International Association
for Cryptologic Research

In CRYPTO'19, Gohr proposed a new cryptanalysis strategy using machine learning algorithms. Combining the differential-neural distinguisher with a differential path and integrating the advanced key recovery procedure, Gohr achieved a 12-round key recovery attack on Speck32/64. Chen and Yu improved prediction accuracy of differential-neural distinguisher considering derived features from multiple-ciphertext pairs instead of single-ciphertext pairs. By modifying the kernel size of initial convolutional layer to capture more dimensional information, the prediction accuracy of differential-neural distinguisher can be improved for for three reduced symmetric ciphers. For DES, we improve the prediction accuracy of (5-6)-round differential-neural distinguisher and train a new 7-round differential-neural distinguisher. For Chaskey, we improve the prediction accuracy of (3-4)-round differential-neural distinguisher. For PRESENT, we improve the prediction accuracy of (6-7)-round differential-neural distinguisher. The source codes are available in https://drive.google.com/drive/folders/1i0RciZlGZsEpCyW-wQAy7zzJeOLJNWqL?usp=sharing.

Attribute based encryption (ABE) is a cryptographic technique allowing fine-grained access control by enabling one-to-many encryption. Existing ABE constructions suffer from at least one of the following limitations. First, single point of failure on security meaning that, once an authority is compromised, an adversary can either easily break the confidentiality of the encrypted data or effortlessly prevent legitimate users from accessing data; second, the lack of user and/or attribute revocation mechanism achieving forward secrecy; third, a heavy computation workload is placed on data user; last but not least, the lack of adaptive security in standard models.

In this paper, we propose the first single-point-of-failure free multi-authority ciphertext-policy ABE that simultaneously (1) ensures robustness for both decryption key issuing and access revocation while achieving forward secrecy; (2) enables outsourced decryption to reduce the decryption overhead for data users that have limited computational resources; and (3) achieves adaptive (full) security in standard models. The provided theoretical complexity comparison shows that our construction introduces linear storage and computation overheads that occurs only once during its setup phase, which we believe to be a reasonable price to pay to achieve all previous features.

This paper explores a modular design architecture aimed at helping blockchains (and other SMR implementation) to scale to a very large number of processes. This comes in contrast to existing monolithic architectures that interleave transaction dissemination, ordering, and execution in a single functionality. To achieve this we first split the monolith to multiple layers which can use existing distributed computing primitives. The exact specification of the data dissemination are formally defined by the Proof of Availability & Retrieval (PoA&R) abstraction. Solutions to the PoA&R problem contain two related sub-protocols: one that ``pushes'' information into the network and another that ``pulls'' this information. Regarding the latter, there is a dearth of research literature which is rectified in this paper. We present a family of pulling sub-protocols and rigorously analyzing them. Extensive simulations support the theoretical claims of efficiency and robustness in case of a very large number of players. Finally, actual implementation and deployment on a small number of machines (roughly the size of several industrial systems) demonstrates the viability of the architecture's paradigm.

We present a novel compiler for transforming arbitrary, passively secure MPC protocols into efficient protocols with covert security and public verifiability in the honest majority setting. Our compiler works for protocols with any number of parties > 2 and treats the passively secure protocol in a black-box manner.

In multi-party computation (MPC), covert security provides an attractive trade-off between the security of actively secure protocols and the efficiency of passively secure protocols. In this security notion, honest parties are only required to detect an active attack with some constant probability, referred to as the deterrence rate. Extending covert security with public verifiability additionally ensures that any party, even an external one not participating in the protocol, is able to identify the cheaters if an active attack has been detected.

Recently, Faust et al. (EUROCRYPT 2021) and Scholl et al. (Pre-print 2021) introduced similar covert security compilers based on computationally expensive time-lock puzzles. At the cost of requiring an honest majority, our work avoids the use of time-lock puzzles completely. Instead, we adopt a much more efficient publicly verifiable secret sharing scheme to achieve a similar functionality. This obviates the need for a trusted setup and a general-purpose actively secure MPC protocol. We show that our computation and communication costs are orders of magnitude lower while achieving the same deterrence rate.

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability.

