Traceable ring signatures enhance ring signatures by adding an accountability layer. Specifically, if a party signs two different messages within the protocol, their identity is revealed. Another desirable feature is $\textit{extendability}$. In particular, $\textit{extendable threshold}$ ring signatures (ETRS) allow to $\textit{non-interactively}$ update already finalized signatures by enlarging the ring or the set of signers. Combining traceability and extendability in a single scheme...

Data markets play a pivotal role in modern industries by facilitating the exchange of data for predictive modeling, targeted marketing, and research. However, as data becomes a valuable commodity, privacy and security concerns have grown, particularly regarding the personal information of individuals. This tutorial explores privacy and security issues when integrating different data sources in data market platforms. As motivation for the importance of enforcing privacy requirements, we...

The large key size for fully homomorphic encryption (FHE) requires substantial costs to generate and transmit the keys. This has been problematic for FHE clients who want to delegate the computation, as they often have limited power. A recent work, Lee-Lee-Kim-No [Asiacrypt 2023], partly solved this problem by suggesting a hierarchical key management system. However, the overall key size was still several gigabytes for real-world applications, and it is barely satisfactory for mobile phones...

Fully Homomorphic Encryption (FHE) presents unique challenges in programming due to the contrast between traditional and FHE language paradigms. A key challenge is selecting ciphertext configurations (CCs) to achieve the desired level of security, performance, and accuracy simultaneously. Finding the design point satisfying the goal is often labor-intensive (probably impossible), for which reason previous works settle down to a reasonable CC that brings acceptable performance. When FHE is...

Plaintext-ciphertext matrix multiplication (PC-MM) is an indispensable tool in privacy-preserving computations such as secure machine learning and encrypted signal processing. While there are many established algorithms for plaintext-plaintext matrix multiplication, efficiently computing plaintext-ciphertext (and ciphertext-ciphertext) matrix multiplication is an active area of research which has received a lot of attention. Recent literature have explored various techniques for...

Federated Learning (FL) allows clients to engage in learning without revealing their raw data. However, traditional FL focuses on developing a single global model for all clients, limiting their ability to have personalized models tailored to their specific needs. Personalized FL (PFL) enables clients to obtain their customized models, either with or without a central party. Current PFL research includes mechanisms to detect poisoning attacks, in which a couple of malicious nodes try to...

Searchable encryption (SE) enables privacy-preserving keyword search on encrypted data. Public-key SE (PKSE) supports multi-user searches but suffers from high search latency due to expensive public-key operations. Symmetric SE (SSE) offers a sublinear search but is mainly limited to single-user settings. Recently, hybrid SE (HSE) has combined SSE and PKSE to achieve the best of both worlds, including multi-writer encrypted search functionalities, forward privacy, and sublinear search with...

The major Fully Homomorphic Encryption (FHE) schemes guarantee the privacy of the encrypted message only in the honest-but-curious setting, when the server follows the protocol without deviating. However, various attacks in the literature show that an actively malicious server can recover sensitive information by executing incorrect functions, tampering with ciphertexts, or observing the client’s reaction during decryption. Existing integrity solutions for FHE schemes either fail to...

In an anonymous credential system, users collect credentials from issuers, and can use their credentials to generate privacy-preserving identity proofs that can be shown to third-party verifiers. Since the introduction of anonymous credentials by Chaum in 1985, there has been promising advances with respect to system design, security analysis and real-world implementations of anonymous credential systems. In this paper, we examine Hyperledger AnonCreds, an anonymous credential system that...

An interactive aggregate signature scheme allows $n$ signers, each with their own secret/public key pair $(sk_i, pk_i)$ and message $m_i$, to jointly produce a short signature that simultaneously witnesses that $m_i$ has been signed under $pk_i$ for every $i \in \{1, \dots, n\}$. Despite the large potential for savings in terms of space and verification time, which constitute the two main bottlenecks for large blockchain systems such as Bitcoin, aggregate signatures have received much less...

The securities of a large fraction of zero-knowledge arguments of knowledge schemes rely on the discrete logarithm (DL) assumption or the discrete logarithm relation assumption, such as Bulletproofs (S&P 18) and compressed $\Sigma$-protocol (CRYPTO 20). At the heart of these protocols is an interactive proof of knowledge between a prover and a verifier showing that a Pedersen vector commitment $P=h^{\rho}\cdot\textbf{g}^{\textbf{x}}$ to a vector $\textbf{x}$ satisfies multi-variate...

As billions of people rely on end-to-end encrypted messaging, the exposure of metadata, such as communication timing and participant relationships, continues to deanonymize users. Asynchronous metadata-hiding solutions with strong cryptographic guarantees have historically been bottlenecked by quadratic $O(N^2)$ server computation in the number of users $N$ due to reliance on private information retrieval (PIR). We present Myco, a metadata-private messaging system that preserves strong...

