A Decentralized Randomness Beacon (DRB) is a powerful cryptographic tool that aims to generate fair, unpredictable, and publicly verifiable randomness. DRBs are gaining significant importance in distributed computing systems and decentralized network applications, where they serve as crucial sources of randomness for consensus protocols and other essential functions. While most existing DRBs prioritize core security attributes such as infeasibility and unpredictability, they often neglect participants' identity privacy. Traditional DRB protocols, such as GRandLine (CCS 2024) and RandFlash (TIFS 2025), typically expose participants' identities to potential attackers during the leader-election process and subsequent interactions. This exposure can disclose sensitive information, including blockchain stakes, rendering these protocols unsuitable for applications that require stringent privacy guarantees. In this work, we propose a privacy-aware DRB protocol, PADRE, that conceals participants' identities while generating randomness without compromising efficiency or basic security. To this end, we propose a new cryptographic primitive, the “anonymous threshold verifiable random function (ATVRF)”, that introduces both “threshold-capability” and “anonymity” on top of verifiable random functions (VRFs) under the Decisional Diffie-Hellman assumption (on elliptic curves). In addition, we integrate an “anonymous lottery” into the committee-based DRB, enhancing privacy protection and performance to a wide range of state-of-the-art DRBs. Our proof-of-concept implementation (102 beacons per minute on average across 64 nodes) indicates that PADRE is suitable for real-world deployments.

Threshold ECDSA schemes distribute the capability of issuing signatures to multiple parties. They have been used in practical MPC wallets holding cryptocurrencies. However, most prior protocols are not robust, wherein even one misbehaving or non-responsive party would mandate an abort. Robust schemes have been proposed (Wong et al., NDSS ’23, ’24), but they do not match state-of-the-art number of rounds which is only three (Doerner et al., S&P ’24). In this work, we propose robust threshold ECDSA schemes RompSig-Q and RompSig-L that each take three rounds (where the first two are broadcasts, whereas the non-robust scheme of Doerner et al. uses no broadcasts). Building on the works of Wong et al. and further optimized towards saving bandwidth, they respectively take each signer (1.0t+ 1.6) KiB and 3.0 KiB outbound broadcast communication, and thus exhibit bandwidth efficiency that is competitive in practical scenarios where broadcasts are natively handled. RompSig-Q preprocesses multiplications and features fast online signing; RompSig-L leverages threshold CL encryption for scalability and dynamic participation.

Onion routing and mix networks are designed to provide users with anonymous Internet access and to prevent the disclosure of real IP addresses. In practice, anonymous networks serve various legitimate purposes, such as whistleblowing, circum-venting censorship, and safeguarding personal online privacy and security. These systems typically achieve anonymity by introducing a series of relays between the sender and the receiver. An anonymous path, or circuit, is usually composed of multiple relays (commonly three), and onion routing systems such as Tor rely on these circuits to relay traffic and ensure anonymity. Although Tor employs hidden relays (known as bridges) to resist censorship and blocking, most relays are publicly listed, and their identities are visible to circuit initiators during circuit construction, making them susceptible to surveillance and targeted attacks. A malicious relay deviating from the protocol (e.g.,injecting modified onions) poses serious threats to system security. An interesting question is how to preserve relay identity privacy while maintaining network functionality. In this paper, we introduce an innovative method for protecting relay privacy within circuits. This is achieved through the application of anonymous credentials, anonymous verifiable random functions (AVRFs), and signatures with key blinding. Additionally, if more than half of the directory authority servers within the current Tor network are compromised, the entire network could collapse. To mitigate this risk, our design distributes trust among more entities, enhancing the network’s resilience against potential adversaries. We have named this approach Avatar to enable users to navigate the online realm with the same freedom, privacy, and anonymity as an Avatar, allowing them to maintain full control over their digital identity. Furthermore, we provide a comprehensive analysis and evaluation of the Avatar framework. The findings indicate that, in comparison to the original Tor network’s onion routing protocol, our proposed protocol exhibits superior time efficiency in many network environments.

Distributed public ledger (e.g., Blockchain) has been proven to be a powerful technique that allows users to sign transactions in an untrusted environment, where identity-privacy disclosure is gaining attention in practice. Ring signatures can protect identities by providing anonymity property for users. However, a malicious anonymous user may generate multiple signatures on the same transaction, called double-spending attack. A unique ring signature avoids this attack by attaching a unique identifier to the transaction. In addition, future-proof cryptographic solutions are attracting attention in the quantum era. Thus, we aim to propose a post-quantum unique ring signature scheme for anonymous transactions, named . We initially provide verifiable random functions over lattices (L-VRF, in short) with tight security and optimize the proof size (compared with the work of Nguyen et al., ESORICS’ 22) using compression techniques. We then obtain from L-VRF inspired by the previous solution of Franklin-Zhang (FC’ 13) while enables to prevent of quantum computer attacks. Finally, is analyzed under the quantum random oracle model (QROM) while providing a prototype via C language. The performance evaluation shows offers a smaller communication load.

