While voiceprint authentication offers convenient user authentication and access control through voice feature recognition, a critical research gap remains: existing voiceprint authentication systems fail to simultaneously achieve sound security against replay, spoofing, and adversarial attacks, preserve voice privacy leakage, and satisfy usability demand. Previous efforts have struggled to balance these issues comprehensively. To bridge this gap, we present SeVoAuth, a cloud-based Voiceprint Authentication as a Service (VAaaS) system designed to provide privacy preservation, robust security, and enhanced usability. SeVoAuth stores a synthesized voiceprint of a user in the cloud during user registration, thereby safeguarding the privacy of the real voiceprint of the user. During user authentication, SeVoAuth applies a hash function to continuously transform features of the synthesized voiceprint, dynamically generating new verification targets for voiceprint feature mapping in each authentication session. This dynamic transformation approach effectively mitigates replay, spoofing, and adversarial attacks without requiring complex user interactions. We conduct a thorough analysis on the security and privacy of SeVoAuth and proceed to implement a prototype for performance evaluation through a series of user tests. Experimental results demonstrate that SeVoAuth outperforms cutting-edge approaches, achieving an average authentication accuracy of 99.47%, and an average Precise Detection Rate (PDR) of 98.35% against various attacks. SeVoAuth is evaluated as highly secure, efficient, and user-friendly across various circumstances.

Searchable encryption enables writers to upload encrypted data to an untrusted cloud server, while allowing authorized readers to perform keyword searches over the encrypted content without compromising data privacy. The multi writer/multi-reader (M/M) setting better reflects real-world de ployment requirements because it aligns with the collaborative nature of many modern applications, but introduces substantial challenges in both efficiency and security. The state-of-the art scheme, delegatable searchable encryption (DSE), achieves optimal search time and forward privacy in the M/M setting. Nevertheless, DSE provides only limited security guarantees: search tokens generated by readers reveal the update count of queried keywords, and the scheme remains susceptible to collusion attacks between users and the server. In this work, we present Archon, a novel hardware-assisted multi-user searchable encryption scheme that addresses the aforementioned security limitations while maintaining sublinear search complexity. By leveraging trusted execution environments and an updatable keyword private information retrieval protocol, Archon achieves strong collusion resistance and supports flexible user revocation in multi-user settings. We implement Archon and evaluate its performance on real-world datasets to demonstrate its practical efficiency.

The combination of Deep Learning (DL) and Federated Learning (FL) makes it a popular paradigm to train powerful models securely on large-scale data in a distributed way. However, current solutions face challenges such as significant communication overheads for clients with limited resources, potential privacy risks arising from FL's distributed nature, and the inability to maintain model accuracy without loss under high compression ratios. To solve these issues, we propose a lightweight Communication-efficient and Privacy-preserving FL scheme CPFL by designing Cyclic Segmented Compressive Sensing (CSCS) and using efficient Symmetric Homomorphic Encryption (SHE), which greatly reduces the number of transmitted model weights without sacrificing model accuracy. Formal analysis shows the security of CPFL against known-plaintext attacks and ensures model convergence. Extensive experiments demonstrate that CPFL achieves remarkable model accuracy under more than 200 & times; compression ratio, and even reduces the communication cost by 99.5% compared with previous solutions.

Payment channels (PCs) are instrumental in enhancing bloc- kchain scalability. As PCs become more prevalent, the imperative for independent and robust auditing mechanisms grows. Despite the critical need, there has been no extensive research on auditing PCs to ensure provable security. Challenges include maintaining global consensus and chronological integrity of off-chain transactions. Moreover, collusive parties pose a threat by potentially launching attacks that could disrupt the auditor’s ability to verify transaction integrity. This paper introduces IvyApc, the novel protocol designed for the auditable PC framework. IvyApc addresses the aforementioned challenges through two innovative techniques: (i) Accountable Assertions with Flexible Public Keys: This mechanism imposes penalties on parties attempting collusion during the audit process; (ii) Chain-Linking of Off-Chain Transactions: It guarantees a verifiable sequence of transactions, safeguarding against tampering within PCs. We validate the IvyApc protocol within the Universal Composability framework, demonstrating its adherence to the security prerequisites of completeness and soundness for auditing purposes. A prototype of IvyApc has been developed and tested for compatibility with Bitcoin’s PC infrastructure, showcasing its practical applicability.

