International Association
for Cryptologic Research

In blockchain space, there are elaborate proof-of-stake based protocols with some assumptions related to clock synchronization, i.e.~that all of them know the current time of the protocol. However, this assumption is satisfied by relying on the security of centralized systems such as Network Time Protocol (NTP) or Global Positioning System (GPS). Clearly, any attack on these systems (which happened in the past) can cause corruption of blockchains that rely on the clock that they provide. To solve this problem in the nature of the decentralized network, we first define a general universally composable (GUC) model that captures the notion of consensus on a clock. Simply, a consensus clock is a clock that is agreed upon by honest parties by considering the clocks of all parties. In the end, we give a simple but useful protocol relying on a blockchain network. Our protocol is secure according to our new model. It can be used by full nodes of a blockchain who need to have a common time notion to preserve the correctness and the security of the blockchain protocol. One advantage of our protocol is that it does not cause any extra communication overhead on the underlying blockchain protocol.

This work studies message authentication code (MAC) schemes supporting variable tag lengths. We provide a formalisation of such a scheme. Several variants of the classical Wegman-Carter MAC scheme are considered. Most of these are shown to be insecure by pointing out detailed attacks. One of these schemes is highlighted and proved to be secure. We further build on this scheme to obtain single-key nonce-based variable tag length MAC schemes utilising either a stream cipher or a short-output pseudo-random function. These schemes can be efficiently instantiated using practical well known primitives. We further consider the problem of building variable tag length MAC schemes without nonces. Again, efficient constructions of such schemes are described along with their proofs of security.

With the proliferation of data and emerging data-driven applications, how to perform data analytical operations while respecting privacy concerns has become a very interesting research topic. With the advancement of communication and computing technologies, e.g. the FoG computing concept and its associated Edge computing technologies, it is now appealing to deploy decentralized data-driven applications. Following this trend, in this paper, we investigate privacy-preserving singular value decomposition (SVD) solutions tailored for these new computing environments. We first analyse a privacy-preserving SVD solution by Chen et al., which is based on the Paillier encryption scheme and some heuristic randomization method. We show that (1) their solution leaks statistical information to an individual player in the system; (2) their solution leaks much more information when more than one players collude. Based on the analysis, we present a new solution, which distributes the SVD results into two different players in a privacy-preserving manner. In comparison, our solution minimizes the information leakage to both individual player and colluded ones, via randomization and threshold homomorphic encryption techniques.

The cloud changed the way we manage and store data. Today, cloud storage services offer clients an infrastructure that allows them a convenient source to store, replicate, and secure data online. However, with these new capabilities also come limitations, such as lack of transparency, limited decentralization, and challenges with privacy and security. And, as the need for more agile, private and secure data solutions continues to grow exponentially, rethinking the current structure of cloud storage is mission-critical for enterprises. By leveraging and building upon blockchain’s unique attributes, including immutability, security to the data element level, distributed (no single point of failure), we have developed a solution prototype that allows data to be reliably stored while simultaneously being secured, with tamper-evident auditability, via blockchain. The result, Audita, is a flexible solution that assures data protection and solves for challenges such as scalability and privacy. Audita works via an augmented blockchain network of participants that include storage-nodes and block-creators. In addition, it provides an automatic and fair challenge system to assure that data is distributed and reliably and provably stored. While the prototype is built on Quorum, the solution framework can be used with any blockchain platform. The benefit is a system that is built to grow along with the data needs of enterprises, while continuing to build the network via incentives and solving for issues such as auditing, outsourcing and malicious users.

In the setting of secure multiparty computation (MPC), a set of mutually distrusting parties wish to jointly compute a function, while guaranteeing the privacy of their inputs and the correctness of the output. An MPC protocol is called fully secure if no adversary can prevent the honest parties from obtaining their outputs. A protocol is called fair if an adversary can prematurely abort the computation, however, only before learning any new information.

We present highly efficient transformations from fair computations to fully secure computations, assuming the fraction of honest parties is constant (e.g., 1% of the parties are honest). Compared to previous transformations that require linear invocations (in the number of parties) of the fair computation, our transformations require super-logarithmic, and sometimes even super-constant, such invocations. The main idea is to delegate the computation to chosen random committees that invoke the fair computation. Apart from the benefit of uplifting security, the reduction in the number of parties is also useful, since only committee members are required to work, whereas the remaining parties simply "listen" to the computation over a broadcast channel.

One application of these transformations is a new $\delta$-bias coin-flipping protocol, whose round complexity has a super-logarithmic dependency on the number of parties, improving over the protocol of Beimel, Omri, and Orlov (Crypto 2010) that has a linear dependency. A second application is a new fully secure protocol for computing the Boolean OR function, with a super-constant round complexity, improving over the protocol of Gordon and Katz (TCC 2009) whose round complexity is linear in the number of parties.