Private information retrieval (PIR) allows a client to query a public database privately and serves as a key building block for privacy-enhancing applications. Minimizing query size is particularly important in many use cases, for example when clients operate on low-power or bandwidth-constrained devices. However, existing PIR protocols exhibit large query sizes: to query $2^{25}$ records, the smallest query size of 14.8KB is reported in Respire [Burton et al., CCS'24]. Respire is based on...

In this paper, we present Trilithium: a protocol for distributed key generation and signing compliant with FIPS 204 (ML-DSA). Our protocol allows two parties, "server" and "phone" with assistance of correlated randomness provider (CRP) to produce a standard ML-DSA signature. We prove our protocol to be secure against a malicious server or phone in the universal composability (UC) model, introducing some novel techniques to argue the security of two-party secure computation protocols with...

Fully Homomorphic Encryption (FHE) enables computation on encrypted data without decryption, demonstrating significant potential for privacy-preserving applications. However, FHE faces several challenges, one of which is the significant plaintext-to-ciphertext expansion ratio, resulting in high communication overhead between client and server. The transciphering technique can effectively address this problem by first encrypting data with a space-efficient symmetric cipher, then converting...

Due to their widespread applications in decentralized and privacy preserving technologies, commitment schemes have become increasingly important cryptographic primitives. With a wide variety of applications, many new constructions have been proposed, each enjoying different features and security guarantees. In this paper, we systematize the designs, features, properties, and applications of vector commitments (VCs). We define vector, polynomial, and functional commitments and we discuss the...

A proof of reserves (PoR) protocol enables a cryptocurrency exchange to prove to its users that it owns a certain amount of coins, as a first step towards proving that it is solvent. We present the design, implementation, and security analysis of MProve-Nova, a PoR protocol for Monero that leverages the Nova recursive SNARK to achieve two firsts (without requiring any trusted setup). It is the first Monero PoR protocol that reveals only the number of outputs owned by an exchange; no other...

Abstract—Anonymous token schemes are cryptographic protocols for limiting the access to online resources to credible users. The resource provider issues a set of access tokens to the credible user that they can later redeem anonymously, i.e., without the provider being able to link their redemptions. When combined with credibility tests such as CAPTCHAs, anonymous token schemes can significantly increase user experience and provider security, without exposing user access patterns to...

Mixnets are powerful building blocks for providing anonymity in applications like electronic voting and anonymous messaging. The en- cryption schemes upon which traditional mixnets are built, as well as the zero-knowledge proofs used to provide verifiability, will, however, soon become insecure once a cryptographically-relevant quantum computer is built. In this work, we construct the most compact verifiable mixnet that achieves privacy and verifiability through encryption and...

Proxy re-encryption (PRE) schemes enable a semi-honest proxy to transform a ciphertext of one user $i$ to another user $j$ while preserving the privacy of the underlying message. Multi-hop PRE schemes allow a legal ciphertext to undergo multiple transformations, but for lattice-based multi-hop PREs, the number of transformations is typically bounded due to the increase of error terms. Recently, Zhao et al. (Esorics 2024) introduced a lattice-based unbounded multi-hop (homomorphic) PRE scheme...

The increased popularity of large language models (LLMs) raises serious privacy concerns, where users' private queries are sent to untrusted servers. Many cryptographic techniques have been proposed to provide privacy, such as secure multiparty computation (MPC), which enables the evaluation of LLMs directly on private data. However, cryptographic techniques have been deemed impractical as they introduce large communication and computation. On the other hand, many obfuscation techniques have...

Electronic voting schemes typically ensure ballot privacy by assuming that the decryption key is distributed among tallying authorities, preventing any single authority from decrypting a voter’s ballot. However, this assumption may fail in a fully dishonest environment where all tallying authorities collude to break ballot privacy. In this work, we introduce the notion of anamorphic voting, which enables voters to convey their true voting intention to an auditor while casting an...

The online realm has witnessed a surge in the buying and selling of data, prompting the emergence of dedicated data marketplaces. These platforms cater to servers (sellers), enabling them to set prices for access to their data, and clients (buyers), who can subsequently purchase these data, thereby streamlining and facilitating such transactions. However, the current data market is primarily confronted with the following issues. Firstly, they fail to protect client privacy, presupposing that...

Group signatures allow a user to sign anonymously on behalf of a group of users while allowing a tracing authority to trace the signer's identity in case of misuse. In Chaum and van Heyst's original model (EUROCRYPT'91), the group needs to stay fixed. Throughout various attempts, including partially dynamic group signatures and revocations, Bootle et al. (ACNS'16, J. Cryptol.) formalized the notion of fully dynamic group signatures (FDGS), enabling both enrolling and revoking users of the...