We present UnTraceable Transactions (UTT), a system for decentralized ecash with accountable privacy. UTT is the first ecash system that obtains three critical properties: (1) it provides decentralized trust by implementing the ledger, bank, auditor, and registration authorities via threshold cryptography and Byzantine Fault Tolerant infrastructure; (2) it balances accountability and privacy by implementing anonymity budgets: users can anonymously send payments, but only up to a limited amount of currency per month. Past this point, transactions can either be made public or subjected to customizable auditing rules; (3) by carefully choosing cryptographic building blocks and co-designing the cryptography and decentralization, UTT is tailored for high throughput and low latency. With a combination of optimized cryptographic building blocks and vertical scaling (optimistic concurrency control), UTT can provide almost 1,000 payments with accountable privacy per second, with latencies of around 100 milliseconds and less. Through horizontal scaling (multiple shards), UTT can scale to tens of thousands of such transactions per second. With 60 shards we measure over 10,000 transactions with accountable privacy per second.

We formally define and prove the security of UTT using an MPC-style ideal functionality. Along the way, we define a new MPC framework that captures the security of reactive functionalities in a stand-alone setting, thus filling an important gap in the MPC literature. Our new framework is compatible with practical instantiations of cryptographic primitives and provides a trade-off between concrete efficiency and provable security that may be also useful for future work.

The stock markets have two primary functions, that of providing liquidity and price discovery. While the market micro-structure was mostly ignored or assumed to function ideally for the purpose of asset pricing, O'Hara (Journal of Finance, 2003) has established that both liquidity and price discovery affect asset pricing, and in particular asset returns. Easley and O'Hara (Journal of Finance 2004) have demonstrated that informed investors' private information is not reflected efficiently in price discovery. We argue that the periodic price discovery has both positive and negative consequences for asset returns. In particular, the inefficient reflection of investors' information during price discovery incentivizes them to conduct research. However, this requires that the auctioneer be ideal or fully trusted. In this work we propose using cryptography, and in particular multi-party secure computation, to setup a novel stock market structure that, to a large extent, removes the negative consequences of liquidity costs and periodic price discovery, as well as incentivizes investors to conduct research. Interestingly, the proposed market structure takes us back to the early days of stock markets, i.e. periodic call markets, but with the not so ``trusted'' auctioneer replaced by a decentralized set of parties where no individual party (or small coalition) gets to know the order book.

The increasing use of blockchain-based cryptocurrencies like Bitcoin has run into inherent scalability limitations of blockchains. Payment channel networks, or PCNs, promise to greatly increase scalability by conducting the vast majority of transactions outside the blockchain while leveraging it as a final settlement protocol. Unfortunately, first-generation PCNs have significant privacy flaws. In particular, even though transactions are conducted off-chain, anonymity guarantees are very weak.

In this work, we present Astrape, a novel PCN construction that achieves strong security and anonymity guarantees with simple, black-box cryptography, given a blockchain with flexible scripting. Existing anonymous PCN constructions often integrate with specific, often custom-designed, cryptographic constructions. But at a slight cost to asymptotic performance, Astrape can use any generic public-key signature scheme and any secure hash function, modeled as a random oracle, to achieve strong anonymity, by using a unique construction reminiscent of onion routing. This allows Astrape to achieve provable security that is "generic" over the computational hardness assumptions of the underlying primitives. Astrape's simple cryptography also lends itself to more straightforward security proofs compared to existing systems. Furthermore, we evaluate Astrape's performance, including that of a concrete implementation on the Bitcoin Cash blockchain. We show that despite worse theoretical time complexity compared to state-of-the-art systems that use custom cryptography, Astrape operations on average have a very competitive performance of less than 10 milliseconds of computation and 1 KB of communication on commodity hardware. Astrape explores a new avenue to secure and anonymous PCNs that achieves similar or better performance compared to existing solutions.

End-to-end encryption (E2EE) is vitally important to security and privacy online, yet currently under-defined. In this note, we map intuitive notions of end-to-end encryption to existing notions of encryption. In particular, we introduce the notion of endness as an notion which end-to-end systems must achieve in addition to traditional security notions associated with encryption, and provide formalizations to capture practical requirements. We demonstrate how the notion of encryption plus endness relates to a variety of case studies that either meet normative security understanding of E2EE or are considered normative failures. Finally, we extend these observations to authentication, and real-world authenticated channel use variants, including authenticated encryption with associated data and message franking.

This paper deals with white-box implementations of the Elliptic Curve Digital Signature Algorithm (ECDSA): First, we consider attack paths to break such implementations. In particular, we provide a systematic overview of various fault attacks, to which ECDSA white-box implementations are especially susceptible. Then, we propose different mathematical countermeasures, mainly based on masking/blinding of sensitive variables, in order to prevent or at least make such attacks more difficult. We also briefly mention some typical implementational countermeasures and their challenges in the ECDSA white-box scenario.

Our work has been initiated by the CHES challenge WhibOx Contest 2021, which consisted of designing and breaking white-box ECDSA implementations, so called challenges. We illustrate our results and findings by means of the submitted challenges and provide a comprehensive overview which challenge could be solved in which way. Furthermore, we analyze selected challenges in more details.

