International Association
for Cryptologic Research

Privacy-Preserving Machine Learning (PPML) provides protocols for learning and statistical analysis of data that may be distributed amongst multiple data owners (e.g., hospitals that own proprietary healthcare data), while preserving data privacy. The PPML literature includes protocols for various learning methods, including ridge regression. Ridge regression controls the $L_2$ norm of the model, but does not aim to strictly reduce the number of non-zero coefficients, namely the $L_0$ norm of the model. Reducing the number of non-zero coefficients (a form of feature selection) is important for avoiding overfitting, and for reducing the cost of using learnt models in practice. In this work, we develop a first privacy-preserving protocol for sparse linear regression under $L_0$ constraints. The protocol addresses data contributed by several data owners (e.g., hospitals). Our protocol outsources the bulk of the computation to two non-colluding servers, using homomorphic encryption as a central tool. We provide a rigorous security proof for our protocol, where security is against semi-honest adversaries controlling any number of data owners and at most one server. We implemented our protocol, and evaluated performance with nearly a million samples and up to 40 features.

The design and analysis of dedicated tweakable block ciphers constitute a dynamic and relatively recent research field in symmetric cryptanalysis. The assessment of security in the related-tweakey model is of utmost importance owing to the existence of a public tweak. This paper proposes an automatic search model for identifying related-tweakey impossible differentials based on the propagation of states under specific constraints, which is inspired by the research of Hu et al. in ASIACRYPT 2020. Our model is universally applicable to block ciphers, but its search efficiency may be limited in some cases. To address this issue, we introduce the Locality Constraint Analysis (LCA) technique to impossible differential cryptanalysis and propose a generalized automatic search model. Technically, we transform our models into Satisfiability Modulo Theories (SMT) problems and solve them using the STP solver. We have applied our tools to several tweakable block ciphers, such as Joltik-BC, SKINNY, QARMA, and CRAFT, to evaluate their effectiveness and practicality. Specifically, we have discovered 7-round related-tweakey impossible differentials for Joltik-BC-192, and 12-round related-tweak impossible differentials, as well as 15-round related-tweakey impossible differentials for CRAFT for the first time. Based on the search results, we demonstrate that the LCA technique can be effectively performed when searching and determining the contradictory positions for the distinguisher with long trails or ciphers with large sizes in impossible differential cryptanalysis.

In this work, we tackle the problem of estimating the security of iterated symmetric ciphers in an efficient manner, with tests that do not require a deep analysis of the internal structure of the cipher. This is particularly useful during the design phase of these ciphers, especially for quickly testing several combinations of possible parameters defining several cipher design variants. We consider a popular statistical test that allows us to determine the probability of flipping each cipher output bit, given a small variation in the input of the cipher. From these probabilities, one can compute three measurable metrics related to the well-known full diffusion, avalanche and strict avalanche criteria. This highly parallelizable testing process scales linearly with the number of samples, i.e., cipher inputs, to be evaluated and the number of design variants to be tested. But, the number of design variants might grow exponentially with respect to some parameters. The high cost of CPUs, makes them a bad candidate for this kind of parallelization. As a main contribution, we propose a framework, ACE-HoT, to parallelize the testing process using multi-GPU. Our implementation does not perform any intermediate CPU-GPU data transfers. The diffusion and avalanche criteria can be seen as an application of discrete first-order derivatives. As a secondary contribution, we generalize these criteria to their high-order version. Our generalization requires an exponentially larger number of samples, in order to compute sufficiently accurate probabilities. As a case study, we apply ACE-HoT on most of the finalists of the NIST lightweight standardization process, with a special focus on the winner ASCON.