(Preprint) Zero-Knowledge Proofs (ZKPs) are rapidly gaining importance in privacy-preserving and verifiable computing. ZKPs enable a proving party to prove the truth of a statement to a verifying party without revealing anything else. ZKPs have applications in blockchain technologies, verifiable machine learning, and electronic voting, but have yet to see widespread adoption due to the computational complexity of the proving process.Recent works have accelerated the key primitives of...

eIDAS 2.0 (electronic IDentification, Authentication and trust Services) is a very ambitious regulation aimed at equipping European citizens with a personal digital identity wallet (EU Digital Identity Wallet) on a mobile phone that not only needs to achieve a high level of security, but also needs to be available as soon as possible for a large number of citizens and respect their privacy (as per GDPR - General Data Protection Regulation). In this paper, we introduce the foundations of...

Modern life makes having a digital identity no longer optional, whether one needs to manage a bank account or subscribe to a newspaper. As the number of online services increases, it is fundamental to safeguard user privacy and equip service providers (SP) with mechanisms enforcing Sybil resistance, i.e., preventing a single entity from showing as many. Current approaches, such as anonymous credentials and self-sovereign identities, typically rely on identity providers or identity...

Secure two-party computation (2PC) enables two parties to jointly evaluate a function while maintaining input privacy. Despite recent significant progress, a notable efficiency gap remains between actively secure and passively secure protocols. In S\&P'12, Huang, Katz, and Evans formalized the notion of \emph{active security with one-bit leakage}, providing a promising approach to bridging this gap. Protocols derived from this notion have become foundational in designing highly efficient...

Semantic communication systems, which focus on transmitting the semantics of data rather than its exact reconstruction, redefine the design of communication networks for transformative efficiency in bandwidth-limited and latency-critical applications. Addressing these goals, we tackle the rate-distortion-perception (RDP) problem for image compression, a critical challenge in achieving perceptually realistic reconstructions under rate constraints. Formulated within the randomized distributed...

CRLite is a low-bandwidth, low-latency, privacy-preserving mechanism for distributing certificate revocation data. A CRLite aggregator periodically encodes revocation data into a compact static hash set, or membership test, which can can be downloaded by clients and queried privately. We present a novel data-structure for membership tests, which we call a clubcard, and we evaluate the encoding efficiency of clubcards using data from Mozilla's CRLite infrastructure. As of November 2024,...

More and more people take advantage of mobile apps to strike up relationships and casual contacts. This sometimes results in the sharing of self-generated nudes. While this opens a way for sexual exploration, it also raises concerns. In this paper, we review existing technology-assisted permissive proposals/features that provide security, privacy or accountability benefits when sharing nudes online. To do so, we performed a systematic literature review combing through 10,026 search results...

The modern internet relies heavily on centralized trust systems controlled by corporations, governments, and intermediaries to manage authentication, identity, and value transfer. These models introduce fundamental vulnerabilities, including censorship, fraud, and systemic insecurity. The Decentralized State Machine (DSM) addresses these issues by introducing a mathematically enforced trust layer that eliminates the need for consensus mechanisms, third-party validators, and centralized...

Homomorphic Encryption (HE) is a promising primitive for evaluating arbitrary circuits while keeping the user's privacy. We investigate how to use HE in the multi-party setting where data is encrypted with several distinct keys. One may use the Multi-Key Homomorphic Encryption (MKHE) in this setting, but it has space/computation overhead of $\mathcal O(n)$ for the number of users $n$, which makes it impractical when $n$ grows large. On the contrary, Multi-Party Homomorphic Encryption (MPHE)...

Know Your Customer (KYC) is a core component of the Anti-Money Laundering (AML) framework, designed to prevent illicit activities within financial systems. However, enforcing KYC and AML on blockchains remains challenging due to difficulties in establishing accountability and preserving user privacy. This study proposes REGKYC, a privacy-preserving Attribute-Based Access Control (ABAC) framework that balances user privacy with externally mandated KYC and AML requirements. REGKYC leverages a...

Federated Learning (FL) has become a crucial framework for collaboratively training Machine Learning (ML) models while ensuring data privacy. Traditional synchronous FL approaches, however, suffer from delays caused by slower clients (called stragglers), which hinder the overall training process. Specifically, in a synchronous setting, model aggregation happens once all the intended clients have submitted their local updates to the server. To address these inefficiencies, Buffered...

Zero-knowledge proofs (ZKPs) are cryptographic protocols that enable a prover to convince a verifier of a statement's truth without revealing any details beyond its validity. Typically, the statement is encoded as an arithmetic circuit, and allows the prover to demonstrate that the circuit evaluates to true without revealing its inputs. Despite their potential to enhance privacy and security, ZKPs are difficult to write and optimize, limiting their adoption in machine learning and data...

Digital identity wallets allow citizens to prove who they are and manage digital documents, called credentials, such as mobile driving licenses or passports. As with physical documents, secure and privacy-preserving management of the credential lifecycle is crucial: a credential can change its status from issued to valid, revoked or expired. In this paper, we focus on the analysis of cryptographic accumulators as a revocation scheme for digital identity wallet credentials. We describe the...

