The vulnerability of graph-based recommender systems to spurious correlations has become a significant obstacle to their practical deployment, hindering their robustness in out-of-distribution (OOD) scenarios. While existing approaches offer partial solutions, they are limited by fundamental shortcomings: model-centric approaches reliant on predefined causal graphs often suffer from suboptimal performance due to complex and dynamic environmental influences. These methods typically require identifying an environmental label or performing feature decoupling, but hidden environments are often difficult to model. Furthermore, existing general feature decoupling methods fail to account for the unique structural characteristics of graphs. To overcome these challenges, we advocate for a shift towards explicit, geometrically-grounded disentanglement. Hyperbolic geometry is particularly suited for this task due to its capacity to model the inherent hierarchies of user interests. We introduce C-HyPOD : Causal Hyperbolic Representation Learning with Prototype Orthogonal Disentanglement, a novel framework designed for graph-based OOD recommendation. Unlike traditional methods, C-HyPOD transforms disentanglement into a concrete geometric task. It introduces a global interest space by learning a single set of universal interest prototypes. They provides a superior geometric foundation for ensuring these prototypes are well-separated and semantically distinct. To ensure a complete separation and prevent information leakage, a targeted orthogonality constraint is then applied. This constraint purifies the aggregated causal representation by forcing it to be orthogonal to the spurious representation in the tangent space, thereby eliminating their linear correlation. Extensive experiments on four public datasets demonstrate that C-HyPOD significantly improves OOD robustness and recommendation performance, surpassing state-of-the-art methods.

Cloud-fog-assisted electronic health record (EHR) systems offer promising solutions for large-scale medical data storage and processing. However, they also raise critical privacy concerns, particularly regarding secure computation over sensitive data, fine-grained bilateral access control, dynamic revocation, and decryption key exposure. Existing cryptographic primitives, such as functional encryption and matchmaking encryption, address some of these challenges individually but fail to offer a unified solution. In this work, we design a revocable and privacy-preserving data computing system with bilateral access control (RPDC-BAC) for cloud-fog-assisted EHR sharing by proposing a novel cryptographic primitive, called server-aided revocable attribute-based matchmaking functional encryption (SR-AB-MFE). Specifically, the proposed scheme supports expressive bilateral access control and computation over encrypted data. In addition, it incorporates time-evolving decryption keys and a server-aided revocation mechanism to mitigate key exposure and efficiently revoke users. To further reduce receiver-side overhead, fog nodes assist in ciphertext authentication and partial decryption. We formally define the proposed primitive and prove its security under static assumptions. Finally, extensive experimental results demonstrate the efficiency and practicality of our design.

The integration of cloud-edge computing with Internet of Things (IoT) healthcare greatly improves medical service efficiency and reduces home monitoring costs. However, in an untrusted and open environment, it still faces significant privacy and security challenges, especially in terms of confidentiality and authenticity of medical data, as well as bilateral access control between patients and healthcare providers. At present, there are few solutions capable of addressing the aforementioned issues efficiently, as they typically come with significant communication and computational overhead. This poses a substantial challenge for IoT devices that are usually resource-constrained. To address the above issues, this paper proposes an efficient and verifiable fine-grained bilateral access control scheme for cloud-edge IoT healthcare. The scheme adopts flexible attribute-based threshold bilateral access control to provide data confidentiality and authenticity at the same time. In addition, our scheme achieves constant-size ciphertexts, utilizes offline/online technology to accelerate ciphertext generation, and outsources the data authenticity verification and partial decryption process to edge nodes, thereby improving the communication and computational efficiency. Furthermore, our scheme implements verification of outsourced results to resist attacks from malicious edge nodes. The formal security proof and experimental evaluation show that our scheme is more functional and practical than other bilateral access control schemes for IoT healthcare.