A multi-key fully homomorphic encryption (MKFHE) scheme allows a public server to evaluate arbitrary circuits over ciphertexts encrypted under different keys. One of the main drawbacks of MKFHE schemes is the need for a ciphertext expansion procedure prior to evaluation, which combines ciphertexts encrypted under different keys to a (much larger) ciphertext encrypted under a concatenated key. In this paper, we present a new (leveled) RLWE-based MKFHE scheme without ciphertext expansion.

In this paper, we present an efficient side-channel key recovery attack against Dumbo, the 160-bit variant of NIST lightweight cryptography contest candidate Elephant. We use Correlation Power Analysis to attack the first round of the Spongent permutation during the absorption of the first block of associated data. The full attack runs in about a minute on a common laptop and only requires around 30 power traces to recover the entire secret key on an ARM Cortex-M4 microcontroller clocked at 7.4MHz. This is, to the best of our knoweledge, the first attack of this type presented against Elephant.

Blockchain ``on-chain'' oracles are critical to the functioning of many Decentralized Finance (DeFi) protocols. We analyze these oracles for manipulation resistance. Specifically, we analyze the cost of manipulating on-chain time-weighted average price (TWAP) oracles that use the arithmetic mean. It has been assumed that manipulating a TWAP oracle with the well-known multi-block attack is expensive and scales linearly with the length of the TWAP. We question this assumption with two novel results. First, we describe a single-block attack that works under the same setting as the multi-block attack but costs less to execute. Second, we describe a multi-block MEV (MMEV) style attack where the attacker colludes with a miner/proposer who can mine/propose two blocks in a row. This MMEV style attack makes oracle manipulation orders of magnitude cheaper than previously known attacks. In the proof-of-work setting, MMEV can be done by selfish mining even with very low shares of hashpower.

In 2002, Chow et al. initiated the formal study of white-box cryptography and introduced the CEJO framework. Since then, various white-box designs based on their framework have been proposed, all of them broken. Ranea and Preneel proposed a different method in 2020, called self-equivalence encodings and analyzed its security for AES. In this paper, we apply this method to generate the first academic white-box Speck implementations using self-equivalence encodings. Although we focus on Speck in this work, our design could easily be adapted to protect other add-rotate-xor (ARX) ciphers. Then, we analyze the security of our implementation against key-recovery attacks. We propose an algebraic attack to fully recover the master key and external encodings from a white-box Speck implementation, with limited effort required. While this result shows that the linear and affine self-equivalences of self-equivalence encodings are insecure, we hope that this negative result will spur additional research in higher-degree self-equivalence encodings for white-box cryptography. Finally, we created an open-source Python project implementing our design, publicly available at https://github.com/jvdsn/white-box-speck. We give an overview of five strategies to generate output code, which can be used to improve the performance of the white-box implementation. We compare these strategies and determine how to generate the most performant white-box Speck code. Furthermore, this project could be employed to test and compare the efficiency of attacks on white-box implementations using self-equivalence encodings.

We cryptanalyse the SHealS and HealS cryptosystems of Fouotsa and Petit from Asiacrypt 2021.

We proposed three general frameworks F1,F2, and F3 for n-to-n-bit PRFs with one, two parallel, and two serial public permutation calls respectively, where every permutation is preceded and followed by any bitwise linear mappings. We analyze them in the Q2 model where attackers have quantum-query access to PRFs and permutations. Our results show F1 is not secure with O(n) quantum queries while its PRFs achieve n/2-bit security in the classical setting, and F2,F3 are not secure with O(2^{n/2}n) quantum queries while their PRFs, such as SoEM, PDMMAC, and pEDM, achieve 2n/3-bit security in the classical setting. Besides, we attack three general instantiations XopEM, EDMEM, and EDMDEM of F2,F3, which derive from replacing the two PRPs in Xop, EDM, and EDMD with two independent EM constructions, and concrete PRF instantiations DS-SoEM, PDMMAC, and pEDM, SoKAC21 of F2,F3, with at most O(2^{n/2}n) quantum queries.

In this paper, we extend Inner-Product Functional Encryption (IPFE), where there is just a vector in the key and a vector in the single sender's ciphertext, to two-client ciphertexts. More precisely, in our two-client functional encryption scheme, there are two Data Providers who can independently encrypt vectors $\mathbf{x}$ and $\mathbf{y}$ for a data consumer who can, from a functional decryption key associated to a vector $\mathbf{\alpha}$, compute $\sum \alpha_i x_i y_i = \mathbf{x} \cdot \mathsf{Diag}(\mathbf{\alpha}) \cdot \mathbf{y}^\top$. Ciphertexts are linear in the dimension of the vectors, whereas the functional decryption keys are of constant size.

