International Association
for Cryptologic Research

Inspired by the literature on side-channel attacks against cryptographic implementations, we describe a framework for the analysis of location privacy. It allows us to revisit (continuous) re-identification attacks with a combination of information theoretic and security metrics. Our results highlight conceptual differences between re-identification attacks exploiting leakages that are internal or external to a pseudonymised database. They put forward the amount of data to collect in order to estimate a predictive model as an important -- yet less discussed -- dimension of privacy assessments. They finally leverage recent results on the security evaluations/certification of cryptographic implementations to connect information theoretic and security metrics, and to formally bound the risk of re-identification with external leakages.

Recently with the advent of technology, a lot of data are stored and mined in cloud servers. Since most of the data contain potential private information, it has become necessary to preserve the privacy in data mining. In this paper, we propose a protocol for collaboratively performing the K-means clustering algorithm on the data distributed among multiple data owners, while protecting the sensitive private data. We employ two service providers in our scenario, namely a main service provider and a key manager. Under the assumption that the cryptosystems used in our protocol are secure and that the two service providers do not collude, we provide a perfect secrecy in the sense that the cluster centroids and data are not leaked to any party including the two service providers. Also, we implement the scenario using recently proposed leveled homomorphic encryption called HEAAN. With our construction, the privacy-preserving K-means clustering can be done in less than one minute while maintaining 80-bit security in a situation with 10,000 data, 8 features and 4 clusters.

Clustering analysis is one of the most significant unsupervised machine learning tasks, and it is utilized in various fields associated with privacy issue including bioinformatics, finance and image processing. In this paper, we propose a practical solution for privacy-preserving clustering analysis based on homomorphic encryption~(HE). Our work is the first HE solution for the mean-shift clustering algorithm. To reduce the super-linear complexity of the original mean-shift algorithm, we adopt a novel random sampling method called dust sampling which perfectly fits in HE and achieve the linear complexity. We also substitute non-polynomial kernels by a new polynomial kernel so that it can be efficiently computed in HE. The quality of clustering analysis with the new HE-friendly kernel is fairly fine in practice.

The performance of our modified mean-shift clustering algorithm based on the approximate HE scheme HEAAN is quite remarkable in terms of speed and accuracy. It takes about $30$ minutes with $99\%$ accuracy over several public datasets with hundreds of data, but even for two hundred thousands of data it takes only $82$ minutes with SIMD operations in HEAAN. Our results outperform the previously best known result over $400$ times.

In this note, we leverage some of our previous results to produce a concise and rigorous proof for the complexity of the generalized MinRank Problem in the under-defined and well-defined case. Our main theorem recovers and extends previous results by Faugère, Safey El Din, Spaenlehauer.

An account of meandering research efforts in the area of cryptographic obfuscation over several years.

Being the most widely used and comprehensive standard for hardware security modules, cryptographic tokens and smart cards, PKCS#11 has been the subject of academic study for years. PKCS#11 provides a key store that is separate from the application, so that, ideally, an application never sees a key in the clear. Again and again, researchers have pointed out the need for an import/export mechanism that ensures the integrity of the permissions associated to a key. With version 2.40, for the first time, the standard included authenticated deterministic encryption schemes. The interface to this operation is insecure, however, so that an application can get the key in the clear, subverting the purpose of using a hardware security module.

This work proposes a formal model for the secure use of authenticated deterministic encryption in PKCS11, including concrete API changes to allow for secure policies to be implemented. Owing to the authenticated encryption mechanism, the policy we propose provides more functionality than any policy proposed so far and can be implemented without access to a random number generator. Our results cover modes of operation that rely on unique initialisation vectors (IVs), like GCM or CCM, but also modes that generate synthetic IVs. We furthermore provide a proof for the deduction soundness of our modelling of deterministic encryption in Böhl et.al.'s composable deduction soundness framework.

Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk of misclassification can lead to serious consequences. It is necessary to know to which extent are Deep Neural Networks (DNNs) robust against various types of adversarial conditions.

In this paper, we experimentally evaluate DNNs implemented in embedded device by using laser fault injection, a physical attack technique that is mostly used in security and reliability communities to test robustness of various systems. We show practical results on four activation functions, ReLu, softmax, sigmoid, and tanh. Our results point out the misclassification possibilities for DNNs achieved by injecting faults into the hidden layers of the network. We evaluate DNNs by using several different attack strategies to show which are the most efficient in terms of misclassification success rates. Protection techniques against these attacks are also presented. Outcomes of this work should be taken into account when deploying devices running DNNs in environments where malicious attacker could tamper with the environmental parameters that would bring the device into unstable conditions, resulting into faults.