Blockchain advancements, currency digitalization, and declining cash usage have fueled global interest in Central Bank Digital Currencies (CBDCs). The BIS states that the hybrid model, where central banks authorize intermediaries to manage distribution, is more suitable than the direct model. However, designing a CBDC for practical implementation requires careful consideration. First, the public blockchain raises privacy concerns due to transparency. While zk-SNARKs can be a solution, they...

Digital signatures underpin identity, authenticity, and trust in modern computer systems. Cryptography research has shown that it is possible to prove possession of a valid message and signature for some public key, without revealing the message or signature. These proofs of possession work only for specially-designed signature schemes. Though these proofs of possession have many useful applications to improving security, privacy, and anonymity, they are not currently usable for widely...

Blockchain technology and smart contracts have revolutionized digital transactions by enabling trustless and decentralized exchanges of value. However, the inherent transparency and immutability of blockchains pose significant privacy challenges. On-chain data, while pseudonymous, is publicly visible and permanently recorded, potentially leading to the inadvertent disclosure of sensitive information. This issue is particularly pronounced in smart contract applications, where contract details...

In this paper, we present a ring referral scheme, by which a user can publicly prove her knowledge of a valid signature for a private message that is signed by one of an ad hoc set of authorized issuers, without revealing the signing issuer. Ring referral is a natural extension to traditional ring signature by allowing a prover to obtain a signature from a third-party signer. Our scheme is useful for diverse applications, such as certificate-hiding decentralized identity, privacy-enhancing...

Solitary output secure computation allows a set of mutually distrustful parties to compute a function of their inputs such that only a designated party obtains the output. Such computations should satisfy various security properties such as correctness, privacy, independence of inputs, and even guaranteed output delivery. We are interested in full security, which captures all of these properties. Solitary output secure computation has been the study of many papers in recent years, as it...

Sigma protocols ($\Sigma$-protocols) provide a foundational paradigm for constructing secure algorithms in privacy-preserving applications. To enhance efficiency, several extended models [BG18], [BBB+18], [AC20] incorporating various optimization techniques have been proposed as ``replacements'' for the original $\Sigma$-protocol. However, these models often lack the expressiveness needed to handle complex relations and hinder designers from applying appropriate instantiation and...

Single Sign-On (SSO) is a popular authentication mechanism enabling users to access multiple web services with a single set of credentials. Despite its convenience, SSO faces outstanding privacy challenges. The Identity Provider (IdP) represents a single point of failure and can track users across different Relying Parties (RPs). Multiple colluding RPs may track users through common identity attributes. In response, anonymous credential-based SSO solutions have emerged to offer...

Federated Learning (FL) has recently emerged as one of the leading paradigms for collaborative machine learning, serving as a tool for model computation without a need to expose one’s privately stored data. However, despite its advantages, FL systems face severe challenges within its own security solutions that address both privacy and robustness of models. This paper focuses on vulnerabilities within the domain of FL security with emphasis on model-robustness. Identifying critical gaps in...

DNA edit distance (ED) measures the minimum number of single nucleotide insertions, substitutions, or deletions required to convert a DNA sequence into another. ED has broad applications in healthcare such as sequence alignment, genome assembly, functional annotation, and drug discovery. Privacy-preserving computation is essential in this context to protect sensitive genomic data. Nonetheless, the existing secure DNA edit distance solutions lack efficiency when handling large data sequences...

Hyperledger Fabric is a unique permissioned platform for implementing blockchain in a consortium. It has a distinct transaction flow of execute-order-validate. During the execution phase, a pre-determined set of endorsing peers execute a transaction and sign the transaction response. This process is termed endorsement. In the validation phase, peers validate the transaction with reference to an endorsement policy. The identity of the endorsing organizations is obtainable to all the nodes in...

The emergence of video streams as a primary medium for communication and the demand for high-quality video sharing over the internet have given rise to several security and privacy issues, such as unauthorized access and data breaches. To address these limitations, various Selective Video Encryption (SVE) schemes have been proposed, which encrypt specific portions of a video while leaving others unencrypted. The SVE approach balances security and usability, granting unauthorized users access...

We revisit the problem of secure aggregation of high-dimensional vectors in a two-server system such as Prio. These systems are typically used to aggregate vectors such as gradients in private federated learning, where the aggregate itself is protected via noise addition to ensure differential privacy. Existing approaches require communication scaling with the dimensionality, and thus limit the dimensionality of vectors one can efficiently process in this setup. We propose PREAMBLE:...

Multi-party computation (MPC) has become increasingly practical in the last two decades, solving privacy and security issues in various domains, such as healthcare, finance, and machine learning. One big caveat is that MPC sometimes lacks usability since the knowledge barrier for regular users can be high. Users have to deal with, e.g., various CLI tools, private networks, and sometimes even must install many dependencies, which are often hardware-dependent. A solution to improve the...