We study two interesting particular cases: - 2-party Inner-Product Functional Encryption, with $\mathbf{\alpha}= (1,\ldots,1)$. There is a unique functional decryption key, which enables the computation of $\mathbf{x}\cdot \mathbf{y}^\top$ by a third party, where $\mathbf{x}$ and $\mathbf{y}$ are provided by two independent clients; - Inner-Product Functional Encryption with a Selector, with $\mathbf{x}= \mathbf{x}_0 \| \mathbf{x}_1$ and $\mathbf{y}= \bar{b}^n \| b^n \in \{ 1^n \| 0^n, 0^n \| 1^n \}$, for some bit $b$, on the public coefficients $\mathbf{\alpha} = \mathbf{\alpha}_0 \| \mathbf{\alpha}_1$, in the functional decryption key, so that one gets $\mathbf{x}_b \cdot \mathbf{\alpha}_b^\top$, where $\mathbf{x}$ and $b$ are provided by two independent clients.

This result is based on the fundamental Product-Preserving Lemma, which is of independent interest. It exploits Dual Pairing Vector Spaces (DPVS), with security proofs under the \mathsf{SXDH} assumption. We provide two practical applications to medical diagnosis for the latter IPFE with Selector, and to money-laundering detection for the former 2-party IPFE, both with strong privacy properties, with adaptative security and the use of labels granting a Multi-Client Functional Encryption (MCFE) security for the scheme, thus enabling its use in practical situations.

Cache side-channel attacks allow adversaries to learn sensitive information about co-running processes by using only access latency measures and cache contention. This vulnerability has been shown to lead to several microarchitectural attacks. As a promising solution, recent work proposes Randomization-based Protected Caches (RPCs). RPCs randomize cache addresses, changing keys periodically so as to avoid long-term leakage. Unfortunately, recent attacks have called the security of state-of-the-art RPCs into question.

In this work, we tackle the problem of formally defining and analyzing the security properties of RPCs. We first give security definitions against access-based cache side-channel attacks that capture security against known attacks such as Prime+Probe and Evict+Probe. Then, using these definitions, we obtain results that allow to guarantee security by adequately choosing the rekeying period, the key generation algorithm and the cache randomizer, thus providing security proofs for RPCs under certain assumptions.

Conventional wisdom purports that FFT-based integer multiplication methods (such as the Schönhage-Strassen algorithm) begin to compete with Karatsuba and Toom-Cook only for integers of several tens of thousands of bits. In this work, we challenge this belief: Leveraging recent advances in the implementation of Number-Theoretic Transforms (NTT) stimulated by their use in Post-Quantum Cryptography, we report on implementations of NTT-based integer arithmetic on two Arm Cortex-M CPUs on opposite ends of the performance spectrum: Cortex-M3 and Cortex-M55. Our results indicate that NTT-based multiplication is capable of outperforming the big-number arithmetic implementations of popular embedded cryptography libraries for integers as small as 2048 bits. To provide a realistic case study, we benchmark implementations of the RSA encryption and decryption operations. Between Cortex-M3 and Cortex-M55, we observe a $\approx10\times$ performance improvement.

Would you like to have your impact on the elderly care? Gaining a PhD along the way? We are delighted to be offering the opportunities for PhD studentship at University of Plymouth, United Kingdom in the scope of the project “Privacy-preserving IoT-assisted Elderly Monitoring for Smart Health Community” (PEM)(https://lnkd.in/dBBQtaUp) and its collaborated project “Harnessing Wearables for Protection” (https://lnkd.in/dd4R3MXZ). The focus of the research (PEM) is to create privacy-preserving anomaly detection service for IoT and cloud computing and promote its use for the elderly care. The studentship is supported for 3.5 years and includes full Home tuition fees (United Kingdom) plus a stipend of £16,062.00 per annum (2022/23 rate). Applicants should have a first or upper second class honours degree in an appropriate subject and preferably a relevant Masters qualification. Prospective applicant should have a mathematical inclination, good knowledge of applied cryptography, good development skills, problem-solving skills and an ability to work independently, interpersonal and collaborative skills.

Closing date for applications:

Contact: Dr. Hai-Van Dang

More information: https://www.plymouth.ac.uk/student-life/your-studies/research-degrees/postgraduate-research-studentships/privacy-preserving-iot-assisted-elderly-monitoring-for-smart-health-community