This paper introduces Bicameral and Auditably Private Signatures (BAPS) -- a new privacy-preserving signature system with several novel features. In a BAPS system, given a certified attribute $\mathbf{x}$ and a certified policy $P$, a signer can issue a publicly verifiable signature $\Sigma$ on a message $m$ as long as $(m, \mathbf{x})$ satisfies $P$. A noteworthy characteristic of BAPS is that both attribute $\mathbf{x}$ and policy $P$ are kept hidden from the verifier, yet the latter is convinced that these objects were certified by an attribute-issuing authority and a policy-issuing authority, respectively. By considering bicameral certification authorities and requiring privacy for both attributes and policies, BAPS generalizes the spirit of existing advanced signature primitives with fine-grained controls on signing capabilities (e.g., attribute-based signatures, predicate signatures, policy-based signatures). Furthermore, BAPS provides an appealing feature named auditable privacy, allowing the signer of $\Sigma$ to verifiably disclose various pieces of partial information about $P$ and $\mathbf{x}$ when asked by auditor(s)/court(s) at later times. Auditable privacy is intrinsically different from and can be complementary to the notion of accountable privacy traditionally incorporated in traceable anonymous systems such as group signatures. Equipped with these distinguished features, BAPS can potentially address interesting application scenarios for which existing primitives do not offer a direct solution.

We provide rigorous security definitions for BAPS, following a ``sim-ext'' approach. We then demonstrate a generic construction based on commonly used cryptographic building blocks, which employs a sign-then-commit-then-prove design. Finally, we present a concrete instantiation of BAPS, that is proven secure in the random oracle model under lattice assumptions. The scheme can handle arbitrary policies represented by polynomial-size Boolean circuits and can address quadratic disclosing functions. In the construction process, we develop a new technical building block that could be of independent interest: a zero-knowledge argument system allowing to prove the satisfiability of a certified-and-hidden Boolean circuit on certified-and-committed inputs.

The constant-sized polynomial commitment scheme by Kate, Zaverucha, and Goldberg (Asiscrypt 2010), also known as the KZG commitment, is an essential component in designing bandwidth-efficient verifiable secret-sharing (VSS) protocols. We point out, however, that the KZG commitment is missing two important properties that are crucial for VSS protocols.

First, the KZG commitment has not been proven to be degree binding in the standard adversary model without idealized group assumptions. In other words, the committed polynomial is not guaranteed to have the claimed degree, which is supposed to be the reconstruction threshold of VSS. Without this property, shareholders in VSS may end up reconstructing different secrets depending on which shares are used.

Second, the KZG commitment does not support polynomials with different degrees at once with a single setup. If the reconstruction threshold of the underlying VSS protocol changes, the protocol must redo the setup, which involves an expensive multi-party computation known as the powers of tau setup.

In this work, we augment the KZG commitment to address both of these limitations. Our scheme is degree-binding in the standard model under the strong Diffie-Hellman (SDH) assumption. It supports any degree $0 < d \le m$ under a powers-of-tau common reference string with $m+1$ group elements generated by a one-time setup.

Constructing key-agreement protocols in the random oracle model (ROM) is a viable method to assess the feasibility of developing public-key cryptography within Minicrypt. Unfortunately, as shown by Impagliazzo and Rudich (STOC 1989) and Barak and Mahmoody (Crypto 2009), such protocols can only guarantee limited security: any $\ell$-query protocol can be attacked by an $O(\ell^2)$-query adversary. This quadratic gap matches the key-agreement protocol proposed by Merkle (CACM 78), known as Merkle's Puzzles.

Besides query complexity, the communication complexity of key-agreement protocols in the ROM is also an interesting question in the realm of find-grained cryptography, even though only limited security is achievable. Haitner et al. (ITCS 2019) first observed that in Merkle's Puzzles, to obtain secrecy against an eavesdropper with $O(\ell^2)$ queries, the honest parties must exchange $\Omega(\ell)$ bits. Therefore, they conjectured that high communication complexity is unavoidable, i.e., any $\ell$-query protocols with $c$ bits of communication could be attacked by an $O(c\cdot \ell)$-query adversary. This, if true, will suggest that Merkle's Puzzle is also optimal regarding communication complexity. Building upon techniques from communication complexity, Haitner et al. (ITCS 2019) confirmed this conjecture for two types of key agreement protocols with certain natural properties.

This work affirms the above conjecture for all non-adaptive protocols with perfect completeness. Our proof uses a novel idea called density increment argument. This method could be of independent interest as it differs from previous communication lower bounds techniques (and bypasses some technical barriers).