We show the first threshold blind signature scheme and threshold Oblivious PRF (OPRF) scheme which remain secure in the presence of an adaptive adversary, who can adaptively decide which parties to corrupt throughout the lifetime of the scheme. Moreover, our adaptively secure schemes preserve the minimal round complexity and add only a small computational overhead over prior solutions that offered security only for a much less realistic static adversary, who must choose the subset of...

Shor's and Grover's algorithms' efficiency and the advancement of quantum computers imply that the cryptography used until now to protect one's privacy is potentially vulnerable to retrospective decryption, also known as harvest now, decrypt later attack in the near future. This dissertation proposes an overview of the cryptographic schemes used by Tor, highlighting the non-quantum-resistant ones and introducing theoretical performance assessment methods of a local Tor network. The...

The rapid growth of cloud computing and data-driven applications has amplified privacy concerns, driven by the increasing demand to process sensitive data securely. Homomorphic encryption (HE) has become a vital solution for addressing these concerns by enabling computations on encrypted data without revealing its contents. This paper provides a comprehensive evaluation of two leading HE libraries, SEAL and OpenFHE, examining their performance, usability, and support for prominent HE schemes...

Chat groups in secure messaging applications such as Signal, Telegram, and Whatsapp are nowadays used for rapid and widespread dissemination of information to large groups of people. This is common even in sensitive contexts, associated with the organisation of protests, activist groups, and internal company dialogues. Manual administration of who has access to such groups quickly becomes infeasible, in the presence of hundreds or thousands of members. We construct a practical,...

In the interconnected global financial system, anti-money laundering (AML) and combating the financing of terrorism (CFT) regulations are indispensable for safeguarding financial integrity. However, while illicit transactions constitute only a small fraction of overall financial activities, traditional AML/CFT frameworks impose uniform compliance burdens on all users, resulting in inefficiencies, transaction delays, and privacy concerns. These issues stem from the institution-centric...

This paper examines the deployment of Multi-Party Computation (MPC) in corporate data processing environments, focusing on its legal and technical implications under the European Union’s General Data Protection Regulation (GDPR). By combining expertise in cryptography and legal analysis, we address critical questions necessary for assessing the suitability of MPC for real-world applications. Our legal evaluation explores the conditions under which MPC qualifies as an anonymizing approach...

Generative models have achieved remarkable success in a wide range of applications. Training such models using proprietary data from multiple parties has been studied in the realm of federated learning. Yet recent studies showed that reconstruction of authentic training data can be achieved in such settings. On the other hand, multiparty computation (MPC) guarantees standard data privacy, yet scales poorly for training generative models. In this paper, we focus on improving...

We revisit the privacy and security analyses of FIDO2, a widely deployed standard for passwordless authentication on the Web. We discuss previous works and conclude that each of them has at least one of the following limitations: (i) impractical trusted setup assumptions, (ii) security models that are inadequate in light of state of the art of practical attacks, (iii) not analyzing FIDO2 as a whole, especially for its privacy guarantees. Our work addresses these gaps and proposes...

Privacy-preserving distributed computation enables a resource-limited client to securely delegate computations on sensitive data to multiple servers by distributing shares of the data. In such systems, verifiable secret sharing (VSS) is a fundamental component, ensuring secure data distribution and directly impacting the overall performance. The most practical approach to construct VSS is through polynomial commitment (PC), with two main research directions to improve the VSS efficiency....

Oblivious permutation (OP) enables two parties, a sender with a private data vector $x$ and a receiver with a private permutation π, to securely obtain the shares of π(x). OP has been used to construct many important MPC primitives and applications such as secret shuffle, oblivious sorting, private set operations, secure database analysis, and privacy-preserving machine learning. Due to its high complexity, OP has become a performance bottleneck in several practical applications, and many...

Matrix multiplication of two encrypted matrices (CC-MM) is a key challenge for privacy-preserving machine learning applications. As modern machine learning models focus on scalability, fast CC-MM on large datasets is increasingly in demand. In this work, we present a CC-MM algorithm for large matrices. The algorithm consists of plaintext matrix multiplications (PP-MM) and ciphertext matrix transpose algorithms (C-MT). We propose a fast C-MT algorithm, which is computationally inexpensive...

In verifiable secret sharing (VSS), a dealer shares a secret input among several parties, ensuring each share is verifiable. Motivated by its applications in the blockchain space, we focus on a VSS where parties holding shares are not allowed to reconstruct the dealer's secret (even partially) on their own terms, which we address as privacy-targeted collusion if attempted. In this context, our work investigates mechanisms deterring such collusion in VSS among rational and malicious...

Practical signature-based witness encryption (SWE) schemes recently emerged as a viable alternative to instantiate timed-release cryptography in the honest majority setting. In particular, assuming threshold trust in a set of parties that release signatures at a specified time, one can ``encrypt to the future'' using an SWE scheme. Applications of SWE schemes include voting, auctions, distributed randomness beacons, and more. However, the lack of homomorphism in existing SWE schemes reduces...