Cryptographic anonymous credential schemes allow users to prove their personal attributes, such as age, nationality, or the validity of a ticket or a pre-paid pass, while preserving their privacy, as such proofs are unlinkable and attributes can be selectively disclosed. Recently, Chase et al. (CCS 2014) observe that in such systems, a typical setup is that the credential issuer also serves as the verifier. They introduce keyed-verification credentials that are tailored to this setting. In this paper, we present a novel keyed-verification credential system designed for lightweight devices (primarily smart cards) and prove its security. By using a novel algebraic MAC based on Boneh-Boyen signatures, we achieve the most efficient proving protocol compared to existing schemes. To demonstrate the practicality of our scheme in real applications, including large-scale services such as public transportation or e-government, we present an implementation on a standard, off-the-shelf, Multos smart card. While using significantly higher security parameters than most existing implementations, we achieve performance that is more than 44 % better than the current state-of-the-art implementation.

A chosen-prefix collision attack is a stronger variant of a collision attack, where an arbitrary pair of challenge prefixes are turned into a collision. Chosen-prefix collisions are usually significantly harder to produce than (identical-prefix) collisions, but the practical impact of such an attack is much larger. While many cryptographic constructions rely on collision-resistance for their security proofs, collision attacks are hard to turn into a break of concrete protocols, because the adversary has limited control over the colliding messages. On the other hand, chosen-prefix collisions have been shown to break certificates (by creating a rogue CA) and many internet protocols (TLS, SSH, IPsec).

In this article, we propose new techniques to turn collision attacks into chosen-prefix collision attacks. Our strategy is composed of two phases: first, a birthday search that aims at taking the random chaining variable difference (due to the chosen-prefix model) to a set of pre-defined target differences. Then, using a multi-block approach, carefully analysing the clustering effect, we map this new chaining variable difference to a colliding pair of states using techniques developed for collision attacks.

We apply those techniques to MD5 and SHA1, and obtain improved attacks. In particular, we have a chosen-prefix collision attack against SHA1 with complexity between $2^{66.9}$ and $2^{69.4}$ (depending on assumptions about the cost of finding near-collision blocks), while the best-known attack has complexity $2^{77.1}$. This is within a small factor of the complexity of the classical collision attack on SHA1 (estimated as $2^{64.7}$). This represents yet another warning that industries and users have to move away from using SHA1 as soon as possible.

The area of practical proof systems, like SNARKs, STARKs, or Bulletproofs, is seeing a very dynamic development. Many use-cases of such systems involve, often as their most expensive apart, proving the knowledge of a preimage under a certain cryptographic hash function.

In this paper we present a modular framework and concrete instances of cryptographic hash functions which either work natively with GF(p) objects or on binary strings. Compared to competitors, our hash function Poseidon uses up to 8x fewer constraints per message bit compared to Pedersen Hash, whereas our STARK-friendly hash Starkad takes wins the factor of 4 over the hash function Friday by using a much smaller field.

mixFeed [CN19] is a round 1 candidate for the NIST Lightweight Cryptography Standardization Project. It is a single-pass, nonce-based, AES-based authenticated encryption algorithms. The authors claim that while there are no guarantees for security in terms of confidentiality in case of nonce-misuse (repetition), the integrity security still holds up to 2^32 data complexity. In this report, this claim is not true in case the plaintext length is non-zero (≥ 16 bytes to be exact). We show a forgery attack that requires only two encryption queries with the same nonce and 34 bytes of data.

We present "UniqueChain", a proof-of-stake based blockchain protocol that achieves secure initialization of newly joining parties without any additional trusted assumptions and fast messages (transactions) confirmation. Specifically, the adversary can send corrupt instructions to any parties at any time mildly and have messages delivery delay with an upper bound. Security of our protocol holds if majority of overall stakes are controlled by honest parties.

In "UniqueChain", we propose a new form of two-chain structure that consists of two tightly linked chains named leader chain and transaction chain with two types of corresponding blocks named leader block and transaction block. To achieve the above guarantees, we formalize a secure bootstrapping mechanism for new parties in open setting and realize uniqueness of transaction chains held by honest parties. We prove that "UniqueChain" satisfies security properties as chain growth, chain quality, common prefix and soundness, and two additional properties as uniqueness and high efficiency.

