With the growing adoption of electronic voting in digital societies, ensuring voter anonymity and ballot integrity is essential for secure and trustworthy elections. Blind ring signature is a promising cryptographic primitive that simultaneously provides blindness (concealing the link between signer and message) and ring anonymity (hiding the actual signer within a group), thus enabling anonymous yet verifiable voting. However, most existing schemes either rely on discrete logarithm assumptions, rendering them vulnerable to quantum adversaries, or fail to achieve both blindness and ring anonymity within a single construction. In the post quantum setting, only a handful of lattice-based blind ring signature schemes have been proposed, and all suffer from prohibitively large signature sizes, limiting their practicality for large-scale deployments. Consequently, there is a pressing need for lattice-based blind ring signatures that achieve post-quantum security, simultaneous blindness and ring anonymity, and practical efficiency for large-scale elections. In this work, we present LBRS, the first lattice-based blind ring signature scheme that transforms the two-round SnowBlind protocol [Crypto'23] from the discrete logarithm setting to the lattice setting while seamlessly integrating ring signature functionality. Unlike existing solutions, our design achieves post-quantum security while preserving blindness and introducing anonymity, making it robust against quantum attackers and capable of simultaneously providing blindness and ring anonymity. We implement LBRS within a complete post-quantum anonymous voting architecture, ensuring end-to-end security and full voter anonymity, while maintaining ballot integrity, enabling transparent and verifiable tallying, and supporting practical deployment. We formally prove that LBRS satisfies correctness, anonymity, blindness, and one-more unforgeability under standard lattice assumptions. Experiments show that LBRS reduces blind signature sizes to 58.37% and real signature sizes to 2.75%-16.52% of those in prior arts, while significantly decreasing registration time and maintaining comparable costs in other phases. These advantages scale with ring size, making LBRS a strong candidate for large-scale, post-quantum secure elections.

Web data management has become crucial to data sharing among users and servers. One promising approach to guaranteeing the privacy of shared data is searchable encryption (SE), which allows users to outsource encrypted data to the web server, which can then respond confidentially to keyword queries. Several SE schemes support access control to meet data-sharing requirements. However, several works (e.g., Zhang TSC'23, Zhang TCC'21) only focus on single-user access control and ignore the need for multi-user scenarios. Besides, a malicious data owner may send useless ciphertexts to the web server, potentially making the system insecure and impractical (e.g., Wang TPDS'22, Xu TDSC'20). As a result, research regarding owner authentication and multiuser access control in SE schemes remains underexplored. In this work, we construct SEOMA, the first multi-keyword encrypted search primitive supporting owner authentication and multi-user access control for Web data management. Unlike existing solutions, our design achieves owner authentication and multi-user access control simultaneously in a malicious setting. We incorporate attribute encryption to realize the attribute authentication for a data owner. Then, we leverage the policy tree and linear secret-sharing techniques to achieve hierarchical access control for users. We also formalize and demonstrate its security in a random oracle model by reducing to the DBDH and CBDH problem. Eventually, we conduct comprehensive performance evaluations compared to existing state-of-the-art schemes. Specifically, the computation and communication overhead is only 0.05-0.4× and 0.07-0.47× compared to prior arts, respectively.

Mobile cloud computing has become widely adopted for its convenience in data storage and sharing, but it also introduces challenges related to data privacy and security. To address these issues, public key authenticated encryption with keyword search (PAEKS) has emerged as a potential solution that ensures data privacy while resisting internal keyword guessing attacks (IKGAs). Unfortunately, most existing PAEKS schemes have limited adaptability to multi-user scenarios. Specifically, in PAEKS, ciphertext generation requires the participation of users' secret keys, which results in ciphertexts being unique, even when the same keywords are encrypted by different users. Con sequently, the number of trapdoors used to match the ciphertexts grows linearly with the amount of senders. Designing an efficient PAEKS scheme for multiple users remains an open challenge. In this paper, we propose CT-PAEKS, a lattice-based PAEKS scheme with constant trapdoor for data privacy-preserving in mobile cloud computing. CT-PAEKS introduces an additional administrator, enabling the receiver to generate a unified search trapdoor for ciphertexts from multiple senders. Additionally, it allows multiple senders to generate a single ciphertext for the same keyword encryption, thus avoiding ciphertext duplication. Furthermore, CT-PAEKS supports fast search during ciphertext matching, allowing all corresponding ciphertexts to be identified with a single match. We also formalize and prove the security of CT-PAEKS in the random oracle model. Comprehensive perfor mance evaluations indicate that our scheme outperforms prior arts, achieving the 1.7×-2.7× and 2.0×-4.4× reduction in terms of computational and communication overhead, respectively.