Presenting a novel use of encryption, not for hiding a secret, but for marking letters. Given a 2n letters plaintext, the transmitter encrypts the first n letters with key K1 to generate corresponding n cipherletters, and encrypts the second n letters with key K2 to generate n corresponding cipherletters. The transmitter sends the 2n cipherletters along with the keys, K1 and K2 The recipient (and any interceptor) will readily decrypt the 2n cipherletters to the original plaintext. This...

Secure Two-Party Computation (2PC) enables secure inference with cryptographic guarantees that protect the privacy of the model owner and client. However, it adds significant performance overhead. In this work, we make 2PC-based secure inference efficient while considering important deployment scenarios. We observe that the hitherto unconsidered latency of fetching keys from storage significantly impacts performance, as does network speed. We design a Linear Secret Sharing (LSS)-based...

Private computation on common records refers to analyze data from two databases containing shared records without revealing personal information. As a basic requirement for private computation, the databases involved essentially need to be aligned by a common identification system. However, it is hard to expect such common identifiers in real world scenario. For this reason, multiple quasi-identifiers can be used to identify common records. As some quasi-identifiers might be missing or have...

Consider a weak analyst that wishes to outsource data collection and computation of aggregate statistics over a a potentially large population of (also weak) clients to a powerful server. For flexibility and efficiency, we consider public-key and non-interactive protocols, meaning the clients know the analyst's public key but do not share secrets, and each client sends at most one message. Furthermore, the final step should be silent, whereby the analyst simply downloads the (encrypted)...

The requirement for privacy-aware machine learning increases as we continue to use PII (Personally Identifiable Information) within machine training. To overcome these privacy issues, we can apply Fully Homomorphic Encryption (FHE) to encrypt data before it is fed into a machine learning model. This involves creating a homomorphic encryption key pair, and where the associated public key will be used to encrypt the input data, and the private key will decrypt the output. But, there is often a...

The introduction of shared computation architectures assembled from heterogeneous chiplets introduces new security threats. Due to the shared logical and physical resources, an untrusted chiplet can act maliciously to surreptitiously probe the data communication between chiplets or sense the computation shared between them. This paper presents Garblet, the first framework to leverage the flexibility offered by chiplet technology and Garbled Circuits (GC)-based MPC to enable efficient,...

Hiding the metadata in Internet protocols serves to protect user privacy, dissuade traffic analysis, and prevent network ossification. Fully encrypted protocols require even the initial key exchange to be obfuscated: a passive observer should be unable to distinguish a protocol execution from an exchange of random bitstrings. Deployed obfuscated key exchanges such as Tor's pluggable transport protocol obfs4 are Diffie–Hellman-based, and rely on the Elligator encoding for obfuscation....

Secure multi-party computation (MPC) enables collaborative, privacy-preserving computation over private inputs. Advances in homomorphic encryption (HE), particularly the CKKS scheme, have made secure computation practical, making it well-suited for real-world applications involving approximate computations. However, the inherent approximation errors in CKKS present significant challenges in developing MPC protocols. This paper investigates the problem of secure approximate MPC from CKKS....

Oblivious Transfer (OT) is a significant two party privacy preserving cryptographic primitive. OT involves a sender having several pieces of information and a receiver having a choice bit. The choice bit represents the piece of information that the receiver wants to obtain as an output of OT. At the end of the protocol, sender remains oblivious about the choice bit and receiver remains oblivious to the contents of the information that were not chosen. It has applications ranging from secure...

Authenticated encryption (AE) provides both authenticity and privacy. Starting with Bellare's and Namprempre's work in 2000, the Encrypt-then-MAC composition of an encryption scheme for privacy and a MAC for authenticity has become a well-studied and common approach. This work investigates the security of the Encrypt-then-MAC composition in a quantum setting which means that adversarial queries as well as the responses to those queries may be in superposition. We demonstrate that the...

CKKS is a homomorphic encryption (HE) scheme that supports arithmetic over complex numbers in an approximate manner. Despite its utility in PPML protocols, formally defining the security of CKKS-based protocols is challenging due to its approximate nature. To be precise, in a sender-receiver model, where the receiver holds input ciphertexts and the sender evaluates its private circuit, it is difficult to define sender's privacy in terms of indistinguishability, whereas receiver's privacy...

Oblivious Transfer (OT) is a fundamental cryptographic primitive introduced nearly four decades ago. OT allows a receiver to select and learn $t$ out of $n$ private messages held by a sender. It ensures that the sender does not learn which specific messages the receiver has chosen, while the receiver gains no information about the remaining $n − t$ messages. In this work, we introduce the notion of functional OT (FOT), for the first time. FOT adds a layer of security to the conventional OT...