Publicly Verifiable Secret Sharing (PVSS) is a fundamental primitive that allows to share a secret $S$ among $n$ parties via a publicly verifiable transcript $T$. Existing (efficient) PVSS are only proven secure against static adversaries who must choose who to corrupt ahead of a protocol execution. As a result, any protocol (e.g., a distributed randomness beacon) that builds on top of such a PVSS scheme inherits this limitation. To overcome this barrier, we revisit the security of PVSS under adaptive corruptions and show that, surprisingly, many protocols from the literature already achieve it in a meaningful way:

- We propose a new security definition for aggregatable PVSS, i.e., schemes that allow to homomorphically combine multiple transcripts into one compact aggregate transcript $AT$ that shares the sum of their individual secrets. Our notion captures that if the secret shared by $AT$ contains at least one contribution from an honestly generated transcript, it should not be predictable. We then prove that several existing schemes satisfy this notion against adaptive corruptions in the algebraic group model.

- To motivate our new notion, we show that it implies the adaptive security of two recent random beacon protocols, SPURT (S&P '22) and OptRand (NDSS '23), who build on top of aggregatable PVSS schemes satisfying our notion of unpredictability. For a security parameter $\lambda$, our result improves the communication complexity of the best known adaptively secure random beacon protocols to $O(\lambda n^2)$ for synchronous networks with $t

Repeated modular squaring plays a crucial role in various time-based cryptographic primitives, such as Time-Lock Puzzles and Verifiable Delay Functions. At ACM CCS 2021, Thyagarajan et al. introduced “OpenSquare”, a decentralised protocol that lets a client delegate the computation of repeated modular squaring to third-party servers while ensuring that these servers are compensated only if they deliver valid results. In this work, we unveil a significant vulnerability in OpenSquare, which enables servers to receive payments without fulfilling the delegated task. To tackle this issue, we present a series of mitigation measures.

Over the last decade, online reputation has become a central aspect of our digital lives. Most online services and communities assign a reputation score to users, based on feedback from other users about various criteria such as how reliable, helpful, or knowledgeable a person is. While many online services compute reputation based on the same set of such criteria, users currently do not have the ability to use their reputation scores across services. As a result, users face trouble establishing themselves on new services or trusting each other on services that do not support reputation tracking. Existing systems that aggregate reputation scores, unfortunately, provide no guarantee in terms of user privacy, and their use makes user accounts linkable. Such a lack of privacy may result in embarrassment, or worse, place users in danger.

In this paper, we present StreetRep, a practical system for aggregating user reputation scores in a privacy-preserving manner. StreetRep makes it possible for users to provide their aggregated scores over multiple services without revealing their respective identities on each service. We discuss our novel approach for tamper-proof privacy preserving score aggregation from multiple sources by combining existing techniques such as blind signatures, homomorphic signatures and private information retrieval. We discuss its practicality and resiliency against different types of attacks. We also built a prototype implementation of StreetRep. Our evaluation demonstrates that StreetRep (a) performs efficiently and (b) practically scales to a large user base.

How can a model owner prove they trained their model according to the correct specification? More importantly, how can they do so while preserving the privacy of the underlying dataset and the final model? We study this problem and formulate the notion of zero-knowledge proof of training (zkPoT), which formalizes rigorous security guarantees that should be achieved by a privacy-preserving proof of training. While it is theoretically possible to design zkPoT for any model using generic zero-knowledge proof systems, this approach results in extremely unpractical proof generation times. Towards designing a practical solution, we propose the idea of combining techniques from MPC-in-the-head and zkSNARKs literature to strike an appropriate trade-off between proof size and proof computation time. We instantiate this idea and propose a concretely efficient, novel zkPoT protocol for logistic regression.

Crucially, our protocol is streaming-friendly and does not require RAM proportional to the size of the circuit being trained and, hence, can be adapted to the requirements of available hardware. We expect the techniques developed in this paper to also generally be useful for designing efficient zkPoT protocols for other relatively more sophisticated ML models.

We implemented and benchmarked prover/verifier runtimes and proof sizes for training a logistic regression model using mini-batch gradient descent on a 4~GB dataset of 262,144 records with 1024 features. We divide our protocol into three phases: (1) data-independent offline phase (2) data-dependent phase that is independent of the model (3) online phase that depends both on the data and the model. The total proof size (across all three phases) is less than $10\%$ of the data set size ($<350$~MB). In the online phase, the prover and verifier times are under 10 minutes and half a minute respectively, whereas in the data-dependent phase, they are close to one hour and a few seconds respectively.

