Abusaid Manap, Gulnara Abitova, Gulzhan Uskenbayeva, Aigul Shaikhanova
In the era of pervasive digital data, ensuring secure file storage has become a paramount concern. This paper explores the significance of hybrid cryptography in the development of information technology for secure file storage. Hybrid cryptography, combining symmetric and asymmetric encryption, offers robust protection against unauthorized access, tampering, and data loss. The article reviews recent cryptography literature, highlighting the importance of secure file storage in today's interconnected world and examining the benefits of hybrid cryptography. The analysis of articles on cryptography reveals emerging trends and challenges. Post-quantum cryptography addresses concerns about quantum threats, while blockchain-based cryptography enhances security in IoT data sharing. Homomorphic encryption enables computations on encrypted data, and privacy-preserving cryptographic protocols facilitate secure multi-party computation. Machine learning's intersection with cryptanalysis introduces efficiency but raises ethical considerations. The paper further discusses advancements and trends in cryptography techniques, including post-quantum cryptography, homomorphic encryption, zero-knowledge proofs, post-quantum key exchange, secure multi-party computation, and post-quantum signature schemes. These developments aim to ensure long-term security against quantum attacks, enable privacy-preserving computations, and enhance the confidentiality, integrity, and authentication of digital communication and data storage. In conclusion, the paper advocates for the adoption of hybrid cryptography in secure file storage systems. Its combination of symmetric and asymmetric encryption, along with its adaptability to evolving security landscapes, positions hybrid cryptography as a formidable approach to data protection. By embracing hybrid cryptography and staying informed about the latest advancements, organizations can navigate the digital age with confidence, ensuring the confidentiality, integrity, and availability of stored files.
Rafael Belchior, Dimo Dimov, Zahary Karadjov, Jonas Pfannschmidt · 6 authors
The field of blockchain interoperability plays a pivotal role in blockchain adoption. Despite these advances, a notorious problem persists: the high number and success rate of attacks on blockchain bridges. We propose Harmonia, a framework for building robust, secure, efficient, and decentralized cross-chain applications. A main component of Harmonia is DendrETH, a decentralized and efficient zero-knowledge proof-based light client. DendrETH mitigates security problems by lowering the attack surface by relying on the properties of zero-knowledge proofs. The DendrETH instance of this paper is an improvement of Ethereum’s light client sync protocol that fixes critical security flaws. This light client protocol is implemented as a smart contract, allowing blockchains to read the state of the source blockchain in a trust-minimized way. Harmonia and DendrETH support several cross-chain use cases, such as secure cross-blockchain bridges (asset transfers) and smart contract migrations (data transfers), without a trusted operator. We implemented Harmonia in 9K lines of code. Our implementation is compatible with the Ethereum Virtual Machine (EVM) based chains and some non-EVM chains. Our experimental evaluation shows that Harmonia can generate light client updates with reasonable latency, costs (a dozen to a few thousand US dollars per year), and minimal storage requirements (around 4.5 MB per year). We also carried out experiments to evaluate the security of DendrETH. We provide an open-source implementation and reproducible environment for researchers and practitioners to replicate our results.
Background. To ensure the protection of the biometric access control system used in unsecured communication channels, it is necessary to exclude the storage and transfer, transfer of biometric data as well as sequences generated on their basis. The paper proposes a cryptographic protocol of two-factor authentication with the zero-knowledge over the extended field GF(2m) on elliptic curves using biometric data and the private key of the user. Objective. The aim of the article is to develop a cryptographic protocol for zero-knowledge two-factor authentication based on elliptic curves using biometric data and the user’s private key, which allows increasing cryptographic strength and reducing the duration of the authentication process. Methods. The process of implementing zero-knowledge proof protocols is as follows: one user (proofer) can convince another user (verifier) that he has some secret without disclosing the secret itself. Results. A cryptographic protocol for two-factor authentication with zero-knowledge over the extended field GF(2m) of elliptic curves using user biometric data is proposed, which significantly reduces the size of the protocol parameters and increases cryptographic strength (computational complexity of the breaking). There is no leakage of private key information and biometric data of the user during the execution of the zero-knowledge proof protocol. Conclusions. The implementation of a cryptographic protocol with zero-knowledge proof two-factor authentication based on elliptic curves allows significantly reducing the size of protocol parameters and increasing the cryptographic strength (computational complexity of the breaking).