In robotic perception, cross-granularity object detection is essential for identifying and localizing targets at varying levels of detail. Traditional detection methods often struggle to bridge the gap between coarse object detection and fine-grained component localization, limiting their ability to associate parts, such as a cup and its handle. Vision-language models (VLMs), while effective in spatial reasoning, face challenges in fine-grained detection due to the scarcity of annotated datasets. To address these issues, we first propose the chain-of-detection (CoD) framework, which focuses on guiding detection in a step-by-step manner from coarse recognition to fine-grained localization. During this process, we observe that existing detectors still lack sufficient capability in recognizing fine-grained components. To overcome this limitation, we further combine the CoD framework with Monte Carlo tree search (MCTS) to automatically generate fine-grained datasets, eliminating the need for manual labeling and significantly improving detector performance. Experiments show that our approach achieves an average improvement of 17.31% in robotic manipulation success rates for common objects, 51.39% for larger object operations, and about 50% in simulated environments. These results demonstrate the effectiveness of CoD in advancing cross-granularity detection and enhancing precise robotic manipulation. The implementation is publicly available at https://github.com/tinnel123666888/CoD and the CoD dataset is released at https://huggingface.co/datasets/tinnel123/CoD_dataset.

Introduced by Google in 2016, federated learning (FL) is a distributed machine learning framework to ensure data privacy amid the surge in big data. FL enables secure data sharing without accessing local data. Despite its advantages, it faces challenges due to the limited participation of the data owner. To address this, this article proposes the model value transfer incentive (MVTI) to enhance FL incentives for Artificial Intelligence of Things (AIoT). MVTI allows active participation of data requesters in FL training, addressing limited data owner engagement, and facilitating personalized model construction. The integrated model bail and contribution assessment mechanism ensures fair benefit redistribution. Using smart contracts (SCs) and interplanetary file system (IPFS) enhances security and reliability, ensuring transparent and tamper-resistant execution for secure transactions and data integrity. Our experiments highlight MVTI’s superiority in addressing FL incentive challenges for AIoT compared to state-of-the-art baselines on real-world datasets. We also demonstrate the compatibility of multiple gradient protections with incentive mechanisms, especially with gradient compression. The proposed SC-MVTI scheme is resilient and demonstrates the potential to significantly improve the overall efficacy of the FL system within incentive frameworks.

With the advancement of artificial intelligence technology, smart proctoring has gradually supplanted traditional manual invigilation and becomes the dominant mode of examination supervision. However, existing technologies mostly rely on singular object detection algorithms or deep learning techniques, which are inadequate in addressing the complex and varied conditions of examination environments. In this paper, we design a multi-level intelligent recognition system for candidates’ cheating behaviors, integrating an optimized YOLOv8 object detection method based on multilayer perceptron (MLP) with the ResNet deep learning framework. This system mines key frames from surveillance videos to precisely capture candidates’ positional information and automatically tags those suspected of engaging in cheating activities. Our model’s development relies on a custom-tailored dataset, the cheating and normal (CAN) dataset, which includes instances of academic misconduct alongside standard behavior for training purposes. The model’s performance is then validated by assessing its effectiveness on real-life surveillance videos from examination halls. The resulting intelligent analysis model is capable of real-time, meticulous tracking and evaluation of every movement of each candidate within the examination venue, accurately discerning the nature of their actions. Our approach represents a significant step forward in enhancing the adaptability and effectiveness of AI-powered exam supervision systems.

Public key authenticated encryption with keyword search (PAEKS) has been widely studied in cloud storage systems, which allows the cloud server to search encrypted data while safeguarding against insider keyword guessing attacks (IKGAs). Most PAEKS schemes are based on the discrete logarithm (DL) hardness. However, this assumption becomes insecure when it comes to quantum attacks. To address this concern, there have been studies on post-quantum PAEKS based on lattice. But to our best knowledge, current lattice-based PAEKS exhibit limited applicability and security, such as only supporting single user scenarios, or encountering secret key leakage problem. In this paper, we propose FS-MUAEKS, the forward-secure multi-user authenticated searchable encryption, mitigating the secret key exposure problem and further supporting multi-user scenarios in a quantum setting. Additionally, we formalize the security models of FS-MUAEKS and prove its security in the random oracle model (ROM). Ultimately, the comprehensive performance evaluation indicates that our scheme is computationally efficient and surpasses other state-of-the-art PAEKS schemes. The ciphertext generation overhead of our scheme is only 0.27 times of others in the best case. The communication overhead of our FS-MUAEKS algorithm is constant at 1.75MB under different security parameter settings.

