SKENO: Secret key encryption with non-interactive opening

We present a secure and private blockchain-based Verifiable Random Function (VRF) scheme addressing some limitations of classical VRF constructions. Given the imminent quantum computing adversarial scenario, conventional cryptographic methods face vulnerabilities. To enhance our VRF’s secure randomness, we adopt post-quantum RingLWE encryption for synthesizing pseudo-random sequences. Considering computational costs and resultant on-chain gas costs, we suggest a bifurcated architecture for VRF design, optimizing interactions between on-chain and off-chain. Our approach employs a secure ring signature supported by NIZK proof and a delegated key generation method, inspired by the Chaum-Pedersen equality proof and the Fiat-Shamir Heuristic. Our VRF scheme integrates multi-party computation (MPC) with blockchain-based decentralized identifiers (DID), ensuring both security and randomness. We elucidate the security and privacy aspects of our VRF scheme, analyzing temporal and spatial complexities. We also approximate the entropy of the VRF scheme and detail its implementation in a Solidity contract. Also, we delineate a method for validating the VRF’s proof, matching for the contexts requiring both randomness and verification. Conclusively, using the NIST SP800-22 of the statistical randomness test suite, our results exhibit a 98.86% pass rate over 11 test cases, with an average 𝑝-value of 0.5459 from 176 total tests.

Cohen, Goldwasser, and Vaikuntanathan (TCC’15) introduced the concept of aggregate pseudo-random functions (PRFs), which allow efficiently computing the aggregate of PRF values over exponential-sized sets. In this paper, we explore the aggregation augmentation on verifiable random function (VRFs), introduced by Micali, Rabin and Vadhan (FOCS’99), as well as its application to e-lottery schemes. We introduce the notion of static aggregate verifiable random functions (Agg-VRFs), which perform aggregation for VRFs in a static setting. Our contributions can be summarized as follows: (1) we define static aggregate VRFs, which allow the efficient aggregation of VRF values and the corresponding proofs over super-polynomially large sets; (2) we present a static Agg-VRF construction over bit-fixing sets with respect to product aggregation based on the q-decisional Diffie–Hellman exponent assumption; (3) we test the performance of our static Agg-VRFs instantiation in comparison to a standard (non-aggregate) VRF in terms of costing time for the aggregation and verification processes, which shows that Agg-VRFs lower considerably the timing of verification of big sets; and (4) by employing Agg-VRFs, we propose an improved e-lottery scheme based on the framework of Chow et al.’s VRF-based e-lottery proposal (ICCSA’05). We evaluate the performance of Chow et al.’s e-lottery scheme and our improved scheme, and the latter shows a significant improvement in the efficiency of generating the winning number and the player verification.

We give a simple and efficient construction of a verifiable random function (VRF) on bilinear groups. Our construction is direct. In contrast to prior VRF constructions [14,15], it avoids using an inefficient Goldreich-Levin transformation, thereby saving several factors in security. Our proofs of security are based on a decisional bilinear Diffie-Hellman inversion assumption, which seems reasonable given current state of knowledge. For small message spaces, our VRF’s proofs and keys have constant size. By utilizing a collision-resistant hash function, our VRF can also be used with arbitrary message spaces. We show that our scheme can be instantiated with an elliptic group of very reasonable size. Furthermore, it can be made distributed and proactive.

Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications. As a result, continuous data integrity should be guaranteed in decentralized storage, which ensures that data is intact and available for the entire storage period. However, this requires frequent checking for long time periods and incurs heavy burdens of both communication and computation, especially in a big data scenario. In this paper, we propose an efficient continuous big data integrity checking approach for decentralized storage. We design a data-time sampling strategy that randomly checks the integrity of multiple files at each time slot with high checking probability. Furthermore, to tackle the fairness problem derived from the sampling strategy, we propose a fair approach by designing an arbitration algorithm with the verifiable random function. Security analysis shows the security of our approach under the random oracle model. Evaluation and experiments demonstrate that our approach is more efficient in the big data scenario compared with the state-of-the-arts.

In this paper, we focus on the constructions of adaptively secure identity-based encryption (IBE) from lattices and verifiable random function (VRF) with large input spaces. Existing constructions of these primitives suffer from low efficiency, whereas their counterparts with weaker guarantees (IBEs with selective security and VRFs with small input spaces) are reasonably efficient. We try to fill these gaps by developing new partitioning techniques that can be performed with compact parameters and proposing new schemes based on the idea.

