The rapid expansion of the Internet of Things (IoT) has introduced critical security challenges in authentication, data integrity, and privacy preservation. Traditional digital signature schemes, such as RSA and ECDSA, rely on identity-based trust models, which face scalability bottlenecks, lack fine-grained access control, and pose privacy risks in IoT environments. Attribute-based signatures (ABS) offer a promising solution by allowing devices to sign data only if their attributes satisfy a predefined policy, without revealing their exact identity. However, most existing ABS constructions rely on pairing-based cryptography, which is vulnerable to quantum computer attacks, while lattice-based ABS schemes often suffer from either large signature sizes or dependence on non-interactive zero-knowledge (NIZK) proofs. In this paper, we propose an efficient lattice-based ABS scheme that eliminates the need for NIZK proofs while achieving constant-size signatures. Our construction leverages the lattice-based vector commitment technique to achieve quantum resistance while reducing signature size to a constant independent of the number of attributes, significantly improving efficiency compared to prior works. Experimental evaluations confirm that our scheme outperforms existing lattice-based ABS in both computational cost and signature size, particularly for large attribute sets and deep policy circuits. Our results pave the way for practical ABS deployment in resource-constrained IoT applications, such as secure firmware updates, industrial access control, and vehicular networks.

Cross-silo federated learning (FL) allows organizations to collaboratively train machine learning (ML) models by aggregating local gradients from clients without sharing their training data. Despite its merits, it suffers from privacy concerns due to the leakage of local gradients. A popular approach is to have clients mask their local gradients using homomorphic encryption (HE). However, this not only results in a reliance on a trusted third party (TTP), but also leads to significant computational and communication overhead. In addition, in existing cross-silo FL protocols, the aggregation operation is performed by a centralized aggregator, raising new security issues. One of these issues involves verifying the correctness of the aggregated results returned by the aggregator. The aggregator has been removed in the cross-device setting by leveraging blockchain technology, but not in the cross-silo setting. In this paper, we propose FreeFL, an efficient privacy-preserving cross-silo FL that eliminates the need for the TTP and aggregator, as well as achieves the optimal communication rounds. The high-level idea behind FreeFL is to customize alightweightdecentralized symmetric encryption with additive homomorphism for cross-silo FL. To this end, we design an efficient decentralized multiparty symmetric encryption (DMSE) scheme and twolightweightmultiparty computation protocols. We evaluate the performance of FreeFL, and the experimental results indicate that FreeFL exhibits high efficiency in both computation and communication. Additionally, we conduct experimental comparisons between FreeFL and other existing HE-based cross-silo FL protocols to show that FreeFL achieves significant computational efficiency improvements.

With the rapid progress of urbanization, city governance faces growing challenges such as traffic violations and environmental pollution. Traditional manual monitoring methods are inefficient and costly. To enhance the efficiency of monitoring and managing uncivil behaviors in urban environments, we propose a self-supervised video hashing retrieval framework for uncivil behavior recognition. Leveraging deep learning techniques, our method generates compact binary hash codes for both video and text modalities via a contrastive masked autoencoder (ConMH), enabling efficient large-scale retrieval. We further improve ConMH by introducing cross-attention mechanisms in the text hashing branch to better handle context dependencies. To optimize retrieval results, we integrate five multimodal fusion and ranking strategies, including a novel Hybrid Distance-Rank Fusion method that balances similarity scores and rank information. Experiments conducted on MSRVTT and MSVD datasets demonstrate that our approach achieves superior performance in mAP@K and NDCG metrics. The framework significantly enhances cross-modal semantic coverage, ensures high retrieval precision, and maintains low computational and storage overhead through binary encoding.

Federated learning (FL) allows multiple clients to train an efficient deep-learning model collaboratively but is susceptible to backdoor attacks. Traditional detection-based defenses depend on specific metrics to distinguish client gradients. Defense-aware attackers exploit this by constraining attack gradients on these metrics to evade detection, leading to metric-constrained attacks. This paper concretely instantiates such threats and introduces cosine-constrained attacks, which successfully compromise advanced defenses based on cosine distance. To address the aforementioned challenge, we propose Scope, a novel defense that detects cosine-constrained attacks using cosine distance by exposing the constrained backdoor dimensions of attack gradients. Scope employs dimension-wise normalization and differential scaling to amplify the distinction between backdoor dimensions and benign or unused ones, countering sophisticated attackers’ attempts to obscure them. Moreover, we develop a novel clustering approach, namely Dominant Gradient Clustering (DGC), to isolate and eliminate backdoor gradients. Extensive experiments across various datasets, models, FL settings, and adversary scenarios demonstrate that Scope consistently outperforms existing defenses by a significant margin, especially against the cosine-constrained attack. Additionally, we present a Scope-tailored attack designed to evade Scope, but it remains ineffective even when maximizing stealthiness, further underscoring the robustness of Scope. We release our source code at:https://github.com/siquanhuang/Scope.