Public Key Infrastructure (PKI) has gained widespread attention for ensuring the security and integrity of data communication. While existing PKI mainly supports digital signatures, it is lacking in crucial anonymity, leading to the leakage of a signer’s identity information. To alleviate the issue, ring signatures are a suitable choice to provide anonymity as they allow users to create their own rings without the need for an administrator. Unfortunately, the utilization of ring signatures in PKI may present compatibility challenges within the system. Thus, proposing a general mechanism to convert a standardized$\Sigma $-based signature to a ring signature is far-reaching. In this paper, we propose a general construction for converting$\Sigma $-based signatures into ring signatures. To achieve this, we first introduce a$\Sigma $-based general model, providing a general transformation to convert existing$\Sigma $-based signatures into a$\Sigma $-protocol form. Subsequently, we incorporate our redesigned one-out-of-many relation within our general model and proceed to devise ring signatures leveraging on one-out-of-many proofs. Furthermore, to reduce the signature size, we employ the Bulletproofs folding technique, enabling the attainment of logarithmic size ring signatures. To demonstrate the wide applicability of our general construction, we present four prominent signatures as case studies. Ultimately, we conduct a rigorous security analysis and benchmark experimental evaluation. The signing and verification times are 0.44 to 0.97 times and 0.27 to 0.91 times compared to other state-of-the-art schemes, respectively. Additionally, we exhibit the lowest signature size to date.

Sample alignment is recognized as a vital component of vertical federated learning, which facilitates the integration of differential samples and high-quality model training. In this trend, providing Private Sample Alignment (PSA) among multi-clients becomes naturally necessary for preventing unauthorized sample access and client privacy exposure. However, exiting PSA protocols mainly focus on two-party scenarios and cannot be directly adapted to the multi-client delegated computing scenarios required for vertical federated learning. Besides, these studies fail to address the need for protocol robustness in practical federated Learning network environments. Therefore, we aim to design an efficient and reliable PSA protocol in multi-client vertical federated learning. In this work, we present the first practical PSA protocol for vertical federated learning, allowing multi-clients to efficiently identify common samples without revealing additional information. Toward this direction, our PSA protocol first explores the Learning With Errors (LWE) problem to create a lightweight delegated Private Set Intersection (PSI) scheme, enabling efficient sample intersection among multiple clients. To achieve the reliability of the PSA protocol, we devise a multi-client vector aggregation algorithm that securely delegates the server to calculate the sample intersection. Building on this foundation, we develop an efficient Threshold-based Private Sample Alignment (T-PSA) protocol that allows multiple clients to determine the intersection of their input samples only if the intersection size surpasses a specific threshold. We implement a prototype and conduct a thorough security analysis. Comprehensive evaluation results confirm the efficiency and practicality of our design.

The Internet of Medical Things (IoMT) has gained significant research focus in both academic and medical institutions. Nevertheless, the sensitive data involved in IoMT raises concerns regarding user validation and data privacy. To address these concerns, certificateless signcryption (CLSC) has emerged as a promising solution, offering authenticity, confidentiality, and unforgeability. Unfortunately, most existing CLSC schemes are impractical for IoMT due to their heavy computational and storage requirements. Additionally, these schemes are vulnerable to quantum computing attacks. Therefore, research focusing on designing an efficient post-quantum CLSC scheme is still far-reaching. In this work, we propose PQ-CLSCL, a novel post-quantum CLSC scheme with linkability for IoMT. Our proposed design facilitates secure transmission of medical data between physicians and patients, effectively validating user legitimacy and minimizing the risk of private information leakage. To achieve this, we leverage lattice sampling algorithms and hash functions to generate the partial secret key, then employ the sign-then-encrypt method and design a link label. We also formalize and prove the security of our design, including indistinguishability against chosen-ciphertext attacks (IND-CCA2), existential unforgeability against chosen-message attacks (EU-CMA), and linkability. Finally, through comprehensive performance evaluation, our computation overhead is just 5% of other existing schemes. The evaluation results demonstrate that our solution is practical and efficient.