Private set intersection (PSI) is an important cryptographic primitive with many real-world applications. Delegating PSI computation to the cloud can effectively reduce management and computational costs of data owners, and therefore has received widespread attention from researchers. However, in the existing delegated PSI schemes, it is hard for the data owner to authorize only a part of its outsourced data, and for users to flexibly select a part of the outsourced encrypted data of the data owner for intersection computation. In this paper, we propose the multi-subset fine-grained authorization PSI (MA-PSI) over outsourced encrypted datasets. Specially, our protocol is designed for the multi-subset case where each subset is associated with one single tag for data classification. Through tag classification and encryption technology, the data owner can perform fine-grained authorization on its subsets. By querying tags, the user can flexibly select any subset of the data owner for intersection computation. The security definition of the MA-PSI protocol is given, and its security is proved in the semi-honest model. Experimental results show that our protocol is very efficient and the computational cost is linear with the size of the subset. Specifically, the intersection algorithm SetI in our MA-PSI is about 80× (the subset size is 215) - 2500× (the subset size is 210) faster than APSI (Wang et al., IEEE TIFS 2021).

As the deployment of blockchains expands across various industries, the demand for exchanging digital assets among blockchain users has risen. Most of existing solutions either solely support asset exchanges among users on the same blockchain, or have limitations by only enabling cross-chain asset exchanges among a few specific blockchains or requiring an intermediary to involve in the cross-chain transaction. To address this problem, in this paper, we propose the concept of cross-chain digital asset marketplace which enables users across different blockchains to exchange their assets securely and efficiently. We then propose a privacy-preserving and adaptive cross-chain digital asset marketplace scheme, denoted as PACDAM. It adaptively matches purchasers’ requests and ensures atomic and privacy-preserving cross-chain transactions. Built on adaptor signatures and randomizable time-lock puzzles, the cross-chain transaction procedure only relies on the underlying blockchain for signature verification, making PACDAM compatible with various blockchains. Furthermore, this protocol eliminates the necessity for third-party involvement (e.g., brokers) in cross-chain transactions, leading to a substantial enhancement in system efficiency and scalability. We also give a comprehensive security analysis of PACDAM, demonstrating its robustness against common attacks and preserving the privacy of transaction participants. Finally, we conduct a series of experiments, and the results validate the effectiveness of our proposed scheme.

Federated learning (FL) trains a model over multiple datasets by collecting the local models rather than raw data, which can help facilitate distributed data analysis in many real-world applications. Since the model parameters can leak information about the training datasets, it is necessary to preserve the privacy of the FL participants’ local models. Furthermore, FL is vulnerable to poisoning attacks which can significantly decrease the model utility. To settle the above issues, we propose a privacy-preserving and Byzantine-robust FL scheme$\Pi _{\text{P2Brofl}}$that maintains robustness in the presence of poisoning attacks and preserves the privacy of local models simultaneously. Specifically,$\Pi _{\text{P2Brofl}}$leverages three-party computation (3 PC) to securely achieve a Byzantine-robust aggregation method. To improve the efficiency of privacy-preserving local model selection and aggregation, we propose a maliciously secure top-$k$protocol$\Pi _{\text{top}-k}$that has low communication overhead. Moreover, we present an efficient maliciously secure shuffling protocol$\Pi _{\text{shuffle}}$since secure shuffling is necessary for our secure top-$k$protocol. The security proof of the scheme is given and experiments on real-world datasets are conducted in this paper. When the proportion of Byzantine participants is 50%, the error rate of the model only increases by 1.05% while it increases by 23.78% without using our protection.

Blockchain rewriting has become widely explored for addressing data deletion requirements, such as error data deletion, space-saving, and compliance with the “right-to-be-forgotten” rule. However, existing approaches are inadequate for handling cross-chain redaction issues, issues, in facing with the increasing need for inter-chain communication. In particular, transaction rewriting on a blockchain might have relevant effects on the states of other blockchains. The cross-chain interoperability results in inter-chain transactions with more complex dependency relations. The issues pose new challenges to achieve rewriting consistency, for example, ensuring the rewriting of related transactions when a transaction is being modified, and achieve atomic rewriting, whereby two cross-chain transactions must either all, or neither, be processed. This paper introduces a cross-chain solution IvyRedaction, with an emphasis on customizing a decentralized intermediary for generating and maintaining global cross-chain redaction states and transaction dependencies. The paper proposes a novel cross-chain state mapping method with rollback rules, as well as customized block structures and verification algorithms, to address the aforementioned issues. Proof-of-concept experiments are conducted to demonstrate the feasibility of the proposed framework.