Secure two-party comparison, known as Yao's millionaires' problem, has been a fundamental challenge in privacy-preserving computation. It enables two parties to compare their inputs without revealing the exact values of those inputs or relying on any trusted third party. One elegant approach to secure computation is based on homomorphic encryption. Recently, building on this approach, Carlton et al. (CT-RSA 2018) and Bourse et al. (CT-RSA 2020) presented novel solutions for the problem of...

Highly-optimized assembly is commonly used to achieve the best performance for popular cryptographic schemes such as the newly standardized ML-KEM and ML-DSA. The majority of implementations today rely on hand-optimized assembly for the core building blocks to achieve both security and performance. However, recent work by Abdulrahman et al. takes a new approach, writing a readable base assembly implementation first and leaving the bulk of the optimization work to a tool named SLOTHY based...

Traitor tracing is a traditional cryptographic primitive designed for scenarios with multiple legitimate receivers. When the plaintext - that is, the output of decryption - is leaked and more than one legitimate receiver exists, it becomes imperative to identify the source of the leakage, a need that has motivated the development of traitor tracing techniques. Recent advances in standard encryption have enabled decryption outcomes to be defined in a fine-grained manner through the...

Blind signatures allow a user to obtain a signature from an issuer in a privacy-preserving way: the issuer neither learns the signed message, nor can link the signature to its issuance. The threshold version of blind signatures further splits the secret key among n issuers, and requires the user to obtain at least t ≤ n of signature shares in order to derive the final signature. Security should then hold as long as at most t − 1 issuers are corrupt. Security for blind signatures is expressed...

With the growing emphasis on data privacy, secure multi-party computation has garnered significant attention for its strong security guarantees in developing privacy-preserving machine learning (PPML) schemes. However, only a few works address scenarios with a large number of participants. The state of the art by Liu et al. (LXY24, USENIX Security'24) first achieves a practical PPML protocol for up to 63 parties but is constrained to semi-honest security. Although naive extensions to the...

Proxy re-encryption (PRE) has been regarded as an effective cryptographic primitive in data sharing systems with distributed proxies. However, no literature considers the honesty of data owners, which is critical in the age of big data. In this paper, we fill the gap by introducing a new proxy re-encryption scheme, called publicly verifiable threshold PRE (PVTPRE). Briefly speaking, we innovatively apply a slightly modified publicly verifiable secret sharing (PVSS) scheme to distribute the...

A recent work by Boneh, Partap, and Rotem [Crypto'24] introduced the concept of traceable threshold encryption, in that if $t$ or more parties collude to construct a decryption box, which performs decryptions, then at least one party's identity can be traced by making a few black-box queries to the box. This has important applications, e.g., in blockchain mempool privacy, where collusion yields high financial gain through MEVs without any consequence - the possibility of tracing discourages...

Constant-time (CT) is a popular programming discipline to protect cryptographic libraries against micro-architectural timing attacks. One appeal of the CT discipline lies in its conceptual simplicity: a program is CT iff it has no secret-dependent data-flow, control-flow or variable-timing operation. Thanks to its simplicity, the CT discipline is supported by dozens of analysis tools. However, a recent user study demonstrates that these tools are seldom used due to poor usability and...

Multi-signatures allow a set of parties to produce a single signature for a common message by combining their individual signatures. The result can be verified using the aggregated public key that represents the group of signers. Very recent work by Lehmann and Özbay (PKC '24) studied the use of multi-signatures for ad-hoc privacy-preserving group signing, formalizing the notion of multi-signatures with probabilistic yet verifiable key aggregation. Moreover, they proposed new BLS-type...

In Crypto'19, Goyal, Jain, and Sahai (GJS) introduced the elegant notion of *secret-sharing of an NP statement* (NPSS). Roughly speaking, a $t$-out-of-$n$ secret sharing of an NP statement is a reduction that maps an instance-witness pair to $n$ instance-witness pairs such that any subset of $(t-1)$ reveals no information about the original witness, while any subset of $t$ allows full recovery of the original witness. Although the notion was formulated for general $t \leq n$, the only...

Oblivious pseudorandom functions (OPRFs) are an important primitive in privacy-preserving cryptographic protocols. The growing interest in OPRFs, both in theory and practice, has led to the development of numerous constructions and variations. However, most of these constructions rely on classical assumptions. Potential future quantum attacks may limit the practicality of those OPRFs for real-world applications. To close this gap, we introduce Leap, a novel OPRF based on heuristic...

Secret-sharing-based multi-party computation provides effective solutions for privacy-preserving machine learning. In this paper, we present novel protocols for privacy-preserving neural network training using Shamir secret sharing scheme over Galois rings. The specific Galois ring we use is \(GR(2^k, d)\), which contains $\mathbb{Z}_{2^k}$ as a subring. The algebraic structure of \(GR(2^k, d)\) enables us to benefit from Shamir scheme while performing modulo operations only on \(2^k\)...