With the development of air transportation, Space-Air-Ground Integrated Network (SAGIN) are playing an increasingly important role in optimizing air traffic management and enhancing flight safety for billions of passengers and trillions dollars of aviation industry. As the key technology of SAGIN, the Automatic Dependent Surveillance-Broadcast (ADS-B) system is widely used due to its simple operation, low construction cost, and high information accuracy. However, the security problems in ADS-B system, including lack of identity authentication between all communication links, crucial information transmitted in plaintext, and susceptibility to the single point of failure, have been serious obstacle to its wide application. Existing solutions fail to account for the unique characteristics of ADS-B and SAGIN, leading to inadequate security and poor performance in these specialized contexts. Aiming to solve the above issues and provide security and scalability for ADS-B system, we conduct the following research. Firstly, an enhanced identity-based broadcast signcryption (e-IBBSC) scheme is designed to keep crucial information confidential and all messages authenticated simultaneously. Secondly, we propose an efficient batch message authentication method combined with the Merkle tree and proposed e-IBBSC, significantly improving the ADS-B message utilization ratio from 1.35% to 74.10%. Thirdly, we utilize the sharding blockchain and Byzantine fault tolerance protocol to design the first sharding-based distributed management system for SAGIN that realizes fault tolerance and scalability. Finally, after a detailed security analysis and comprehensive performance evaluation, we demonstrate that our solution can achieve all proposed system goals including security, scalability, and high performance of 1s flight transaction processing latency and 62KTPS throughput.

The BBS+ signature scheme is a widely used foundation for anonymous credential systems. It is favored for its support of selective disclosure and its efficiency in proving credential possession. However, in traditional settings, credentials are typically issued by a single authority, creating a single point of failure and potential security risk. This limitation can be mitigated by adopting a distributed variant, known as the threshold BBS+ scheme. In this work, we present Robot, the first two-round threshold BBS+ signature scheme. Robot is round-minimal and achieves robustness, ensuring that every signing execution successfully completes as long as there exist $t+1$ parties behaving honestly. To achieve this, we employ a threshold verifiable random function (TVRF) to robustly generate the public nonces within a single round. Specifically, we utilize an efficient DDH-based TVRF construction, which not only provides our scheme with a round advantage but also enhances its overall performance. Then, by carefully invoking the threshold Castagnos-Laguillaumie and threshold ElGamal homomorphic encryptions, we complete all remaining non-linear operations within the second round. Asymptotically, Robot achieves a constant per-party upload communication and linear computation overhead with respect to the number of signers. Compared with the four-round robust scheme of Wong et al. (NDSS'24, WMC24), which has the same asymptotic complexity, Robot achieves a smaller constant communication cost (2.02 KB vs. 3.23 KB) and nearly halves the runtime. Compared with the three-round robust scheme of Tang and Xue (S&P'25, TX25), which has linear communication overhead, Robot exhibits better communication and computational efficiency when the number of signers is five or more.

Artificial General Intelligence (AGI) offers transformative potential for low-altitude maritime monitoring systems (LMMS), enabling enhanced situational awareness, intelligent UAV coordination, and adaptive maritime operations. However, leveraging AGI in LMMS introduces critical challenges, particularly in ensuring secure, efficient, and privacy-preserving signature aggregation in bandwidth-constrained environments. In this paper, we present SecLMMS, a secure and AGI-enabled LMMS architecture tailored for the low-altitude economy. SecLMMS fuses real-time and offline AGI-driven decision-making to achieve both responsive control and strategic maritime insight. To minimize the computational and communication burden in AGI-enhanced UAV networks, we design a novel compound aggregate signature scheme with dual-layer aggregation, offering strong data authenticity guarantees while reducing public key overhead. SecLMMS further supports multimodal data collection and redaction, safeguarding against replay attacks, Sybil attacks, and privacy leaks. Formal analysis confirms the system’s security and privacy resilience, while extensive experiments demonstrate substantial reductions in system overhead. SecLMMS represents a promising step toward scalable, secure, and intelligent AGI-driven maritime monitoring.