Finally, we show that our positive results are in a sense optimal, by proving that for some functionalities, a super-constant number of (sequential) invocations of the fair computation is necessary for computing the functionality in a fully secure manner.

Kyber is a KEM based their security on the Modular Learning with Errors problem and was selected in the second round of NIST Post-quantum standardization process. Before we put Kyber into practical application, it is very important to assess its security in hard practical conditions especially when the Fujisaki-Okamoto transformations are neglected. In this paper, we propose an efficient key mismatch attacks on Kyber, which can recover one participant's secret key if the public key is reused. We first define the oracles in which the adversary is able to launch the attacks. Then, we show that by accessing the oracle multiple times, the adversary is able to recover the coefficients in the secret key. Furthermore, we propose two strategies to reduce the queries and time in recovering the secret key. It turns out that it is actually much easier to use key mismatch attacks to break Kyber than NewHope, another NIST second round candidate, due to their different design structures. Our implementations have demonstrated the efficiency of the proposed attacks and verified our findings. Another interesting observation from the attack is that in the most powerful Kyber-1024, it is easier to recover each coefficient compared with that in Kyber-512 and Kyber-768. Specifically, for Kyber-512 on average we recover each coefficient with $2.7$ queries, while in Kyber-1024 and 768, we only need $2.4$ queries. This demonstrates further that implementations of LWE based schemes in practice is very delicate.

Wildcard identity-based encryption (WIBE) allows a sender to simultaneously encrypt messages to a group of users matching a certain pattern, defined as a sequence of identifiers and wildcards. We propose a novel scalable wildcarded identity-based encryption, called SWIBE, which reduces the ciphertext size to be constant. To the best of our knowledge, SWIBE is the first wildcard identity-based encryption scheme that generates a constant size ciphertext regardless of the depth of the identities with fast decryption. The proposed scheme improves the decryption time. According to our experiment results, decryption of the SWIBE scheme is 3, 10, and 650 times faster than existing WIBE, WW-IBE, and CCP-ABE schemes. The SWIBE scheme also subsumes the generalized key derivation naturally by allowing wildcards in the key delegation process. We prove CPA security of the proposed scheme and extend it to be CCA secure.

We present the first maliciously secure protocol for succinct non-interactive secure two-party computation (SNISC): Each player sends just a single message whose length is (essentially) independent of the running time of the function to be computed. The protocol does not require any trusted setup, satisfies superpolynomial-time simulation-based security (SPS), and is based on (subexponential) security of the Learning With Errors (LWE) assumption. We do not rely on SNARKs or "knowledge of exponent"-type assumptions.

Since the protocol is non-interactive, the relaxation to SPS security is needed, as standard polynomial-time simulation is impossible; however, a slight variant of our main protocol yields a SNISC with polynomial-time simulation in the CRS model.

Generating secret shares of a shuffled dataset - such that neither party knows the order in which it is permuted - is a fundamental building block in many protocols, such as secure collaborative filtering, oblivious sorting, and secure function evaluation on set intersection. Traditional approaches to this problem either involve expensive public-key based crypto or using symmetric crypto on permutation networks. While public-key based solutions are bandwidth efficient, they are computation-bound. On the other hand, permutation network based constructions are communication-bound, especially when the elements are long, for example feature vectors in an ML context.

We design a new 2-party protocol for this task of computing secret shares of shuffled data, which we refer to as secret-shared shuffle. Our protocol is secure against static semi-honest adversary.

At the heart of our approach is a new method of obtaining two sets of pseudorandom shares which are ``correlated via the permutation'', which can be implemented with low communication using GGM puncturable PRFs. This gives a new protocol for secure shuffle which is concretely more efficient than the existing techniques in the literature. In particular, we are three orders of magnitude faster than public key based approach and one order of magnitude faster compared to the best known symmetric-key cryptography approach based on permutation network when the elements are moderately large.

We revisit the well-studied problem of extracting nearly uniform randomness from an arbitrary source of sufficient min-entropy. Strong seeded extractors solve this problem by relying on a public random seed, which is unknown to the source. Here, we consider a setting where the seed is reused over time and the source may depend on prior calls to the extractor with the same seed. Can we still extract nearly uniform randomness?

In more detail, we assume the seed is chosen randomly, but the source can make arbitrary oracle queries to the extractor with the given seed before outputting a sample. We require that the sample has entropy and differs from any of the previously queried values. The extracted output should look uniform even to a distinguisher that gets the seed. We consider two variants of the problem, depending on whether the source only outputs the sample, or whether it can also output some correlated public auxiliary information that preserves the sample's entropy. Our results are:

* Without Auxiliary Information: We show that every pseudo-random function (PRF) with a sufficiently high security level is a good extractor in this setting, even if the distinguisher is computationally unbounded. We further show that the source necessarily needs to be computationally bounded and that such extractors imply one-way functions.