In many areas of cybersecurity, we require access to Personally Identifiable Information (PII), such as names, postal addresses and email addresses. Unfortunately, this can lead to data breaches, especially in relation to data compliance regulations such as GDPR. An IP address is a typical identifier which is used to map a network address to a person. Thus, in applications which are privacy-aware, we may aim to hide the IP address while aiming to determine if the address comes from a...

Recent applications and attacks have highlighted the need for authenticated encryption (AE) schemes to achieve the so-called committing security beyond privacy and authenticity. As a result, several generic solutions have been proposed to transform a non-committing AE scheme to a committing one, for both basic unique-nonce security and advanced misuse-resistant (MR) security. We observe that all existing practical generic transforms are subject to at least one of the following limitations:...

Although privately programmable pseudorandom functions (PPPRFs) are known to have numerous applications, so far, the only known constructions rely on Learning with Error (LWE) or indistinguishability obfuscation. We show how to construct a relaxed PPPRF with only one-way functions (OWF). The resulting PPPRF satisfies $1/\textsf{poly}$ security and works for polynomially sized input domains. Using the resulting PPPRF, we can get new results for preprocessing Private Information Retrieval...

Ever since the introduction of encryption, society has been divided over whether the government (or law enforcement agencies) should have the capability to decrypt private messages (with or without a war- rant) of its citizens. From a technical viewpoint, the folklore belief is that semantic security always enables some form of steganography. Thus, adding backdoors to semantically secure schemes is pointless: it only weakens the security of the “good guys”, while “bad guys” can easily...

Dynamic Decentralized Functional Encryption (DDFE) is a generalization of Functional Encryption which allows multiple users to join the system dynamically without interaction and without relying on a trusted third party. Users can independently encrypt their inputs for a joint evaluation under functions embedded in functional decryption keys; and they keep control on these functions as they all have to contribute to the generation of the functional keys. In this work, we present new...

We address the problem of proving the validity of computation on ciphertexts of homomorphic encryption (HE) schemes, a feature that enables outsourcing of data and computation while ensuring both data privacy and integrity. We propose a new solution that handles computations in RingLWE-based schemes, particularly the CKKS scheme for approximate arithmetic. Our approach efficiently handles ciphertext arithmetic in the polynomial ring $R_q$ without emulation overhead and manages ciphertexts...

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without requiring decryption, ensuring data privacy during processing. However, FHE introduces a significant expansion of ciphertext sizes compared to plaintexts, which results in higher communication. A practical solution to mitigate this issue is transciphering, where only the master key is homomorphically encrypted, while the actual data is encrypted using a symmetric cipher, usually a stream cipher. The server...

NIST has standardized ML-KEM and ML-DSA as replacements for pre-quantum key exchanges and digital signatures. Both schemes have already seen analysis with respect to side-channels, and first fully masked implementations of ML-DSA have been published. Previous attacks have focused on unprotected implementations or assumed only hiding countermeasures to be in-place. Thus, in contrast to ML-KEM, the threat of side-channel attacks for protected implementations of ML-DSA is mostly unclear. In...

Software watermarking for cryptographic functionalities enables embedding an arbitrary message (a mark) into a cryptographic function. An extraction algorithm, when provided with a (potentially unauthorized) circuit, retrieves either the embedded mark or a special symbol unmarked indicating the absence of a mark. It is difficult to modify or remove the embedded mark without destroying the functionality of a marked function. Previous works have primarily employed black-box extraction...

Fully Homomorphic Encryption (FHE) enables computations on encrypted data, ensuring privacy for outsourced computation. However, verifying the integrity of FHE computations remains a significant challenge, especially for bootstrapping, the most computationally intensive operation in FHE. Prior approaches, including zkVM-based solutions and general-purpose SNARKs, suffer from inefficiencies, with proof generation times ranging from several hours to days. In this work, we propose HasteBoots, a...

Floating-point arithmetic plays a central role in computer science and is used in various domains where precision and computational scale are essential. One notable application is in machine learning, where Fully Homomorphic Encryption (FHE) can play a crucial role in safeguarding user privacy. In this paper, we focus on TFHE and develop novel homomorphic operators designed to enable the construction of precise and adaptable homomorphic floating-point operations. Integrating floating-point...

$\textit{Linkable ring signatures}$ (LRS) allow a user to sign anonymously on behalf of a ring, while maintaining linkability—two signatures from the same signer are publicly identified, i.e., linked. This linkability makes LRS suitable to prevent double-voting in classical, $\textit{plurality}$ voting protocols—each voter casts one vote and the candidate with the most votes wins the election. Several voting scenarios rely on (generalized) rules rather than plurality. For example, in...

We introduce DART, a fully anonymous, account-based payment system designed to address a comprehensive set of real-world considerations, including regulatory compliance, while achieving constant transaction size. DART supports multiple asset types, enabling users to issue on-chain assets such as tokenized real-world assets. It ensures confidentiality and anonymity by concealing asset types, transaction amounts, balances, and the identities of both senders and receivers, while guaranteeing...