Despite the ubiquity of cloud storage, achieving secure and flexible file sharing remains challenging. Through a case study of six mainstream cloud storage platforms, we identify three key issues: the lack of native or universally available end-to-end encryption, reliance on third-party key management with additional trust assumptions, and inflexible or inefficient privilege revocation. To address these challenges, we propose GuardShare, a secure cloud file-sharing system for user collaboration in an enterprise environment. GuardShare leverages Trusted Execution Environment (TEE) to provide end-to-end data confidentiality and discretionary access control. At its core, we design a TEE-assisted Verifiable and Conditional Proxy Re-Encryption (VCPRE) scheme that eliminates the need for integrity checks or complex cross-validation between re-encryption keys and ciphertexts. By re-encrypting only symmetric data encryption key from the owner to the recipient, our system enables fast file sharing. Our security analysis is conducted under the Universal Composability (UC) framework, covering three subsystems: user, file, and permission management. To align the proposed TEE-assisted VCPRE scheme with UC security, we define a novel ideal functionality that captures both Chosen-Ciphertext Attack (CCA) security and re-encryption verifiability. Finally, we implement GuardShare and integrate it with Microsoft OneDrive in a TEE-enabled cloud environment. Experimental results show that sharing a 1GB file takes only 3.27 s using a 128-byte re-encryption key, indicating the practicality of our system in real use.

The Internet of Vehicles (IoV) generates massive sensitive perception data, typically managed by manufacturer-specific domains. While encryption with domain-specific parameters protects confidentiality, many IoV applications require secure cross-domain data sharing to access complementary information, and expressive keyword search for efficient access. However, existing Attribute-Based Keyword Search (ABKS) schemes are designed for single-domain settings, and thus cannot address heterogeneous key management or provide traceability without a universally trusted authority. To address these issues, we propose TCroS, a traceable cross-domain data sharing scheme that generalizes CP-ABE via proxy re-encryption mechanism, enabling ciphertexts generated in one domain to be securely transformed for authorized requesters in another. To provide traceability, TCroS embeds requester identities into decryption keys using Boneh-Boyen signatures, allowing any party (rather than the universally trusted authority) to trace the source of a leaked key. We further extend TCroS to TCroSS, which incorporates privacy-preserving expressive keyword search supporting Boolean queries, thereby enabling efficient retrieval of authorized data while resisting keyword guessing attacks. Formal security analysis proves that our schemes achieve IND-SCPA and IND-SCKA security. Experimental results demonstrate their practicality, showing that cross-domain sharing can be realized with computation and storage overheads comparable to single-domain setting.

Anonymous credentials are a fundamental cryptographic primitive for achieving privacy-preserving authentication and fine-grained access control. The proliferation of the mobile internet usage has introduced two key requirements for the design of anonymous credentials: (1) enabling lightweight deployment on resource-constrained mobile devices such as smartphones, and (2) supporting expressive and complex access policies required by diverse service providers. However, satisfying both requirements is challenging due to the inherent computational limitations of mobile devices and the substantial resource demands of advanced access policies. To fill this gap, we propose MobCred, a new mobile-cloud integration anonymous credential framework with universal access policy. In MobCred, the credential presentation protocol is partitioned into two components: the mobile user efficiently generates identity and credential proofs, while the cloud servers compute zkSNARK proofs to enforce universal access policies. With the help of the updateable public keys [ASIACRYPT19], the structure-preserving signatures with equivalence classes [JOC19], the Poseidon hash function [USENIXSec21], and the Groth16 protocol [EUROCRYPT16], we present both a generic construction and a concrete construction of MobCred and formally prove its security properties, including unforgeability, unlinkability, and dependability. Rigorous experimental evaluations on smartphones, laptops, and workstations confirm the practical efficiency of our framework.