In this work, we introduce the first practical post-quantum verifiable random function (VRF) that relies on well-known (module) lattice problems, namely Module-SIS and Module-LWE. Our construction, named LB-VRF, results in a VRF value of only 84 bytes and a proof of around only 5 KB (in comparison to several MBs in earlier works), and runs in about 3 ms for evaluation and about 1 ms for verification.In order to design a practical scheme, we need to restrict the number of VRF outputs per key pair, which makes our construction few-time. Despite this restriction, we show how our few-time LB-VRF can be used in practice and, in particular, we estimate the performance of Algorand using LB-VRF. We find that, due to the significant increase in the communication size in comparison to classical constructions, which is inherent in all existing lattice-based schemes, the throughput in LB-VRF-based consensus protocol is reduced, but remains practical. In particular, in a medium-sized network with 100 nodes, our platform records a 1.14\(\times \) to 3.4\(\times \) reduction in throughput, depending on the accompanying signature used. In the case of a large network with 500 nodes, we can still maintain at least 24 transactions per second. This is still much better than Bitcoin, which processes only about 5 transactions per second.KeywordsPost-quantumVerifiable random functionBlockchainLatticeAlgorand

Cryptography is a practice of technique to ensure security by using the cryptography keys. Key derivation function (KDF) is a standard algorithm to generate these cryptographic keys. Stream ciphers are one of the cryptographic primitives that are used to construct the key derivation function namely key derivation function based on stream ciphers. Though the key derivation function based on stream ciphers have a great role in security, it is necessary to have a framework which can evaluate the security level of the different types of key derivation function based on stream ciphers. Random oracle model (ROM) is the current procedure to proofs the security of KDF. However, the security evaluation of ROM did not evaluate the degree of secureness of KDF as it can only proof either the KDF is theoretically secure or insecure. Hence, this research applies fuzzy evaluation method to form a framework to evaluate the degree of secureness of the KDF for different types of key derivation function based on stream ciphers. Key sizes and complexity attacks are two main variables which are considered in the design of fuzzy rule. The proposed method introduces the information extraction to construct fuzzy membership function and rules. The result from this proposal is effective to approximate the security aspect in the computer system as well as network system.

Recent lightweight hardware-based stream cipher designs keep an external non-volatile internal state that is not part of the cipher’s hardware module. The purpose of these so-called small-state ciphers is to keep the size of the hardware and the power consumption low. We propose a random oracle model for stream ciphers. This will allow us to analyse the recent small-state stream cipher designs’ resistance against generic attacks and, in particular, time-memory-data tradeoff attacks. We analyse the conventional construction underlying stream ciphers like Grain and Trivium, constructions continuously using the external non-volatile secret key during keystream generation like Sprout, Plantlet, Fruit, and Atom, constructions continuously using the external non-volatile IV, and constructions using a combination of the IV and the key like DRACO. We show the tightness of all bounds by first presenting the time-memory-data tradeoff attacks on the respective constructions, establishing the upper bound on security, and then presenting the proof of security to establish the lower bound on security. In this work, we extend the theoretical work done by Hamann et al. who introduced the DRACO stream cipher at FSE 2023. We use the same random oracle model as the aforementioned work and apply it to the earlier work by Hamann et al. presented at SAC 2019, which showed security for two of the four constructions we consider in this work. Our model is equivalent but allows for a much simpler proof of security. Furthermore, we provide a proof of security for stream ciphers continuously using the secret key during keystream generation, giving upper and lower bounds for all four generic stream cipher constructions proposed so far.

Practical Byzantine Fault Tolerance (PBFT) is a deterministic consensus algorithm, which is widely used in blockchain due to the advantage of no forks. However, PBFT still has some problems, such as not being suitable for dynamic networks, requiring large communication overhead, and not being able to identify Byzantine nodes. Therefore, we propose Extensible-PBFT (EPBFT) consensus algorithm, it can take different steps to reach consensus according to the network environment of the system. Firstly, we add the election of consensus nodes based on verifiable random function (VRF), which makes EPBFT suitable for dynamic networks. Then, we modify the consistency protocol so that EPBFT can reach consensus with less communication in a stable network, and it can also identify Byzantine nodes and reduce confirmation latency. Finally, we simplify the checkpoint protocol and the view-change protocol, reducing the time and communication overhead they require.

Diverse technologies, such as machine learning and big data, have been driving the prosperity of the Internet of Things (IoT) and the ubiquitous proliferation of IoT devices. Consequently, it is natural that IoT becomes the driving force to meet the increasing demand for frictionless transactions. To secure transactions in IoT, blockchain is widely deployed since it can remove the necessity of a trusted central authority. However, the mainstream blockchain-based IoT payment platforms, dominated by Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms, face several major security and scalability challenges that result in system failures and financial loss. Among the three leading attacks in this scenario, double-spend attacks and long-range attacks threaten the tokens of blockchain users, while eclipse attacks target Denial of Service. To defeat these attacks, a novel bidirectional-linked blockchain (BLB) using chameleon hash functions is proposed, where bidirectional pointers are constructed between blocks. Furthermore, a new committee members auction (CMA) consensus algorithm is designed to improve the security and attack resistance of BLB while guaranteeing high scalability. In CMA, distributed blockchain nodes elect committee members through a verifiable random function. The smart contract uses Shamir's secret-sharing scheme to distribute the trapdoor keys to committee members. To better investigate BLB's resistance against double-spend attacks, an improved Nakamoto's attack analysis is presented. In addition, a modified entropy metric is devised to measure eclipse attack resistance across different consensus algorithms. Extensive evaluation results show the superior resistance against attacks and demonstrate high scalability of BLB compared with current leading paradigms based on PoS and PoW.