* With Auxiliary Information: We construct secure extractors in this setting, as long as both the source and the distinguisher are computationally bounded. We give several constructions based on different intermediate primitives, yielding instantiations based on the DDH, DLIN, LWE or DCR assumptions. On the negative side, we show that one cannot prove security against computationally unbounded distinguishers in this setting under any standard assumption via a black-box reduction. Furthermore, even when restricting to computationally bounded distinguishers, we show that there exist PRFs that are insecure as extractors in this setting and that a large class of constructions cannot be proven secure via a black-box reduction from standard assumptions.

We construct new protocols for two parties to securely compute on the items in their intersection. Our protocols make use of an untrusted third party that has no input. The use of this party allows us to construct highly efficient protocols that are secure against a single malicious corruption.

The first construction of Witness Encryption (WE) by Garg et al. (STOC 2013) has led to many exciting avenues of research in the past years.A particularly interesting variant is Offline WE (OWE) by Abusalah et al. (ACNS 2016), as the encryption algorithm uses neither obfuscation nor multilinear maps.

Current OWE schemes provide only selective security. That is, the adversary must commit to their challenge messages $m_0$ and $m_1$ before seeing the public parameters.We provide a new, generic framework to construct OWE, which achieves adaptive security in the sense that the adversary may choose their challenge messages adaptively. We call this semi-adaptive security, because - as in prior work - the instance of the considered NP language that is used to create the challenge ciphertext must be fixed before the parameters are generated in the security proof. We show that our framework gives the first OWE scheme with constant ciphertext overhead even for messages of polynomially-bounded size. We achieve this by introducing a new variant of puncturable encryption defined by Green and Miers (S&P 2015) and combining it with the iO-based approach of Abusalah et al. Finally, we show that our framework can be easily extended to construct the first Extractable Offline Witness Encryption (EOWE), by using extractability obfuscation of Boyle et al. (TCC 2014) in place of iO, opening up even more possible applications.

The obfuscation is needed only for our public parameters, but its functionality can be realised with a Trusted Execution Environment (TEE), which means we have a very efficient scheme with ciphertexts consisting of only 5 group elements.

We give an overview of our critiques of “proofs” of security and a guide to our papers on the subject that have appeared over the past decade and a half. We also provide numerous additional examples and a few updates and errata.

\textit{Verifiable random functions} (VRFs) are essentially digital signatures with additional properties, namely \textit{verifiable uniqueness} and \textit{pseudorandomness}, which make VRFs a useful tool, e.g., to prevent enumeration in DNSSEC Authenticated Denial of Existence and the CONIKS key management system, or in the random committee selection of the Algorand blockchain.

Most standard-model VRFs rely on \textit{admissible hash functions} (AHFs) to achieve security against \textit{adaptive} attacks in the standard model. Known AHF constructions are based on error-correcting codes, which yield \textit{asymptotically} efficient constructions. However, previous works do not clarify how the code should be instantiated \textit{concretely} in the real world. The \textit{rate} and the \textit{minimal distance} of the selected code have significant impact on the efficiency of the resulting cryptosystem, therefore it is unclear if and how the aforementioned constructions can be used in practice.

First, we explain inherent limitations of code-based AHFs. Concretely, we show that even if we were given codes that achieve the well-known Gilbert-Varshamov or McEliece-Rodemich-Rumsey-Welch bounds, existing AHF-based constructions of verifiable random functions (VRFs) can only be instantiated quite inefficiently. Then we introduce and construct \textit{computational} AHFs (cAHFs). While classical AHFs are information-theoretic, and therefore work even in presence of computationally unbounded adversaries, cAHFs provide only security against computationally bounded adversaries. However, we show that cAHFs can be instantiated significantly more efficiently. Finally, we present a new VRF scheme using cAHFs and show that it is currently the most efficient verifiable random function with full adaptive security in the standard model.

Machine Learning has been widely applied in practice, such as disease diagnosis, target detection. Commonly, a good model relies on massive training data collected from different sources. However, the collected data might expose sensitive information. To solve the problem, researchers have proposed many excellent methods that combine machine learning with privacy protection technologies, such as secure multiparty computation(MPC), homomorphic encryption(HE), and differential privacy. In the meanwhile, some other researchers proposed distributed machine learning which allows the clients to store their data locally but train a model collaboratively. The first kind of method focuses on security, but the performance and accuracy remain to be improved, while the second provides higher accuracy and better performance but weaker security, for instance, the adversary can launch membership attacks from the gradients' updates in plaintext. In this paper, we join secret sharing to distributed machine learning to achieve reliable performance, accuracy and high-level security. Next, we design, implement, and evaluate a practical system to jointly learn an accurate model under semi-honest and servers-only malicious adversary security, respectively. And the experiments show our protocols achieve the best overall performance as well.