Presently, similar sequence search is a fundamental technique in genomic data research. Patients or researchers, who want to check whether they or their research objects have genetic diseases or potential illnesses, need to query similar sequences with their genes in certain genomic databases. As a consequence, this may raise privacy issues since the genomic data are regarded as an identifier of each individual and contain lots of sensitive information. Up to date, some solutions have been brought up for achieving secure similarity search over genomic data, but they are still defective in searching exact similar sequences, supporting fine-grained access control, preventing side information leakage, and being built on strong security models at the same time. In this paper, aiming at the above challenge, we propose a maliciously secure similar sequence search scheme with fine-grained access control over genomic data, named PriGenX. Based on oblivious transfer and authenticated garbling techniques, our scheme also supports secure access control with anonymity for protecting the identity of each party preventing side information leakage, and implementing a flexible over-threshold similarity search. Experimental results and security analysis indicate that our scheme is scalable and maliciously secure.

Abstract With the advent of the screen‐reading era, the confidential documents displayed on the screen can be easily captured by a camera without leaving any traces. Thus, this paper proposes a novel screen‐shooting resilient watermarking scheme for document image using deep neural network. By applying this scheme, when the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the captured photographs. Specifically, the scheme is an end‐to‐end neural network with an encoder to embed watermark and a decoder to extract watermark. During the training process, a distortion layer between encoder and decoder is added to simulate the distortions introduced by screen‐shooting process in real scenes, such as camera distortion, shooting distortion, and light source distortion. Furthermore, a background sensitive loss and a lpips loss are used to improve visual quality of the watermarked document images in the training process. Besides, a strength factor adjustment strategy is also designed to improve the visual quality with little loss of bit extraction accuracy. The experimental results show that the proposed scheme has higher visual quality and robustness than the other two recent state‐of‐the‐art methods.

Genome-Wide Association Study (GWAS) aims at detecting the association between diseases and Single-Nucleotide Polymorphisms (SNPs) with statistical techniques and has great potential for disease diagnosis. To obtain high-quality results, GWAS requires large-scale genomic data containing individuals’ privacy information. Thus, how to improve the efficiency of GWAS while protecting the privacy of genomic data becomes a critical challenge. In this paper, we propose a secure and efficient GWAS scheme. By using secure three-party computation, we present a series of protocols, i.e., Secure Quality Control, Secure Principle Component Analysis, Secure Cochran-Armitage trend test, and Secure Logistic Regression, to cover the most significant procedures of secure GWAS. In these protocols, a new comparison protocol is designed to reduce communication and improve efficiency. Furthermore, by extending the above comparison protocol to be maliciously secure and utilizing other technologies, e.g., consistency check, we extend the whole GWAS scheme to malicious security with rationally additional overhead. Experimental results demonstrate that our protocols achieve about 33% performance improvement than the state-of-art secure GWAS scheme using two-party computation in terms of runtime and communication in the semi-honest setting. The cost of our scheme in the malicious setting is around 1.5X than that in the semi-honest setting.