Ірина Стрелковська, Олексій Онацький, Лариса Григорівна Йона
Background. To ensure the protection of the biometric access control system used in unsecured communication channels, it is necessary to exclude the storage and transfer, transfer of biometric data as well as sequences generated on their basis. The paper proposes a cryptographic protocol of two-factor authentication with the zero-knowledge over the extended field GF(2m) on elliptic curves using biometric data and the private key of the user. Objective. The aim of the article is to develop a cryptographic protocol for zero-knowledge two-factor authentication based on elliptic curves using biometric data and the user’s private key, which allows increasing cryptographic strength and reducing the duration of the authentication process. Methods. The process of implementing zero-knowledge proof protocols is as follows: one user (proofer) can convince another user (verifier) that he has some secret without disclosing the secret itself. Results. A cryptographic protocol for two-factor authentication with zero-knowledge over the extended field GF(2m) of elliptic curves using user biometric data is proposed, which significantly reduces the size of the protocol parameters and increases cryptographic strength (computational complexity of the breaking). There is no leakage of private key information and biometric data of the user during the execution of the zero-knowledge proof protocol. Conclusions. The implementation of a cryptographic protocol with zero-knowledge proof two-factor authentication based on elliptic curves allows significantly reducing the size of protocol parameters and increasing the cryptographic strength (computational complexity of the breaking).
With the emergence of blockchain and smart contracts, traditional digital applications 1 such as identity management, supply chain management, banking and finance etc. are expected 2 to be transformed into Decentralized Applications (DApps). Blockchain interoperability is a new 3 and exciting aspect of blockchain technology that is quickly gaining popularity in many industries. 4 However, the widespread use of blockchains has not yet been achieved because DApps running on the 5 blockchain using smart contracts require access to authentic off-chain data. Users are more conscious 6 of their personal data privacy and consequently are more reluctant to share their data, posing a 7 challenge in the adoption of DApps. To solve this challenge, we present a novel quantum-resistant, 8 data authentication and zero-knowledge proof scheme named zk-DASTARK. Based on our proposed 9 scheme, we present a novel framework which can be used to feed authenticated off-chain data to 10 DApps without compromising user data privacy. Our proposed framework is quite efficient against 11 well-known off-chain data authentication frameworks. The proposed framework is implemented on 12 state-of-the-art quantum-resistant IOTA Blockchain and is highly efficient as it takes approximately 13 60 ms to generate a proof and approximately 10 ms to verify a proof.
Yuping Yan, George Shao, Dennis Song, Mason Song · 5 authors
Blockchain transactions have gained widespread adoption across various industries, largely attributable to their unparalleled transparency and robust security features. Nevertheless, this technique introduces various privacy concerns, including pseudonymity, Sybil attacks, and potential susceptibilities to quantum computing, to name a few. In response to these challenges, innovative privacy-enhancing solutions like zero-knowledge proofs, homomorphic encryption, and stealth addresses (SA) have been developed. Among the various schemes, SA stands out as it prevents the association of a blockchain transaction's output with the recipient's public address, thereby ensuring transactional anonymity. However, the basic SA schemes have exhibited vulnerabilities to key leakage and quantum computing attacks. To address these shortcomings, we present a pioneering solution - Homomorphic Encryption-based Dual-Key Stealth Address Protocol (HE-DKSAP), which can be further extended to Fully HE-DKSAP (FHE-DKSAP). By leveraging the power of homomorphic encryption, HE-DKSAP introduces a novel approach to safeguarding transaction privacy and preventing potential quantum computing attacks. This paper delves into the core principles of HE-DKSAP, highlighting its capacity to enhance privacy, scalability, and security in programmable blockchains. Through a comprehensive exploration of its design architecture, security analysis, and practical implementations, this work establishes a privacy-preserving, practical, and efficient stealth address protocol via additively homomorphic encryption.