In the evolution of Intelligent Transportation Systems (ITS), autonomous vehicle platoon has emerged as a pivotal technology, where platoon followers periodically transmit critical information (such as speed and acceleration) to the platoon leader to maintain coordination and synchronization within the formation. Heterogeneous signcryption has garnered significant attention as a mean to ensure secure data transmission in environments characterized by diverse natures of vehicles. However, existing heterogeneous signcryption schemes fail to provide forward privacy and incur significant computational overhead during unsigncryption. To address these challenges, we propose an efficient heterogeneous signcryption scheme with forward privacy (HSCFP) for vehicular platoon communication. First, based on Elliptic Curve Cryptosystem (ECC), we introduce a heterogeneous signcryption framework that transitions data from Identity-based Cryptosystem (IBC) to Public Key Infrastructure (PKI), enabling seamless communication across cryptosystems. Next, we design an incremental update mechanism based on Updatable Public Key Encryption (UPKE) and Hashed EIGamal, ensuring the long-term security of historical communications. Additionally, we design a batch verification algorithm that significantly accelerates the unsigncryption process when dealing with multiple ciphertexts. Finally, we provide a rigorous security analysis of HSCFP under the random oracle model, and extensive experimental results demonstrate our scheme enhances unsign-cryption efficiency by over 60% compared to existing solutions.

Incentive (or point) systems are widely deployed across industries such as retail, tourism, and finance to enhance customer loyalty and create benefits for service providers. However, their operation typically requires the collection and processing of sensitive customer data, leading to significant privacy concerns. Existing privacy-preserving incentive systems predominantly rely on bilinear pairings and the discrete logarithm assumption, which, while efficient in classical settings, are vulnerable to quantum adversaries and thus lack long-term security guarantees. To address this limitation, we present LatInc, a practical lattice-based privacy-preserving incentive system. LatInc integrates state-of-the-art lattice-based signatures with efficient protocols, the ABDLOP commitment, and efficient lattice zero-knowledge proofs, achieving a robust balance between post-quantum security and efficiency. Relying on the hardness of the MLWE and MSIS problems, we formally prove that LatInc achieves unforgeability, anonymity, and framing-resistance in the random oracle model. We implement a demo of the system and evaluate its performance on a standard laptop platform. Experimental results show that the communication overheads for the Earning and Spending protocols are approximately 99 KB and 140 KB, respectively, with execution times of 610 ms and 900 ms, highlighting significant efficiency gains over previous lattice-based incentive constructions.

In this paper, we present scalable fuzzy PSI protocols for general L_p ∈ [1, ∞] distance, supporting both low- and high-dimensional sets. The core technique is two efficient fuzzy matching protocols. The first is built from a role-reversed oblivious PRF (OPRF) and realizes O(dlog δ) overhead, compared to O((log δ)^d) in previous works. The second leverages customized oblivious transfer (OT) with O(dℓ) overhead, where ℓ is the bit length of inputs, which is particularly suitable for short inputs. With these new techniques, we further propose a new dual-layer hashing framework for fuzzy PSI over low-dimensional sets, instantiated with our OT-based fuzzy matching and enhanced with a domain reduction optimization. The protocols achieve an overhead linear with n, m, log δ, 2^d, without the O((log δ)^d) or O(δ) factors present in prior works. For high-dimensional sets, we construct fuzzy PSI protocols based on our OPRF- and OT-based fuzzy matching, which achieve an asymptotic overhead linear with n, m, d, and log δ but rely on the strong globally disjoint assumption. Extensive evaluations demonstrate that our protocols achieve up to a 145× speedup in running time and a 20× reduction in communication cost compared to van Baarsen and Pu (ASIACRYPT'25), and achieve up to a 25× speedup in running time and up to a 17× reduction in communication cost compared to Piske et al. (CCS'25).

In a scenario where an issuer wishes to issue an attribute-based anonymous credential to a user, this issuance is conditional on a number of real-world outcomes. These outcomes involve multiple entrusted oracles confirming the occurrence of several events, after which the issuance can proceed successfully. Such contractual credentials can serve as an important building block for blockchain-based Web 3.0 systems and can be used in real-world applications that require privacy-preserving, prescheduled authorization. However, there is currently no work that enables the pre-issuance of credentials based on oracles and events. In this work, we propose contractual anonymous credentials, called FlyCred, to fill this gap. With FlyCred, the issuer can issue an encrypted credential to a user, controlled by a dual-layer authorization policy consisting of oracle-based and event-based expressive policies. As core building blocks, we introduce two novel cryptographic primitives: the Adaptor Anonymous Credential and ABE-based Signature Witness Encryption with Tags, which can serve as independent interests. We provide efficient instantiations of these primitives and evaluate their performance under different security levels and system parameters on a laptop, showing that the computation and communication overhead of the credential pre-issuance is less than 85.8 seconds and 8.7 MB, respectively.