Public-key Encryption with Keyword Search (PEKS) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform (on-demand) keyword searches over encrypted data for data users. Existing PEKS schemes are limited to precise or fuzzy keyword searches, creating a gap given the widespread use of wildcards for rapid searches in real-world applications. To address this issue, several wildcard keyword search schemes have been proposed to support wildcard searches in the public-key setting. However, these schemes suffer from inefficiency and/or inflexibility. Worse yet, they are all vulnerable to (insider) keyword guessing attacks (KGA), which is highly effective when the keyword space is polynomial in size. To address these vulnerabilities, this paper first proposes a new wildcard keyword search scheme called Public-key Encryption with Wildcard Search (PEWS), which is built based on the standard Decisional Diffie-Hellman (DDH) assumption. The complexity of all algorithms in PEWS increases linearly with the keyword length, while remaining almost constant or even decreasing linearly with the number of wildcards. To resist against (insider) KGA, we further extend PEWS into the first Public-key Authenticated Encryption with Wildcard Search (PAEWS) scheme. Our PEWS and PAEWS schemes are highly flexible, supporting searches for any number of wildcards positioned anywhere within the keyword. We conduct a comprehensive performance evaluation of our PEWS and PAEWS, while also comparing PEWS with the state-of-the-art scheme in the public-key setting. The experimental results demonstrate that both PEWS and PAEWS are efficient and practical, and the experimental comparisons illustrate that PEWS achieves approximately$2 \times $faster computation and reduces communication by at least 50%.

Deep neural networks (DNNs) is susceptible to surrogate attacks, where adversaries use surrogate data and corresponding outputs from the target model to build their own stolen model. Model stealing attacks jeopardize model privacy and model owners' commercial benefits. To address this issue, this paper proposes a hybrid protection approach-Maximize the confidence differences between benign samples and adversarial samples (MCD), to protect models from theft. Firstly, the LogitNorm approach is used to overcome the overconfidence problem in adversary query classification. Then, samples are divided into four groups according to ES and RS. Different groups are poisoned by different degrees. In addition to enhancing defensive performance and accounting for model integrity, the MCD uses a trigger to confirm the cloned model's owner. Experimental results show that the MCD defends against a variety of original models and attack techniques well. Against KnockoffNets and DFME attacks, the MCD yields an average defense performance of 54.58 % on five datasets, which is a great improvement over other defenses. Compared to other poisoning techniques, the Strong Poisoning (SP) module reduces the adversary's accuracy by 48.23 % on average. Additionally, the MCD overcomes the issue of OOD overconfidence while safeguarding the model accuracy in OOD detection and reduces the misclassification rate of ID samples for multiple OOD datasets.

Federated learning (FL) allows a large number of users to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data. The main security issues of FL, that is, the privacy of the gradient vector and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. To protect the privacy of the gradient, a secure aggregation was proposed; to verify the correctness of the aggregated gradient, a verifiable secure aggregation that requires the server to provide a verifiable aggregated gradient was proposed. In 2021, Hahn et al proposed VERSA, a verifiable secure aggregation. However, in this paper, we will point out a flaw in VERSA, which indicates that VERSA does not work. To address the flaw, we present several approaches with different advantages and disadvantages. We hope that by identifying the flaw, similar errors can be avoided in future designs of verifiable secure aggregation.

Privacy-preserving data aggregation in mobile crowdsensing (MCS) focuses on mining information from massive sensing data while protecting users' privacy. The existence of multiple concurrent tasks is common in urban environments, so privacy-preserving multi-task data aggregation is essential and useful to a large-scale crowdsensing server. However, existing privacy-preserving data aggregation schemes in MCS mainly focus on the single-task data aggregation and the privacy protection of user's data. Little attention is paid to the privacy of user's decision of accepting tasks. Therefore, we propose a privacy-preserving and server-oriented efficient multi-task data aggregation scheme for MCS based fog computing. The proposed scheme can aggregate multiple concurrent tasks from multiple requesters (e.g., for 9 tasks, the proposed scheme completes all tasks in one round as opposed to existing schemes, which finish 9 tasks in nine rounds). Our scheme protects the privacy of user's decision, user's data, and aggregation result of each requester under collusion attacks. Through formal security analyses, our scheme is proved to be secure and privacy-preserving. Both theoretical analyses and experiments show our scheme is efficient.