We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed to enable that a CNN modeldeveloperconvinces auserof the truthful CNN performance over non-public data frommultiple testers, while respecting model and data privacy. To balance the security and efficiency issues, we appropriately integrate three tools with the CNN testing, including collaborative inference, homomorphic encryption (HE) and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). We start with strategically partitioning a CNN model into a private part kept locally by the model developer, and a public part outsourced to an outside server. Then, the private part runs over the HE-protected test data sent by a tester, and transmits its outputs to the public part for accomplishing subsequent computations of the CNN testing. Second, the correctness of the above CNN testing is enforced by generating zk-SNARK based proofs, with an emphasis on optimizing proving overhead for two-dimensional (2-D) convolution operations, since the operations dominate the performance bottleneck during generating proofs. We specifically present a new quadratic matrix program (QMP)-based arithmetic circuit witha single multiplication gatefor expressing 2-D convolution operations between multiple filters and inputs in a batch manner. Third, we aggregate multiple proofs with respect to a same CNN model but different testers’ test data (i.e., different statements) into one proof, and ensure that the validity of the aggregated proof implies the validity of the original multiple proofs. Lastly, our experimental results demonstrate that our QMP-based zk-SNARK performs nearly 13.9× faster than the existing quadratic arithmetic program (QAP)-based zk-SNARK in proving time, and 17.6× faster in Setup time, for high-dimension matrix multiplication. Besides, the limitation on handling a bounded number of multiplications of QAP-based zk-SNARK is relieved.

With the increasing number of traffic accidents and terrorist attacks by modern vehicles, vehicular digital forensics (VDF) has gained significant attention in identifying evidence from the related digital devices. Ensuring the law enforcement agency to accurately integrate various kinds of data is a crucial point to determine the facts. However, malicious attackers or semi-honest participants may undermine the digital forensic procedures. Enabling accountability and privacy preservation while providing secure data access control in VDF is a nontrivial challenge. To mitigate this issue, in this article, we propose a blockchain-based decentralized solution for VDF named BB-VDF, in which the accountable protocols and privacy-preserving algorithm are constructed. The desirable security properties and fine-grained data access control are achieved based on smart contract and the customized cryptographic construction. Specifically, we design a distributed key-policy attribute-based encryption scheme with partially hidden access structures, named DKP-ABE-H, to realize the secure fine-grained forensics data access control. Further, a novel smart contract is designed to model the forensics procedures as a finite state machine, which guarantees accountability that each participant performs auditable cooperation under tamper resistant and traceable transactions. Systematic security analysis and extensive experimental results show the feasibility and practicability of our proposed BB-VDF scheme.

Mobile cloud storage (MCS) provides clients with convenient cloud storage service. In this article, we propose an efficient, secure and privacy-preserving mobile cloud storage scheme, which protects the data confidentiality and privacy simultaneously, especially the access pattern. Specifically, we propose an oblivious selection and update (OSU) protocol as the underlying primitive of the proposed mobile cloud storage scheme. OSU is based on onion additively homomorphic encryption with constant encryption layers and enables the client to obliviously retrieve an encrypted data item from the cloud and update it with a fresh value by generating a small encrypted vector, which significantly reduces the client’s computation as well as the communication overheads. Compared with previous works, our presented work has valuable properties, such as fine-grained data structure (small item size), lightweight client-side computation (a few of additively homomorphic operations) and constant communication overhead, which make it more suitable for MCS scenario. Moreover, by employing the “verification chunks” method, our scheme can be verifiable to resist malicious cloud. The comparison and evaluation indicate that our scheme is more efficient than existing oblivious storage solutions with the aspects of client and cloud workloads, respectively.

Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized framework for crowdsourcing named CrowdBC, in which a requester's task can be solved by a crowd of workers without relying on any third trusted institution, users' privacy can be guaranteed and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, based on which we give a concrete scheme. We further implement a software prototype on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability, and scalability of our proposed crowdsourcing system.

In this paper, we propose the first identity-based broadcast encryption scheme, which can simultaneously achieves confidentiality and full anonymity against adaptive chosen-ciphertext attacks under a standard assumption. In addition, two further desirable features are also provided: one is fully-collusion resistant which means that even if all users outside of receivers S collude they cannot obtain any information about the plaintext. The other one is stateless which means that the users in the system do not need to update their private keys when the other users join or leave our system. In particular, our scheme is highly efficient, where the public parameters size, the private key size and the decryption cost are all constant and independent to the number of receivers.