Service discovery is a fundamental process in wireless networks, enabling devices to find and communicate with services dynamically, and is critical for the seamless operation of modern systems like 5G and IoT. This paper introduces PriSrv+, an advanced privacy and usability-enhanced service discovery protocol for modern wireless networks and resource-constrained environments. PriSrv+ builds upon PriSrv (NDSS'24), by addressing critical limitations in expressiveness, privacy, scalability, and efficiency, while maintaining compatibility with widely-used wireless protocols such as mDNS, BLE, and Wi-Fi. A key innovation in PriSrv+ is the development of Fast and Expressive Matchmaking Encryption (FEME), the first matchmaking encryption scheme capable of supporting expressive access control policies with an unbounded attribute universe, allowing any arbitrary string to be used as an attribute. FEME significantly enhances the flexibility of service discovery while ensuring robust message and attribute privacy. Compared to PriSrv, PriSrv+ optimizes cryptographic operations, achieving 7.62* faster for encryption and 6.23* faster for decryption, and dramatically reduces ciphertext sizes by 87.33

Differential privacy is a fundamental technique for protecting individual privacy in databases, achieved by adding carefully calibrated random noise to query results. To enable privacy-preserving data analysis in cloud settings, it is also necessary to encrypt the database to ensure data confidentiality. However, designing a solution that allows efficient querying over encrypted data while still supporting differentially private outputs remains a challenge. This setting can be viewed as a special case of functional encryption (FE), yet securely and efficiently incorporating noise that satisfies differential privacy is far from trivial. In this paper, we revisit the primitive of Noisy Multi-Input Functional Encryption (NMIFE) first proposed by Zalonis et al. and make two primary contributions. First, we present new NMIFE constructions for the inner product (NMIFE-Lin) and quadratic functions (NMIFE-Quad), and as a stepping stone, their single-input variants (NFE-Lin and NFE-Quad), all based on the Learning with Errors (LWE) assumption. The key insight of our design is to utilize the discrete Gaussian noise inherent in LWE as both the perturbation noise, while preserving both privacy and efficiency. Collectively, these four constructions encompass the most widely studied functions in FE literature and support a broad class of query types over encrypted, horizontally partitioned databases. Second, by exploiting the linear-algebraic structure of LWE, our schemes significantly simplify the decryption process compared to existing approaches, enabling support for a large number of records. We implement our constructions on the MariaDB database and apply them to differentially private mean and variance queries. Experimental studies show that on a $2^{20}$-sized dataset with eight equal-sized partitions, and with a quarter of records being selected, the query key for mean and variance queries can be generated in 1.52s and 33.31s, respectively, while user-side decryption completes in 0.18s and 26.05s. These results represent approximately 7× to 1,552× speedups over state-of-the-art counterparts, demonstrating the practicality of our approach for real-world applications.

Byzantine Fault Tolerance (BFT) protocols are a critical research area in distributed systems and blockchain consensus due to their capacity to deliver high throughput and low latency. Traditional BFT protocols typically rely on a single leader to propose transactions and aggregate votes, which often creates a bottleneck due to the leader’s limited communication and computational capacities. The introduction of multi-leader BFT has the potential to mitigate this issue by increasing system parallelism. However, existing approaches fail to address the challenge of electing multiple leaders and lack a comprehensive analysis of the relationship between the number of leaders, security constraints, and system throughput. In this paper, we study the performance and security of multi-leader BFT protocols. Initially, we introduce a secret multi-leader election method resistant to corruption attacks where selected leaders’ identities remain unknown to others until they proposes transactions. Then, we present specific multi-leader BFT constructions that support a pipelined processing methods, realizing high processing parallelism and optimized throughput. Besides, a cross-leader view-change mechanism is designed for multi-leader BFT to enable efficient replacement of malicious leaders. Furthermore, we analyze the impact of the number of leaders on security and demonstrate that our proposals meet the required security standards. Experimental results reveal that the system achieves a throughput of up to 101 ktx/sec with 128 nodes, highlighting the potential of multi-leader BFT to significantly enhance the performance of blockchain systems.