Randomness is a critical issue in peer-to-peer networks because random numbers allow us to fairly select the candidates, resolve the lotteries, select block producers, etc. The difficulty is that each participant wants to verify that the random number was randomly generated, this led to the invention of verifiable random functions (VRF). The VRF is a pseudo-random function that provides a solution to blockchain-based random number generation. This paper focuses on the implementation of an elliptic curve-based VRF introduced by NIST called ECVRF-secp256r1 in Solidity. The algorithm verifies the randomness on-chain, tests the gas consumption at each stage, and compares it to the ECVRF-secp256k1.

Federated Learning, as a multi-party machine learning paradigm, has garnered significant attention, but model updates may still leak sensitive information. Secure aggregation protocols are considered an effective solution for privacy protection in Federated Learning. However, in large-scale federated learning systems, designing efficient and practical secure aggregation remains a critical challenge. Moreover, while secure aggregation effectively conceals model updates, it unintentionally complicates the detection and mitigation of poisoning attacks, thereby exposing the system to vulnerabilities from both data and model poisoning. To address these challenges, we propose ClusterGuard, a secure clustered aggregation scheme. ClusterGuard leverages Verifiable Random Function (VRF) to ensure fair and transparent client clustering. Within each cluster, it employs a lightweight key-homomorphic masking mechanism combined with verifiable secret sharing to enable secure and efficient aggregation. Fur thermore, we design a dual filtering mechanism based on cosine similarity and norm to effectively detect and resist poisoning attacks. We provide two variants of ClusterGuard for both client-server and decentralized environments with blockchains, respectively. Extensive experiments on standard datasets demonstrate that ClusterGuard achieves over 2× efficiency improvement compared to advanced secure aggregation methods. Even with 20% of clients being malicious, the trained model maintains accuracy comparable to the original model, outperforming state-of-the art robustness solutions. ClusterGuard provides a more efficient, secure, and robust solution for practical federated learning.

Blind signatures allow a signer to issue signatures on messages chosen by the signature recipient. The main property is that the recipient’s message is hidden from the signer. There are many applications, including Chaum’s e-cash system and Privacy Pass, where no special distribution of the signed message is required, and the message can be random. Interestingly, existing notions do not consider this practical use case separately. In this paper, we show that constraining the recipient’s choice over the message distribution spawns a surprising new primitive that improves the well-established state-of-the-art. We formalize this concept by introducing the notion of non-interactive blind signatures ( $$\textsf{NIBS}$$ ). Informally, the signer can create a presignature with a specific recipient in mind, identifiable via a public key. The recipient can use her secret key to finalize it and receive a blind signature on a random message determined by the finalization process. The key idea is that online interaction between the signer and recipient is unnecessary. We show an efficient instantiation of $$\textsf{NIBS}$$ in the random oracle model from signatures on equivalence classes. The exciting part is that, in this case, for the recipient’s public key, we can use preexisting keys for Schnorr, ECDSA signatures, El-Gamal encryption scheme or even the Diffie-Hellman key exchange. Reusing preexisting public keys allows us to distribute anonymous tokens similarly to cryptocurrency airdropping. Additional contributions include tagged non-interactive blind signatures ( $$\textsf{TNIBS}$$ ) and their efficient instantiation. A generic construction in the random oracle or common reference string model based on verifiable random functions, standard signatures, and non-interactive proof systems.

Multiobjective genetic classifier selection for random oracles fuzzy rule-based classifier ensembles: How beneficial is the additional diversity?

In this paper, we study the problem of secure integrating public key encryption with keyword search (PEKS) with public key data encryption (PKE). We argue the previous security model is not complete regarding keyword privacy and the previous constructions are secure only in the random oracle model. We solve these problems by first defining a new security model, then give a generic construction which is secure in the new security model without random oracles. Our construction is based on secure PEKS and tag-KEM/DEM schemes and achieves modular design. We also give some applications and extensions for our construction. For example, instantiate our construction with proper components, we have a concrete scheme without random oracles, whose performance is even competitive to the previous schemes with random oracles.