Internet of Medical Things (IoMT) has garnered significant research attention from both academic and medical institutions. However, the sensitive medical data involved in IoMT raises security and privacy concerns. To mitigate these, ring signature has surfaced as a proper solution, which offers unforgeability and anonymity. Unfortunately, most multi-signer ring signature schemes require a predetermined number of signers and are difficult to adjust dynamically. Additionally, traditional ring signatures have limited adaptability for IoMT due to their reliance on a single entity. It is challenging to effectively define different signature permissions for users of various entities, such as patients and doctors. Therefore, research focusing on constructing a dynamic multi-signer ring signature for multi-party participation remains a critical and ongoing challenge. In this paper, we present ASMR, an anonymity-enhanced sequential multi-signer ring signature scheme for secure medical data sharing in IoMT. ASMR contains two different rings, PR and DR, for patients and doctors, respectively. It allows patients in PR to anonymously sign their electronic healthcare record (EHR) owned by doctors in DR, overcoming the reliance on a single entity in existing approaches while enhancing the anonymity of the signature. Meanwhile, ASMR introduces the concept of signature chaining, allowing multiple users in DR to co-sign information in sequence. In addition, it ensures that each generated signature is traceable, offering a transparent system. We also formally prove the security of ASMR in the random oracle model. Comprehensive performance evaluations indicate that ASMR excels in both computational and storage overhead. In the best case, computational overhead is reduced by approximately 4.7×-61.7×, while storage overhead is reduced by approximately 26.7×-212.8× compared to prior arts.

The Industrial Internet of Things (IIoT) has been widely studied, which dramatically enhanced the manufacturing efficiency and service elasticity. However, how to ensure the data confidentiality and security in the event-driven publish/subscribe communication model becomes a cumbersome problem. To address this concern, ring signatures have been researched deeply. Nevertheless, existing solutions have large computational burdens and neglect to incorporate reporting and tracing features, which makes it impractical for IIoT. In this way, research focus on designing an efficient report and traceable ring signature is still far-reaching. In this article, we propose RAT ring, a novel report and traceable ring signature, which provides publisher authentication, anonymous communication, reporting, and tracing. To achieve this, we adopt the zero knowledge proof to verify the authenticity of publisher data, and the signature of knowledge to trace the signature. Then, we formalize and prove the security of our scheme. Eventually, through comprehensive performance evaluation, our scheme outperforms prior works by approximately up to 51 times in terms of total computational overhead. These results demonstrate that our design is practical and effective for data privacy-preserving in IIoT.

With the widespread development of database systems, data security has become crucial when it comes to sharing among users and servers. A straightforward approach involves using searchable encryption to ensure the confidentiality of shared data. However, in certain scenarios, varying user tiers are granted disparate data searching privileges, and administrators need to restrict the searchability of ciphertexts to select users exclusively. To address this issue, public key encryption with authorized keyword search (PEAKS) was proposed, wherein solely authorized users possess the ability to conduct targeted keyword searches. Nonetheless, it is vulnerable to resist quantum computing attacks. As a result, research focusing on authorizing users to search for keywords while achieving quantum security is far-reaching. In this paper, we propose a lattice-based variant of PEAKS (L-PEAKS) that enables keyword dataset authorization for outsourced data management. Unlike existing schemes, our design incorporates identity-based encryption (IBE) to overcome the bottleneck of public key management. Besides, we utilize several lattice sampling algorithms to defend against attacks from quantum adversaries. Specifically, each authorized user must obtain a search privilege from an authority. The authority distributes an authorized token to the user within a specific time period, and the user generates a trapdoor for any authorized keywords. Our scheme is proven to be secure against IND-sID-CKA and T-EUF security in a quantum setting. We also conduct comprehensive evaluations on a commodity machine to assess completeness and provide theoretical complexity comparisons with existing state-of-the-art schemes.

Federated Learning (FL) transforms collaborative machine learning by enabling data privacy-preserving model training across dispersed devices. Unfortunately, several challenges need to be concerned, such as the non-ID features of cross-client data and the possibility of backdoor attacks, which can result in inconsistent models, inefficiency, and security issues. To address these issues, we propose hypernetwork-based personalization and poisoning-free federated learning (HBIpFL), a unique architecture to improve security and personalization in FL. HBIpFL dramatically reduces communication overhead without sacrificing performance by using a hypernetwork-based parameter classifier to dynamically analyze and only upload the most important model parameters. Furthermore, it utilizes Local Gradient Ascent (LGA) methods to track training loss trends and identify possible backdoor intrusions, guaranteeing the resilience and dependability of the global model. We then compare HBIpFL to the state-of-the-art methods in the context of accuracy, communication efficiency, and defense against adversarial attacks. The results demonstrate that our HBIpFL offers a secure and effective FL environment for practical scenarios with a wide range of data distributions and strict privacy specifications.