Peer review lies at the core of the academic process, but even well-intentioned reviewers can still provide noisy ratings. While ranking papers by average ratings may reduce noise, varying noise levels and systematic biases stemming from ``cheap'' signals (e.g. author identity, proof length) can lead to unfairness. Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information. We ask reviewers to predict others' scores and use these predictions for calibration. Assuming reviewers adjust their predictions according to the noise, we demonstrate that the calibrated score results in a more robust ranking compared to average ratings, even with varying noise levels and biases. In detail, we show that the error probability of the calibrated score approaches zero as the number of reviewers increases and is significantly lower compared to average ratings when the number of reviewers is small.
Motivated by progress in the field of zero-knowledge proofs, so-called Arithmetization-Oriented (AO) symmetric primitives have started to appear in the literature, such as MiMC, Poseidon or Rescue. Due to the design constraints implied by this setting, these algorithms are defined using simple operations over large (possibly prime) fields. In particular, many rely on simple low-degree monomials for their non-linear layers, essentially using x ↦ x3 as an S-box.In this paper, we show that the structure of the material injected in each round (be it subkeys in a block cipher or round constants in a public permutation) could allow a specific pattern, whereby a well-defined affine space is mapped to another by the round function, and then to another, etc. Such chains of one-dimensional subspaces always exist over 2 rounds, and they can be extended to an arbitrary number of rounds, for any linear layer, provided that the round-constants are well chosen.As a consequence, for several ciphers like Rescue, or a variant of AES with a monomial Sbox, there exist some round-key sequences for which the cipher has an abnormally high differential uniformity, exceeding the size of the Sbox alphabet.Well-known security arguments, in particular based on the wide-trail strategy, have been reused in the AO setting by many designers. Unfortunately, our results show that such a traditional study may not be sufficient to guarantee security. To illustrate this, we present two new primitives (the tweakable block cipher Snare and the permutation-based hash function Stir) that are built using state-of-the-art security arguments, but which are actually deeply flawed. Indeed, the key schedule of Snare ensures the presence of a subspace chain that significantly simplifies an algebraic attack against it, and the round constants of Stir force the presence of a subspace chain aligned with the rate and capacity of the permutation. This in turns implies the existence of many easy-to-find solutions to the so-called CICO problem.
Open access
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Fukang Liu, Mohammad Mahzoun, Morten Øygarden, Willi Meier
Designing novel symmetric-key primitives for advanced protocols like secure multiparty computation (MPC), fully homomorphic encryption (FHE) and zero-knowledge proof systems (ZK), has been an important research topic in recent years. Many such existing primitives adopt quite different design strategies from conventional block ciphers. Notable features include that many of these ciphers are defined over a large finite field, and that a power map is commonly used to construct the nonlinear component due to its efficiency in these applications as well as its strong resistance against the differential and linear cryptanalysis. In this paper, we target the MPC-friendly ciphers AIM and RAIN used for the post-quantum signature schemes AIMer (CCS 2023 and NIST PQC Round 1 Additional Signatures) and Rainier (CCS 2022), respectively. Specifically, we can find equivalent representations of 2-round RAIN and full-round AIM, respectively, which make them vulnerable to either the polynomial method, or the crossbred algorithm, or the fast exhaustive search attack. Consequently, we can break 2-round RAIN with the 128/192/256-bit key in only 2111/2170/2225 bit operations. For full-round AIM with the 128/192/256-bit key, we could break them in 2136.2/2200.7/2265 bit operations, which are equivalent to about 2115/2178/2241 calls of the underlying primitives. In particular, our analysis indicates that AIM does not reach the required security levels by the NIST competition.