The Algorand consensus protocol is interesting both in theory and in practice. On the theoretical side, to achieve adaptive security, it introduces the novel idea of player replaceability, where each step of the protocol is executed by a different randomly selected committee whose members remain secret until they send their first and only message. The protocol provides consistency under arbitrary network conditions and liveness under intermittent network partitions. On the practical side, the protocol is used to secure the Algorand cryptocurrency, whose total value is approximately $850M at the time of writing.

The Algorand protocol in use differs substantially from the protocols described in the published literature on Algorand. Despite its significance, it lacks a formal analysis. In this work, we describe and analyze the Algorand consensus protocol as deployed today in Algorand’s ecosystem. We show that the overall protocol framework is sound by characterizing network conditions and parameter settings under which the protocol can be proven secure.

User privacy is becoming increasingly important in our digital society. Yet, many applications face legal requirements or regulations that prohibit unconditional anonymity guarantees, e.g., in electronic payments where surveillance is mandated to investigate suspected crimes.

As a result, many systems have no effective privacy protections at all, or have backdoors, e.g., stored at the operator side of the system, that can be used by authorities to disclose a user’s private information (e.g., lawful interception). The problem with such backdoors is that they also enable silent mass surveillance within the system. To prevent such misuse, various approaches have been suggested which limit possible abuse or ensure it can be detected. Many works consider auditability of surveillance actions but do not enforce that traces are left when backdoors are retrieved. A notable exception which offers retrospective and silent surveillance is the recent work on misuse-resistant surveillance by Green et al. (EUROCRYPT’21). However, their approach relies on extractable witness encryption, which is a very strong primitive with no known efficient and secure implementations.

In this work, we develop a building block for auditable surveillance. In our protocol, backdoors or escrow secrets of users are protected in multiple ways: (1) Backdoors are short-term and user-specific; (2) they are shared between trustworthy parties to avoid a single point of failure; and (3) backdoor access is given conditionally. Moreover (4) there are audit trails and public statistics for every (granted) backdoor request; and (5) surveillance remains silent, i.e., users do not know they are surveilled. Concretely, we present an abstract UC-functionality which can be used to augment applications with auditable surveillance capabilities. Our realization makes use of threshold encryption to protect user secrets, and is concretely built in a blockchain context with committee-based YOSO MPC. As a consequence, the committee can verify that the conditions for backdoor access are given, e.g., that law enforcement is in possession of a valid surveillance warrant (via a zero-knowledge proof). Moreover, access leaves an audit trail on the ledger, which allows an auditor to retrospectively examine surveillance decisions.

As a toy example, we present an Auditably Sender-Traceable Encryption scheme, a PKE scheme where the sender can be deanonymized by law enforcement. We observe and solve problems posed by retrospective surveillance via a special non-interactive non-committing encryption scheme which allows zero-knowledge proofs over message, sender identity and (escrow) secrets.

Event date: 21 December to 23 December 2023
Submission deadline: 15 September 2023
Notification: 31 October 2023

Event date: 4 September to 7 September 2024

The research involves developing SW and HW security solutions and acceleration of core cryptographic primitives deployed at the Edge. The successful candidate will be part of a Horizon Network of Excellence for distributed, trustworthy, efficient and scalable AI at the Edge.
Responsibilities:

Security and privacy threat modeling.

Security evaluation and assessment.

Design and implement optimized privacy-preserving computation protocols for low-power general purpose processors.

Write research papers and project deliverables, and present findings at intessrnational conferences and workshops.
Specific skills required
To be considered for this position, you should have a PhD degree in computer science or mathematics or cryptography, with a strong publication track record in top venues. In addition, good communication and programming skills are required.

Closing date for applications:

Contact: jobs-cosic@esat.kuleuven.be

More information: https://www.esat.kuleuven.be/cosic/vacancies/

The research involves developing SW and HW security solutions and acceleration of core cryptographic primitives deployed at the Edge. The successful candidate will be part of a Horizon Network of Excellence for distributed, trustworthy, efficient and scalable AI at the Edge.
Responsibilities:

Design and develop secure mechanisms for authentication, aothorization, and access control for the distributed network.