Proxy re-encryption (PRE), as a promising cryptographic primitive for secure data sharing in clouds, has been widely studied for decades. PRE allows the proxies to use the re-encryption keys to convert ciphertexts computed under the delegator’s public key into ones that can be decrypted using the delegatees’ secret keys, without knowing anything about the underlying plaintext. This delegable property of decryption rights enables flexible cloud data sharing, but it raises an important issue: if some proxies reveal their re-encryption keys, or collude with some delegatees to create a pirate decoder, then anyone who gains access to the pirate decoder can decrypt all ciphertexts computed under the delegator’s public key without the delegator’s permission. This paper opens up a potentially new avenue of research to address the above (re-encryption) key abuse problem by proposing the first public trace-and-revoke PRE system, where the malicious delegatees and proxies involved in the generation of a pirate decoder can be identified by anyone who gains access to the pirate decoder, and their decryption capabilities can subsequently be revoked by the content distributor. Our construction is multi-hop, supports user revocation and public (black-box) traceability, and achieves significant efficiency advantages over previous constructions. Technically, our construction is a generic transformation from inner-product functional PRE (IPFPRE) that we introduce to trace-and-revoke PRE. In addition, we instantiate our generic construction of trace-and-revoke PRE from the Learning with Errors (LWE) assumption, which was widely believed to be quantum-resistant. This is achieved by proposing the first LWE-based IPFPRE scheme, which may be of independent interest. Finally, we conduct a comprehensive performance evaluation of our LWE-based trace-and-revoke PRE scheme, and the experimental results show that the proposed LWE-based trace-and-revoke PRE scheme is practical and outperforms current state-of-the-art traceable PRE schemes.

Fine-grained access control systems facilitate granting differential access rights to a set of users and allow flexibility in specifying the access rights of individual users. As an important fine-grained access control technique, key-policy attribute-based encryption (KP-ABE) has been introduced to achieve fine-grained access control over encrypted data, where each ciphertext is associated with an attribute set such that users satisfying the attribute set can decrypt the ciphertext. In the real-world application scenarios of KP-ABE, various situations such as users leaving the system, compromise of users’ private keys, and business requirements frequently occur, necessitating the revocation of decryption rights for large-scale users. To address the user revocation, numerous revocable KP-ABE schemes have been proposed. However, existing revocable KP-ABE schemes are vulnerable to quantum computer attacks. More importantly, existing solutions fail to address the user addition, where users capable of decrypting certain ciphertexts would like to grant decryption rights to others; this is a highly common requirement, such as changes in user decryption permissions and business needs. This paper explores a potentially new avenue of research to address the above issues by introducing a novel cryptographic primitive called key-policy ABE with switchable attributes (KP-ABE-SA). In the KP-ABE-SA system, each ciphertext linked to an attribute set can be transformed into one associated with another (distinct) attribute set, enabling both user revocation and addition. Furthermore, to withstand quantum computer attacks, we construct a KP-ABE-SA scheme based on the Learning with Errors (LWE) assumption, which is widely believed to be quantum-resistant. Finally, we conduct a comprehensive performance evaluation of our LWE-based KP-ABE-SA scheme, and the experimental results show that the proposed LWE-based KP-ABE-SA scheme is efficient and practical.

The rapid development of cloud computing and the exponential growth of data have led to an increasing demand for secure data sharing and querying. Proxy re-encryption (PRE) addresses the issue of secure data sharing, since it enables controlled data sharing and delegation of access rights without revealing the actual content of the encrypted data stored in the cloud; public-key encryption with keyword search (PEKS) tackles the issue of secure data querying, since it allows resource-constrained clients to effectively search over encrypted data stored in the cloud. As a combination of PRE and PEKS, proxy re-encryption with keyword search (PRES) enables both secure data sharing and querying. Despite their merits, existing PRES schemes are vulnerable to quantum computer attacks, keyword guessing attacks (KGAs), or incur high end-to-end delay. To address these vulnerabilities, this paper introduces a novel cryptographic primitive called public-key authenticated re-encryption with keyword search (Re-PAEKS), which combines the strengths of PRE and public-key authenticated encryption with keyword search (PAEKS). Our Re-PAEKS has low end-to-end delay, and is resistant to both quantum computer attacks and KGAs. Technically, we improve the previous lattice-based PAEKS scheme and achieve the delegation of access rights by exploiting the lattice-based identity-based encryption (IBE) techniques, which are widely believed to be secure against quantum computer attacks. In addition, we formalize the security model of the Re-PAEKS and prove its security in the random oracle model. Finally, we conduct a comprehensive performance evaluation of the Re-PAEKS, and the experimental results show that the Re-PAEKS is computationally efficient and practical. Particularly, the Re-PAEKS enjoys the lowest end-to-end delay compared to current state-of-the-art PRES.