Many isogeny-based cryptosystems are believed to rely on the hardness of the Supersingular Decision Diffie-Hellman (SSDDH) problem. However, most cryptanalytic efforts have treated the hardness of this problem as being equivalent to the more generic supersingular $\ell^e$-isogeny problem --- an established hard problem in number theory.

In this work, we shine some light on the possibility that the combination of two additional pieces of information given in practical SSDDH instances --- the image of the torsion subgroup, and the starting curve's endomorphism ring --- can lead to better attacks cryptosystems relying on this assumption. We show that SIKE/SIDH are secure against our techniques. However, in certain settings, e.g., multi-party protocols, our results may suggest a larger gap between the security of these cryptosystems and the $\ell^e$-isogeny problem.

Our analysis relies on the ability to find many endomorphisms on the base curve that have special properties. To the best of our knowledge, this class of endomorphisms has never been studied in the literature. We informally discuss the parameter sets where these endomorphisms should exist. We also present an algorithm which may provide information about additional torsion points under the party's private isogeny, which is of independent interest. Finally, we present a minor variation of the SIKE protocol that avoids exposing a known endomorphism ring.

We propose the first auctioneer-free sealed-bid auction protocol with a linear computation and communication complexity $O(c)$, $c$ being the bit length of the bid price. Our protocol, called Self-Enforcing Auction Lot (SEAL), operates in a decentralized setting, where bidders jointly compute the maximum bid while preserving the privacy of losing bids. In our protocol, we do not require any secret channels between participants. All operations are publicly verifiable; everyone including third-party observers is able to verify the integrity of the auction outcome. Upon learning the highest bid, the winner comes forward with a proof to prove that she is the real winner. Based on the proof, everyone is able to check if there is only one winner or there is a tie. While our main protocol works with the first-price sealed-bid, it can be easily extended to support the second-price sealed-bid (also known as the Vickrey auction), revealing only the winner and the second highest bid, while keeping the highest bid and all other bids secret. To the best of our knowledge, this work establishes to date the best computation and communication complexity for sealed-bid auction schemes without involving any auctioneer.

In this work, we formulate and investigate a pragmatic question related to practical side-channel attacks complemented with key enumeration. In a real attack scenario, after an attacker has extracted side-channel information, it is possible that despite the entropy of the key has been signicantly reduced, she cannot yet achieve a direct key recovery. If the correct key lies within a sufficiently small set of most probable keys, it can then be recovered with a plaintext and the corresponding ciphertext, by performing enumeration. Our proposal relates to the following question: how does an attacker know when to stop acquiring side-channel observations and when to start enumerating with a given computational effort? Since key enumeration is an expensive (i.e. time-consuming) task, this is an important question from an adversarial viewpoint. To answer this question, we present an efficient (heuristic) way to perform key-less rank estimation, based on simple entropy estimations using histograms.

Selling digital commodities securely over the Internet is a challenging task when Seller and Buyer do not trust each other. With the advent of cryptocurrencies, one prominent solution for digital exchange is to rely on a smart contract as a trusted arbiter that fairly resolves disputes when Seller and Buyer disagree. Such protocols have an optimistic mode, where the digital exchange between the parties can be completed with only minimal interaction with the smart contract. In this work we present OptiSwap, a new smart contract based fair exchange protocol that significantly improves the optimistic case of smart contract based fair exchange protocols. In particular, OptiSwap has almost no overhead in communication complexity, and improves on the computational overheads of the parties compared to prior solutions. An additional feature of OptiSwap is a protection mechanism against so-called grieving attacks, where an adversary attempts to violate the financial fairness of the protocol by forcing the honest party to pay fees. We analyze OptiSwap's security in the UC model and provide benchmark results over Ethereum.

Elliptic curves play a prominent role in cryptography. For instance, the hardness of the elliptic curve discrete logarithm problem is a foundational assumption in public key cryptography. Drinfeld modules are positive characteristic function field analogues of elliptic curves. It is natural to ponder the existence/security of Drinfeld module analogues of elliptic curve cryptosystems. But the Drinfeld module discrete logarithm problem is easy even on a classical computer. Beyond discrete logarithms, elliptic curve isogeny based cryptosystems have have emerged as candidates for post-quantum cryptography, including supersingular isogeny Diffie-Hellman (SIDH) and commutative supersingular isogeny Diffie-Hellman (CSIDH) protocols. We formulate Drinfeld module analogues of these elliptic curve isogeny based cryptosystems and devise classical polynomial time algorithms to break these Drinfeld analogues catastrophically.