Design and develop lightweight security mechanisms for Federated Learning, and SW/HW acceleration of relevant cryptographic primitives.

Write research papers and present findings at intessrnational conferences and workshops.
Specific skills required To be considered for this position, you should have a strong background in computer science or mathematics, with a specialization in cryptography or related fields. Ideally, you will hold a master's degree in a relevant discipline and have excellent analytical and problem-solving skills. Good programming skills, e.g., in C/C++/Python/Rust, will be merited.

Closing date for applications:

Contact: jobs-cosic@esat.kuleuven.be

More information: https://www.esat.kuleuven.be/cosic/vacancies/

Event date: 4 March to 8 March 2024
Submission deadline: 18 September 2023
Notification: 25 November 2023

Cryptographic proof systems provide integrity, fairness, and privacy in applications that outsource data processing tasks. However, general-purpose proof systems do not scale well to large inputs. At the same time, ad-hoc solutions for concrete applications - e.g., machine learning or image processing - are more efficient but lack modularity, hence they are hard to extend or to compose with other tools of a data-processing pipeline.

In this paper, we combine the performance of tailored solutions with the versatility of general-purpose proof systems. We do so by introducing a modular framework for verifiable computation of sequential operations. The main tool of our framework is a new information-theoretic primitive called Verifiable Evaluation Scheme on Fingerprinted Data (VE) that captures the properties of diverse sumcheck-based interactive proofs, including the well-established GKR protocol. Thus, we show how to compose VEs for specific functions to obtain verifiability of a data-processing pipeline.

We propose a novel VE for convolution operations that can handle multiple input-output channels and batching, and we use it in our framework to build proofs for (convolutional) neural networks and image processing. We realize a prototype implementation of our proof systems, and show that we achieve up to $5 \times$ faster proving time and $10 \times$ shorter proofs compared to the state-of-the-art, in addition to asymptotic improvements.

Physical attacks are well-known threats to cryptographic implementations. While countermeasures against passive Side-Channel Analysis (SCA) and active Fault Injection Analysis (FIA) exist individually, protecting against their combination remains a significant challenge. A recent attempt at achieving joint security has been published at CCS 2022 under the name CINI-MINIS. The authors introduce relevant security notions and aim to construct arbitrary-order gadgets that remain trivially composable in the presence of a combined adversary. Yet, we show that all CINI-MINIS gadgets at any order are susceptible to a devastating attack with only a single fault and probe due to a lack of error correction modules in the compression. We explain the details of the attack, pinpoint the underlying problem in the constructions, propose an additional design principle, and provide new (fixed) provably secure and composable gadgets for arbitrary order. Luckily, the changes in the compression stage help us to save correction modules and registers elsewhere, making the resulting Combined Private Circuits (CPC) more secure and more efficient than the original ones. We also explain why the discovered flaws have been missed by the associated formal verification tool VERICA (TCHES 2022) and propose fixes to remove its blind spot. Finally, we explore alternative avenues to repair the compression stage without additional corrections based on non-completeness, i.e., constructing a compression that never recombines any secret. Yet, while this approach could have merit for low-order gadgets, it is, for now, hard to generalize and scales poorly to higher orders. We conclude that our refurbished arbitrary order CINI gadgets provide a solid foundation for further research.

In the context of post-quantum secure algorithms like CRYSTALS-Kyber, the importance of protecting sensitive polynomial coefficients from side-channel attacks is increasingly recognized. Our research introduces two alternative masking methods to enhance the security of the compression function in Kyber through masking. Prior to this, the topic had been addressed by only one other research study. The "Double and Check" method integrates arithmetic sharing and symmetry adjustments, introducing a layer of obfuscation by determining coefficient values based on modular overflows. In contrast, the Look-Up-Table (LUT) integration method employs arithmetic-to-Boolean conversions, augmented by a pre-computed table for efficient value verifications. Furthermore, by leveraging the alternative prime 7681, we propose a novel masked compression function. This prime, 7681, is also notable as the smallest prime suitable for fast NTT multiplication. While both algorithms prioritize data protection and streamlined processing, they also underscore the inherent challenges of balancing computational speed with the potential vulnerabilities to side-channel attacks.