Monero is one of the first and most popular cryptocurrencies to address privacy issues of other crypto coins such as Bitcoin. Monero has a market capitalization of over one billion US dollars, and is ranked the 12th most valuable cryptocurrency on CoinMarketCap (17 April 2019). This digital coin provides different mechanisms to protect its users, such as decoy keys or mixins to obfuscate transaction inputs. However, in spite of the efforts to protect Monero’s users privacy, transaction tracing attacks are still feasible. Our contribution is twofold. First, we propose and evaluate a new traceability attack, called transaction flooding attack (FloodXMR). Second, we present an analysis of thecosts required for an attacker to conduct FloodXMR. We show how an attacker can take advantage of Monero’s Bulletproof protocol, which reduces transaction fees, to flood the network with his own transactions and, consequently, remove mixins from transaction inputs. Assuming an attack timeframe of 12 months, our findings show that an attacker can trace up to 47.63% of the transaction inputs at a cost of just 1,746.53 USD. Moreover, we show also that more than 90% of the inputs are affected by our tracing algorithm.

Secure multiparty computation (MPC) has been repeatedly optimized, and protocols with two communication rounds and strong security guarantees have been achieved. While progress has been made constructing non-interactive protocols with just one-round of online communication (i.e., non-interactive MPC or NI-MPC), since correct evaluation must be guaranteed with only one round, these protocols are by their nature vulnerable to the residual function attack in the standard model. This is because a party that receives a garbled circuit may repeatedly evaluate the circuit locally, while varying their own inputs and fixing the input of others to learn the values entered by other participants. We present the first MPC protocol with a one-round online phase that is secure against the residual function attack. We also present rigorous proofs of correctness and security in the covert adversary model, a reduction of the malicious model that is stronger than the semi-honest model and better suited for modeling the behaviour of parties in the real world, for our protocol. Furthermore, we rigorously analyze the communication and computational complexity of current state of the art protocols which require two rounds of communication or one-round during the online-phase with a reduced security requirement, and demonstrate that our protocol is comparable to or outperforms their complexity.

A reputation system assigns a user or item a reputation value which can be used to evaluate trustworthiness. Bl{\"o}mer, Juhnke and Kolb in 2015, and Kaafarani, Katsumata and Solomon in 2018, gave formal models for \mathit{centralised} reputation systems, which rely on a central server and are widely used by service providers such as AirBnB, Uber and Amazon. In these models, reputation values are given to items, instead of users. We advocate a need for shift in how reputation systems are modelled, whereby reputation values are given to users, instead of items, and each user has unlinkable items that other users can give feedback on, contributing to their reputation value. This setting is not captured by the previous models, and we argue it captures more realistically the functionality and security requirements of a reputation system. We provide definitions for this new model, and give a construction from standard primitives, proving it satisfies these security requirements. We show that there is a low efficiency cost for this new functionality.

The purpose of this paper is to use a Central Limit approach to develop a statistical framework for analysing ciphertexts in Ring-LWE homomorphic encryption schemes. This statistical framework gives rise to Normal approximations for ciphertext random variables, and we show that this allows probabilities to be determined more accurately and hence enables better bounds for decryption failure probabilities than the widely used existing approach based on $\delta$-subgaussian random variables. To demonstrate the benefit of the Central Limit approach, we apply our framework and results to a homomorphic Ring-LWE cryptosystem of Lyubashevsky, Peikert and Regev (Eurocrypt 2013, full version).

This paper presents CONCRETE (Commit-Encrypt-Send-the-Key) a new Authenticated Encryption mode that offers CIML2 security, that is, ciphertext integrity in the presence of nonce misuse and side-channel leakages in both encryption and decryption.

CONCRETE improves on a recent line of works aiming at leveled implementations, which mix a strongly protected and energy demanding implementation of a single component, and other weakly protected and much cheaper components. Here, these components all implement a tweakable block cipher TBC.

CONCRETE requires the use of the strongly protected TBC only once while supporting the leakage of the full state of the weakly protected components -- it achieves CIML2 security in the so-called unbounded leakage model.

All previous works need to use the strongly protected implementation at least twice. As a result, for short messages whose encryption and decryption energy costs are dominated by the strongly protected component, we halve the cost of a leakage-resilient implementation. CONCRETE additionally provides security when unverified plaintexts are released, and confidentiality in the presence of simulatable leakages in encryption and decryption.