The widespread adoption of cloud computing and the exponential growth of data highlight the need for secure data sharing and querying. Attribute-based keyword search (ABKS) has emerged as an efficient means of searching encrypted data stored in the cloud. However, existing ABKS schemes incur high end-to-end delay and are vulnerable to quantum computer attacks and/or (insider) keyword guessing attacks (KGA). To address these vulnerabilities, this paper introduces a new concept called attribute-based authenticated encryption with keyword search (ABAEKS) and proposes an efficient ABAEKS scheme. Our ABAEKS has low end-to-end delay, and is resistant to both quantum computer attacks and (insider) KGA. In addition, we formalize the security model of ABAEKS system and prove its security in the random oracle model. Finally, we conduct a comprehensive performance evaluation of ABAEKS, and the experimental results show that our ABAEKS is computationally efficient and outperforms current state-of-the-art ABKS schemes.

In mobile crowdsensing (MCS), providing appropriate rewards is a common and efficient way to motivate participants to participate in sensing tasks. However, the privacy of task participants is not protected well in most quality-aware incentive schemes. Moreover, these schemes are designed for general MCS application scenarios where data are collected by internal sensors embedded in participants’ smartphones, and not suitable for scenarios where additional sensors (ASs) except internal sensors are to collect data (e.g., household medical devices). In scenarios with ASs, malicious participants can fabricate sensing data instead of collecting data from ASs, i.e., the source reliability of sensing data cannot be ensured. To address these issues, we propose P2SIM, a privacy-preserving and the source-reliable incentive mechanism scheme for MCS with ASs. We combine redactable signature with private hash function to achieve the source reliability verification of sensing data without revealing the privacy of participants. Moreover, rewards are divided into two parts: 1) fixed rewards and 2) floating rewards, to enhance the flexibility of rewards distribution. Both formal theoretical analysis and extensive experimental evaluations on a real data set show that the proposed P2SIM is secure and efficient.

Data aggregation is a fundamental problem in mobile crowdsensing (MCS). However, the existing approaches are still unsatisfactory considering the privacy protection of sensing data and aggregation results. In addition, most existing privacy-preserving data aggregation schemes can only support a single type of aggregation, which limits their application scenarios. To address these issues, we propose a novel privacy-preserving and customization-supported data aggregation scheme that can achieve multiple types of aggregation. Specifically, we utilize additive secret sharing ($\mathcal {ASS}$) to protect the privacy of both sensing data and aggregation results and then propose a simplified secure triplet generation protocol based on$\mathcal {ASS}$to construct secure aggregation operations. Moreover, we design a secure comparison (SC) algorithm and a secure top-$K$algorithm to realize customized aggregation (i.e., statistical aggregation over top-$K$largest or smallest values of sensing data). The formal theoretical analysis demonstrates that the proposed scheme is effective, and the extensive experiments conducted on a real-world data set show that the proposed approach is privacy preserving and efficient.

Wireless medical sensor networks (WMSNs) have aroused widespread attention in recent years with the development of Internet of Things (IoT) technology. WMSNs offer many new opportunities for healthcare professionals to monitor patients and patient self-monitoring. To overcome the resource (such as memory and power) limitations of sensors and attain data security of patients’ private medical information, researchers have designed plenty of work for securing WMSNs. For years, certificate-based aggregate signature (CBAS) schemes have been put forward for WMSNs to prevent patients’ sensitive medical data from being tampered with and damaged. In this work, we analyze the security flaws of a very recent CBAS scheme proposed by Vermaet al.(2021) by presenting two types of security attacks. We later propose a CBAS scheme with user anonymity protection for WMSNs and prove its security based on the standard cryptographic assumption. The performance comparison results from theory and experiment illustrate the practicality of our design.

Fog-assisted mobile crowdsensing (FA-MCS) alleviates challenges with respect to computation, communication, and storage from the traditional model of mobile crowdsensing (MCS) “requester-server-users.” Data aggregation, as a specific MCS task, has attracted a lot of attentions in mining the potential value of the massive crowdsensing data. However, the process of data aggregation in FA-MCS may threaten the privacies of both users' data and aggregation results. The untrusted server and fog nodes (FNs) may damage the correctness of aggregation results. Moreover, bad FNs, which do not upload data to server or fail to verify successfully, can endanger the reliability of FA-MCS and the accuracy of aggregation results. To tackle these problems, we propose a verifiable, reliable, and privacy-preserving data aggregation scheme for FA-MCS. Specifically, the proposed scheme preserves privacies of both users' data and aggregation results, enables requester to verify the correctness of aggregation result, and is able to tolerate several bad FNs without affecting the data aggregation result. Through formal security analysis, the proposed scheme is shown to be secure and privacy preserving. Extensive experiments also show the proposed scheme is efficient and reliable.