Ring signatures have been extensively researched for Cloud-assisted Electronic Medical Records (EMRs) sharing, aiming to address the challenge of “medical information silos” while safeguarding the privacy of patients’ personal information and the security of EMRs. However, most existing EMRs sharing systems that utilize ring signatures are vulnerable to quantum attacks, posing a severe challenge for the e-health scenario. To alleviate this issue, some studies have been conducted on lattice-based ring signatures. Nevertheless, there still exist two challenges. Firstly, current schemes fail to verify if multiple EMRs come from the same signer, undermining e-health reliability. Additionally, adversaries can exploit weaknesses in the network security of signers’ secret keys to forge signatures. In this paper, we propose an efficient lattice-based linkable ring signature (LLRS) for EMRs sharing to ensure patient privacy through anonymity, EMRs security through unforgeability, and checking the linkability for multiple signatures. We then present an enhancement scheme, called FS-LLRS, to additionally offer forward security, ensuring the security of previous ring signatures even if the current key has been compromised. To achieve this, we introduce a binary tree structure to divide time periods and leverage lattice basis algorithms for one-way secret key evolution, allowing users to update the secret keys periodically. Ultimately, we conduct a rigorous security analysis and compare our primitives with prior arts. In computational cost, the best performance of our LLRS and FS-LLRS schemes are just 0.17 and 0.34 times compared to others, respectively. Our LLRS scheme only incurs 0.08 times the communication overhead of others.

With emerging technology, the blockchain-based Vehicle Ad-hoc Network (VANET) can alleviate traffic congestion and optimize resource management to improve the transportation system's efficiency significantly. However, since the information transmitted in VANET is distributed in an open environment, security, and privacy are now critical issues. Recently, many authentication protocols based on Tamper-Proof Devices (TPD) have been proposed to solve the above-mentioned hindrances, and most of them rely on the ideal TPD with extreme security assumptions. Moreover, when the aggregate signature verification fails, these schemes can only completely discard the aggregate signature, which is impractical for VANET. In order to solve the above problems, we propose a more realistic TPD-based identity verification scheme with a privacy protection function for the blockchain-based VANET. Specifically, it uses an offline self-update method to periodically update the data in the TPD to resist side-channel attacks. Our performance analysis shows that the proposed scheme is superior to the existing schemes regarding security and average delay, so it is more suitable for the actual blockchain-based VANET environment.

The advancement of Internet of Medical Things (IoMT) leading to the proliferation of electronic healthcare scenarios, with an expanding trend of hospitals and healthcare organizations employing Electronic Medical Records (EMRs) with uploading to the cloud for sharing. However, security challenges exist during the generation and uploading of medical records, such as record tampering, key attacks, etc., which will become the maximum bottleneck restricting the development of e-Health scenarios in the near future. Some scholars consider digital signature techniques, such as Attribute-based Signature, to address the security challenges but ignoring its overwhelming efficiency. In this paper, we propose a lightweight forward secure aggregate signature for e-Health scenarios. We devise a lightweight secure aggregate signature to provide unforgeability and forward security for medical records, which is the first aggregate signature scheme that enables forward security in e-Health scenarios. Furthermore, our scheme offers superior lightweight properties, requiring only 5.78ms for aggregation and 4.85ms for verification of 1000 signatures, which is significantly lower than existing schemes, especially suitable for medical systems with enormous data volume. Security analysis and experimental evaluation indicate that our scheme assures correctness, unforgeability and forward security, while fulfilling lightweight demands that outperform existing schemes.

With the advancement of medical science, the implementation of Electronic Medical Records (EMRs) for enhancing the efficiency and reliability of healthcare services has become a widespread phenomenon. However, EMRs are stored in hospitals and medical institutions independently, leading to sharing challenges. Moreover, the highly sensitive EMRs are prone to be tampered with and abused, posing privacy and security threats. To address the aforementioned issues, we propose an Anti- Quantum Attribute-based Signature(AQ — ABS) for Secure EMRs Sharing with Blockchain. Initially, We are the first ones to design an Attribute-based Signature (ABS) that can resist quantum attacks in E-health, called AQ — ABS. Further, the owner and provider of the EMR encrypt and sign it via the AQ — ABS scheme, then store EMR to a secure and distributed file storage system, i.e. Interplanetary File System (IPFS). Finally, the index hashes generated by IPFS and keywords are re-signed and stored in the consortium blockchain. Security analysis indicates that our proposed scheme fulfills the properties of signers’ anonymity, EMRs unforgeability, EMRs shareability, and fine-grained access control. Comprehensive experimental evaluation demonstrates that our proposed scheme performs low overhead as well as outperforms existing ABS and EMR systems.