demonstrates how, in one particular aspect of everyday life --...

Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rates. This reduces costs for the bank, as the list aggregates the bank's internal firm inventory per asset for all clients of long as well as short trades. However, this is somewhat problematic: (1) the bank's inventory is revealed; (2) trades of clients who contribute to the aggregated list, particularly those deemed...

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...

Private messaging platforms provide strong protection against platform eavesdropping, but malicious users can use privacy as cover for spreading abuse and misinformation. In an attempt to identify the sources of misinformation on private platforms, researchers have proposed mechanisms to trace back the source of a user-reported message (CCS '19,'21). Unfortunately, the threat model considered by initial proposals allowed a single user to compromise the privacy of another user whose...

Transport Layer Security ( TLS ) is foundational for safeguarding client-server communication. However, it does not extend integrity guarantees to third-party verification of data authenticity. If a client wants to present data obtained from a server, it cannot convince any other party that the data has not been tampered with. TLS oracles ensure data authenticity beyond the client-server TLS connection, such that clients can obtain data from a server and ensure provenance to any third...

Hardening microprocessors against side-channel attacks is a critical aspect of ensuring their security. A key step in this process is identifying and mitigating “leaky" hardware modules, which leak information during the execution of cryptographic algorithms. In this paper, we explore how different leakage detection methods, the Side-channel Vulnerability Factor (SVF) and the Test Vector Leakage Assessment (TVLA), contribute to hardening of microprocessors. We conduct experiments on two...

A ranking function for permutations maps every permutation of length $n$ to a unique integer between $0$ and $n!-1$. For permutations of size that are of interest in cryptographic applications, evaluating such a function requires multiple-precision arithmetic. This work introduces a quasi-optimal ranking technique that allows us to rank a permutation efficiently without needing a multiple-precision arithmetic library. We present experiments that show the computational advantage of our method...

Imagine being able to deploy a small, battery-powered device nearly anywhere on earth that humans frequent and having it be able to send data to the cloud without needing to provision a network—without buying a physical gateway, setting up WiFi credentials, or acquiring a cellular SIM. Such a capability would address one of the greatest bottlenecks to deploying the long-tail of small, embedded, and power-constrained IoT devices in nearly any setting. Unfortunately, decoupling the device...

The Bitcoin ecosystem has continued to evolve beyond its initial promises of decentralization, transparency, and security. Recent advancements have notably been made with the integration of Layer-2 solutions, which address scalability issues by offloading transactions from the main blockchain. This facilitates faster and more cost-effective transactions while maintaining integrity. The advent of inscriptions and ordinal protocols has further broadened the spectrum of capabilities, enabling...

Byzantine Agreement (BA) enables $n$ processes to reach consensus on a common valid $L_o$-bit value, even in the presence of up to $t<n$ faulty processes that can deviate arbitrarily from their prescribed protocol. Despite its significance, the optimal communication complexity for key variations of BA has not been determined within the honest majority regime ($n=2t+1$), for both the worst-case scenario and the adaptive scenario, which accounts for the actual number $f \leq t$ of failures....

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...