Protecting the copyright of large language models (LLMs) has become crucial due to their resource-intensive training and accompanying carefully designed licenses. However, identifying the original base model of an LLM is challenging due to potential parameter alterations. In this study, we introduce HuRef, a human-readable fingerprint for LLMs that uniquely identifies the base model without interfering with training or exposing model parameters to the public. We first observe that the vector direction of LLM parameters remains stable after the model has converged during pretraining, with negligible perturbations through subsequent training steps, including continued pretraining, supervised fine-tuning, and RLHF, which makes it a sufficient condition to identify the base model. The necessity is validated by continuing to train an LLM with an extra term to drive away the model parameters' direction and the model becomes damaged. However, this direction is vulnerable to simple attacks like dimension permutation or matrix rotation, which significantly change it without affecting performance. To address this, leveraging the Transformer structure, we systematically analyze potential attacks and define three invariant terms that identify an LLM's base model. Due to the potential risk of information leakage, we cannot publish invariant terms directly. Instead, we map them to a Gaussian vector using an encoder, then convert it into a natural image using StyleGAN2, and finally publish the image. In our black-box setting, all fingerprinting steps are internally conducted by the LLMs owners. To ensure the published fingerprints are honestly generated, we introduced Zero-Knowledge Proof (ZKP). Experimental results across various LLMs demonstrate the effectiveness of our method. The code is available at https://github.com/LUMIA-Group/HuRef.
Ralph Ankele, Celi, Sofia, Giles, Ralph, Haddadi, Hamed
In the era of data-driven economies, incentive systems and loyalty programs, have become ubiquitous in various sectors, including advertising, retail, travel, and financial services. While these systems offer advantages for both users and companies, they necessitate the transfer and analysis of substantial amounts of sensitive data. Privacy concerns have become increasingly pertinent, necessitating the development of privacy-preserving incentive protocols. Despite the rising demand for secure and decentralised systems, the existing landscape lacks a comprehensive solution. In this work, we propose the BOOMERANG protocol, a novel decentralised privacy-preserving incentive protocol that leverages cryptographic black box accumulators to securely and privately store user interactions within the incentive system. Moreover, the protocol employs zero-knowledge proofs to transparently compute rewards for users, ensuring verifiability while preserving their privacy. To further enhance public verifiability and transparency, we utilise a smart contract on a Layer 1 blockchain to verify these zero-knowledge proofs. The careful combination of black box accumulators and zero-knowledge proofs makes the BOOMERANG protocol highly efficient.
With the continuous development of Internet of vehicles (IoV) services and the continuous influx of network users, how to solve the security and user privacy protection problems of IoV system has become a top priority. In this regard, based on the in-depth analysis and research of the existing cryptographic theory and anonymous authentication scheme, aiming at the key issues in the security architecture of IoV based on blockchain technology, we propose an anonymous authentication scheme for IoV based on private blockchain. We design a private blockchain-based anonymous authentication scheme for IoV. With the help of the non-tampering characteristics of blockchain, through the decentralized and distributed public key infrastructure (PKI), the registration pressure under high load is effectively shared. In addition, based on the system architecture of distributed authentication, we also design the anonymous identity update stage of the vehicle through the form of hybrid chain, which realizes the continuous tracking and tamper resistance of the vehicle anonymous identity binding information. Finally, through the security analysis and the implementation of the simulation system, we prove that the scheme meets the security requirements and effectively improves the efficiency of the anonymous authentication process.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Lennart Braun, Cyprien Delpech de Saint Guilhem, Robin Jadoul, Emmanuela Orsini · 6 authors
In this work, we extend the MPC-in-the-Head framework, used in recent efficient zero-knowledge protocols, to work over the ring $$\mathbb {Z}_{2^k}$$ , which is the primary operating domain for modern CPUs. The proposed schemes are compatible with any threshold linear secret sharing scheme and draw inspiration from MPC protocols adapted for ring operations. Additionally, we explore various batching methodologies, leveraging Shamir’s secret sharing schemes and Galois ring extensions, and show the applicability of our approach in RAM program verification. Finally, we analyse different options for instantiating the resulting ZK scheme over rings and compare their communication costs.