In this paper, we introduce two lightweight historical data based multi-factor authenticated key exchange (HMAKE) protocols in the random oracle model. Our HMAKE protocols use a symmetric secret key, as their first authentication factor, together with their second authentication factor, historical data exchanged between the two parties in the past, and the third authentication factor, a set of secret tags associated with the historical data, to establish a secure communication channel between the client and the server.

A remarkable security feature of HMAKE is bounded historical tag leakage resilience, which means that (informally speaking) if a small portion of the secret tags is leaked to an adversary, it will not affect the security of one HMAKE protocol with an overwhelming probability. Our first HMAKE protocol can provide static bounded leakage resilience, meaning that the secret tags are leaked at the beginning of the security game. To enhance its security, our second HMAKE protocol makes use of our first protocol as a compiler to transform any passively secure two-message key exchange protocol to an actively secure HMAKE protocol with perfect forward secrecy, and therefore it can be secure even if the historical tags are compromised adaptively by an attacker.

In addition to the strong security properties we achieved, our protocols can potentially have great impacts in practice: they are efficient in computation, and they are compatible with legacy devices in cyber-physical systems.

There have been many successes in constructing explicit non-malleable codes for various classes of tampering functions in recent years, and strong existential results are also known. In this work we ask the following question: "When can we rule out the existence of a non-malleable code for a tampering class $\mathcal{F}$?"

We show that non-malleable codes are impossible to construct for three different tampering classes: 1. Functions that change $d/2$ symbols, where $d$ is the distance of the code; 2. Functions where each input symbol affects only a single output symbol; 3. Functions where each of the $n$ output symbols is a function of $n-\log n$ input symbols.

We additionally rule out constructions of non-malleable codes for certain classes $\mathcal{F}$ via reductions to the assumption that a distributional problem is hard for $\mathcal{F}$, that make black-box use of the tampering functions in the proof. In particular, this yields concrete obstacles for the construction of efficient codes for $\mathsf{NC}$, even assuming average-case variants of $P\not\subseteq\mathsf{NC}$.

Secure multiparty computation (MPC) often relies on sources of correlated randomness for better efficiency and simplicity. This is particularly useful for MPC with no honest majority, where input-independent correlated randomness enables a lightweight “non-cryptographic” online phase once the inputs are known. However, since the amount of correlated randomness typically scales with the circuit size of the function being computed, securely generating correlated randomness forms an efficiency bottleneck, involving a large amount of communication and storage. A natural tool for addressing the above limitations is a pseudorandom correlation generator (PCG). A PCG allows two or more parties to securely generate long sources of useful correlated randomness via a local expansion of correlated short seeds and no interaction. PCGs enable MPC with silent preprocessing, where a small amount of interaction used for securely sampling the seeds is followed by silent local generation of correlated pseudorandomness. A concretely efficient PCG for Vector-OLE correlations was recently obtained by Boyle et al. (CCS 2018) based on variants of the learning parity with noise (LPN) assumption over large fields. In this work, we initiate a systematic study of PCGs and present concretely efficient constructions for several types of useful MPC correlations. We obtain the following main contributions: – PCG foundations. We give a general security definition for PCGs. Our definition suffices for any MPC protocol satisfying a stronger security requirement that is met by existing protocols. We prove that a stronger security requirement is indeed necessary, and justify our PCG definition by ruling out a stronger and more natural definition. – Silent OT extension. We present the first concretely efficient PCG for oblivious transfer correlations. Its security is based on a variant of the binary LPN assumption and any correlation-robust hash function. We expect it to provide a faster alternative to the IKNP OT extension protocol (Crypto ’03) when communication is the bottleneck. We present several applications, including protocols for non-interactive zero-knowledge with bounded-reusable preprocessing from binary LPN, and concretely efficient related-key oblivious pseudorandom functions. – PCGs for simple 2-party correlations. We obtain PCGs for several other types of useful 2-party correlations, including (authenticated) one-time truth-tables and Beaver triples. While the latter PCGs are slower than our PCG for OT, they are still practically feasible. These PCGs are based on a host of assumptions and techniques, including specialized homomorphic secret sharing schemes and pseudorandom generators tailored to their structure. – Multiparty correlations. We obtain PCGs for multiparty correlations that can be used to make the circuit-dependent communication of MPC protocols scale linearly (instead of quadratically) with the number of parties.