A trustworthy source of randomness is a crucial component of many decentralized and crypto-based application systems, especially blockchain consensus. A decentralized random beacon (DRB) periodically outputs a new source of randomness generated using a distributed technique, such as publicly verifiable secret sharing (PVSS) or distributed verifiable random functions (VRFs). These protocols offer a variety of efficiency versus randomness quality tradeoffs, but guarantee security under a variety of configurations, assumptions, and adversarial models. This article aims to provide a future-proof unbiased decentralized randomness (abbreviated as Funder) via a post-quantum threshold VRF for sustainable proof-of-stake blockchain. We also provide a generic compiler for achieving post-quantum VRF from a classical VRF solution, but our approach makes use of symmetric-key primitives Our novel compiler is validated and evaluated using the ZKBoo and ZKB++ quantum-secure zero-knowledge systems, respectively. The implementation of the proof-of-concept demonstrates that the overheads introduced by our solution are acceptable for real-world deployments even in the present day. In addition, we demonstrate the protocol’s possible application in lottery-based proof-of-stake consensus protocols.

In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology-Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP’s efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.

Authentication and key agreement are two basic functionalities to guarantee secure network communications, which are naturally integrated as an Authentication and Key Agreement (AKA) protocol. AKAs usually either need a dedicated device to store a cryptographic key or require the user to remember a password. In recent years, AKAs built on biometrics, e.g., human fingerprints, have gained research attention since they avoid these issues. Unlike keys or passwords that can be updated, biometrics are at greater risk that cannot be reused once disclosed. However, existing mechanisms either explicitly expose the biometrics to the server or consume a massive amount of resources. This paper proposes UFinAKA, a privacy-preserving fingerprint-based authentication and key agreement system with updatable blind credentials. UFinAKA explores a fingerprint-based blind credential authentication scheme as a building block such that the server has no access to the fingerprint data hidden within the credential. Furthermore, UFinAKA provides an updatable fingerprint-based credentials AKA protocol, which allows the server to update the blind credentials and guarantees anonymous fingerprint authentication to mitigate further leakage when the server is corrupted. We perform security analysis and experimental evaluation on UFinAKA. The evaluation results show that UFinAKA requires only linear computation overhead for the client, a single round of interaction, and roughly linear computation and storage cost for the server. The running time of UFinAKA is at least 4 times faster than the state-of-the-art solutions, and the storage cost of these solutions is at least 100 times more than UFinAKA.

Sealed-bid auction is a common mechanism for selling and buying commodities. However, existing auction schemes to protect bids require at least squared computation and communication complexity for the bidders or rely on trusted auctioneers or third parties. To address the above problems, we propose a secure and efficient sealed-bid auction framework, called FACT. We design a lightweight threshold fully homomorphic encryption scheme as the building block. Our framework does not rely on any trusted auctioneer and fulfills a stronger security guarantee, called full privacy, i.e., only the seller and the winning bidder can determine the auction result. While our framework applies to first-price sealed-bid, it can easily be extended to support second-price sealed-bid (i.e., Vickrey auction) with the same security guaranteed. Our framework also supports the dynamic joining and exiting of sellers and bidders. Meanwhile, our framework reduces the bidders’ overhead and the number of interactions to a constant level. We formally prove the security of our framework in the semi-honest adversary model. We implement FACT and run experiments comparing its performance against existing schemes. We find that our framework not only achieves a stronger security guarantee but also shows significant performance improvement compared to existing schemes.

Vehicle authentication is an essential component validating the vehicle’s identity and ensuring the integrity of transformed data for intelligent transport vehicles (ITS) in the vehicular ad hoc network (VANET). Easy to deploy and operate privacy-enhancing vehicle authentication mechanisms are the mainstay for the widespread ITS in the VANET. Very recently, VANET security architectures are constituting by IEEE 1609.2 group, NoW project, the SeVeCom project. However, these approaches heavily depend on the consuming public key infrastructure (PKI) and certification authorities (CA). In this work, walking along the research line, we attempt to design authentication protocols with two diverse factors for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) networks, respectively, without depending on the stumbling block PKI/CA. In addition, a smooth projective hash function (SPHF) (a.k.a., a special case of the designated-verifier zero-knowledge proof system) guarantees any recipient can confirm the authenticity and integrity of the received messages without knowing the authentication factors. Thus, to optimize the communication round, SPHF is used to design a (group) two-factor authenticated key exchange (AKE) with low-interactive communication rounds. The proof-of-concept implementation indicates that the computation and communication overheads introduced by our solution are acceptable in real-world deployments. The security of the proposed approach is validated using Bellare-Pointcheval-Rogaway (BPR) model along with the experimental evaluation and the theoretical analysis.

Secure messaging heavily relies on a session key negotiated by an Authenticated Key Exchange (AKE) protocol. However, existing AKE protocols only verify the existence of a random secret key (corresponding to a certificated public key) stored in the terminal, rather than a legal user who uses the messaging application. In this paper, we propose a Biometrics-Authenticated Key Exchange (BAKE) framework, in which a secret key is derived from a user's biometric characteristics that are not necessary to be stored. To protect the privacy of users' biometric characteristics and realize one-round key exchange, we present an Asymmetric Fuzzy Encapsulation Mechanism (AFEM) to encapsulate messages with a public key derived from a biometric secret key, such that only a similar secret key can decapsulate them. To manifest the practicality, we present two AFEM constructions for two types of biometric secret keys and instantiate them with irises and fingerprints, respectively. We perform security analysis of BAKE and show its performance through extensive experiments.