Michael Eckel, Dominik Roy George, Björn Grohmann, Christoph Krauß
Trusted Platform Modules (TPMs) are used for remote attestation to ensure the authenticity and integrity of software running on a computer system. However, measuring software executed as containers or virtual machines can be challenging as it is measured concurrently, resulting in a jumbled measurement log that is difficult to disentangle. Moreover, disclosing the entire measurement log in traditional binary remote attestation raises privacy and intellectual property concerns. To address these issues, we propose a remote attestation method with constrained disclosure, allowing for selective disclosure of entries in the measurement log using a non-interactive zero-knowledge (NIZK) proof with Schnorr signatures. Our approach is evaluated for security and privacy and proven to be correct, sound, and satisfies the properties of a NIZK proof. Formal verification of our solution with ProVerif also supports our claims. Furthermore, the performance evaluation of our proof-of-concept implementation shows that our contribution is feasible, and the overhead introduced is negligible.
I took a close look at what a blockchain-based compliance layer might actually do for U.S. payment systems using zero-knowledge proofs (ZKPs) as the key ingredient. The study lays out a conceptual model, grounded in current research, and then walks through different architectural options so you can see the trade-offs. The big takeaway? ZKPs appear capable of meeting core U.S. AML and KYC obligations without forcing people to hand over more personal details than necessary. Frankly, that's kind of the point: keep compliance intact, but avoid needless data exposure. The analysis also suggests that a ZKP-enabled layer could smooth regulatory workflows, cut down on operational risk and costs, and perhaps most importantly drastically reduce how much sensitive information gets shared across payment networks. It's promising, if a bit early-stage, but worth paying attention to.
Federated learning (FL) has been widely adopted in various fields of study and business. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years. With the help of blockchains, they attempt to achieve more integrity and efficiency. However, privacy preservation remains an uncovered aspect of these systems. To tackle this, as well as to scale the blockchain-based computations, we propose a zero-knowledge proof (ZKP)-based aggregator (zkDFL). This allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. Additionally, the server can prove that all inputs from clients have been used. We evaluate our approach using a public dataset related to the wearable Internet of Things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of the correctness of the aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has significantly declined.
With the rising use of aircrafts for operations ranging from disaster-relief to warfare, there is a growing risk of adversarial attacks. Malicious entities often only require the location of the aircraft for these attacks. Current satellite-aircraft communication and tracking protocols put aircrafts at risk if the satellite is compromised, due to computation being done in plaintext. In this work, we present \texttt{SPAM}, a private, secure, and accurate system that allows satellites to efficiently manage and maintain tracking angles for aircraft fleets without learning aircrafts' locations. \texttt{SPAM} is built upon multi-party computation and zero-knowledge proofs to guarantee privacy and high efficiency. While catered towards aircrafts, \texttt{SPAM}'s zero-knowledge fleet management can be easily extended to the IoT, with very little overhead.
We show the following unconditional results on quantum commitments in two related yet different models: 1. We revisit the notion of quantum auxiliary-input commitments introduced by Chailloux, Kerenidis, and Rosgen (Comput. Complex. 2016) where both the committer and receiver take the same quantum state, which is determined by the security parameter, as quantum auxiliary inputs. We show that computationally-hiding and statistically-binding quantum auxiliary-input commitments exist unconditionally, i.e., without relying on any unproven assumption, while Chailloux et al. assumed a complexity-theoretic assumption, ${\bf QIP}\not\subseteq{\bf QMA}$. On the other hand, we observe that achieving both statistical hiding and statistical binding at the same time is impossible even in the quantum auxiliary-input setting. To the best of our knowledge, this is the first example of unconditionally proving computational security of any form of (classical or quantum) commitments for which statistical security is impossible. As intermediate steps toward our construction, we introduce and unconditionally construct post-quantum sparse pseudorandom distributions and quantum auxiliary-input EFI pairs which may be of independent interest. 2. We introduce a new model which we call the common reference quantum state (CRQS) model where both the committer and receiver take the same quantum state that is randomly sampled by an efficient setup algorithm. We unconditionally prove that there exist statistically hiding and statistically binding commitments in the CRQS model, circumventing the impossibility in the plain model. We also discuss their applications to zero-knowledge proofs, oblivious transfers, and multi-party computations.