Privacy-preserving spatial range query allows users to obtain valid data based on specific spatial attributes or geographical location while ensuring privacy. However, many existing Privacy-Preserving Spatial Range Query (PSRQ) schemes generally face the problems of low query efficiency and insufficient security when dealing with large-scale mobile cloud data sets, and it is difficult to resist Indistinguishability under Chosen-Plaintext Attack (IND-CPA). To solve these challenges, we first propose an Efficient and Secure Spatial Range Query scheme (ESSRQ), which is based on a dual mobile cloud architecture by integrating Geohash algorithm, Circular Shift Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), achieving a constant search complexity. However, ESSRQ cannot protect the access patterns, where the cloud server still has the potential to infer attacks based on the index position and even obtain plaintext queries. On this basis, we further propose an extended scheme ESSRQ-PIR, which introduces Private Information Retrieval (PIR) into single mobile cloud-based architecture, effectively prevents the leakage of access patterns, enhances the security of ESSRQ and can also realize efficient query on large-scale cloud datasets. Formal security analysis proves that our proposed schemes are secure against IND-CPA, and extensive experiments demonstrate that our schemes improve the query efficiency by up to nearly 20 times when compared with previous solutions. These features make the proposed schemes particularly suitable for privacy-preserving spatial queries in mobile cloud computing environments.

Federated Learning (FL) enables collaborative model training across distributed devices while preserving data privacy. However, it faces critical security challenges, including centralization risks and poisoning attacks, which degrade robustness and scalability. Existing schemes struggle to simultaneously mitigate targeted and untargeted poisoning attacks, impose restrictive adversary ratio assumptions (poison ratio < 50%), and suffer from privacy-performance trade-offs. To address these limitations, we propose SharBipole, a decentralized FL scheme integrating sharding blockchain with a novel dual-metric defense mechanism, Bipole. SharBipole employs a Byzantine Fault Tolerant-enabled sharding architecture to eliminate single points of failure, reduce communication overhead, and enable parallel model aggregation. Meanwhile, the Bipole module defends against poisoning attacks using two adaptive similarity metrics to filter malicious updates dynamically. Reinforcement learning optimizes threshold adjustments, while noise-aware adaptive clipping balances privacy and model utility. Further, we give convergence analysis to prove the theoretical soundness and scalability of SharBipole. Lastly, extensive experimental evaluations demonstrate that SharBipole supports poison ratios exceeding 50% and improves throughput and latency. The model replacement attack with 60% adversaries is entirely ineffective against SharBipole, and the label-flipping attack achieves an attack success rate of only 2.344%. SharBipole establishes a scalable, secure, and privacy-preserving solution for distributed learning in massive environments.

Transaction propagation delay limits the block interval and is one of the main bottlenecks in improving Bitcoin throughput. However, transaction relay in Bitcoin is entirely voluntary, which results in low bandwidth and high transaction propagation delay. Improving relay motivation by introducing incentives can effectively reduce delay, but it still faces challenges such as Sybil attacks during reward allocation, leakage of network-layer privacy, and high on-chain/off-chain overhead. Therefore, this paper proposes Txtail, a practical transaction relay incentive scheme for Bitcoin, based on continuously attaching relay evidence representing the relayers' identity and contribution during transaction propagation. We employ a free pricing mechanism based on the game between relayers to allocate rewards fairly. We design an order-insensitive relay evidence structure based on aggregate signatures and public key mapping, which reduces off-chain data overhead while alleviating the leakage of relay paths by obfuscating the relay order. We construct a verifiable lottery mechanism based on Merkle tree commitments to reduce the data that needs to be uploaded to the chain. Both theoretical and experimental results show that Txtail reduces the per-hop off-chain overhead and the overall on-chain overhead by 96.6% and 79.8%, respectively, compared with state-of-the-art baselines, while remaining practical for deployment.