Graph Neural Networks (GNNs) effectively model long-range dependencies by capturing high-order relationships in user-item graphs, emerging as a mainstream paradigm for building personalized recommender services within distributed service environments. However, interaction data contains highly sensitive user behavior, and centralized modeling faces significant leakage risks. While existing privacy-preserving schemes aim to address this, they lack balance in privacy-utility trade-offs and comprehensive protection. To this end, we introduce PRGNN, a privacy-preserving framework based on a fully-separable GNN, which achieves effective protection of graph structures and node features through cross-client collaborative learning of distributed subgraphs. We first propose a high-order neighborhood aggregation module (HAM), which adopts functional encryption to realize secure multi-hop propagation through self-aggregation of local adjacency matrices, protecting the privacy of the graph structure. Then, a cross-domain secure sharing module (CSM) is designed to achieve secure feature data sharing from client domains to the cloud domain based on the output of HAM. Finally, the privacy-preserving training module (PTM) integrates HAM with CSM to achieve parameter updates. Experiments on three benchmark datasets show PRGNN achieves accuracy comparable to centralized GNNs. Compared with similar privacy-preserving schemes, its modeling efficiency is improved by at least 22.06%, and the communication overhead is reduced by at least$1.62\times$.

Data sharing is ubiquitous in the metaverse, which adopts blockchain as its foundation. Blockchain is employed because it enables data transparency, achieves tamper resistance, and supports smart contracts. However, securely sharing data based on blockchain necessitates further consideration. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising primitive to provide confidentiality and fine-grained access control. Nonetheless, authority accountability and key abuse are critical issues that practical applications must address. Few studies have considered CP-ABE key confidentiality and authority accountability simultaneously. To our knowledge, we are the first to fill this gap by integrating non-interactive zero-knowledge (NIZK) proofs into CP-ABE keys and outsourcing the verification process to a smart contract. To meet the decentralization requirement, we incorporate a decentralized CP-ABE scheme into the proposed data sharing system. Additionally, we provide an implementation based on smart contract to determine whether an access control policy is satisfied by a set of CP-ABE keys. We also introduce an open incentive mechanism to encourage honest participation in data sharing. Hence, the key abuse issue is resolved through the NIZK proof and the incentive mechanism. We provide a theoretical analysis and conduct comprehensive experiments to demonstrate the feasibility and efficiency of the data sharing system. Based on the proposed accountable approach, we further illustrate an application in GameFi, where players can play to earn or contribute to an accountable DAO, fostering a thriving metaverse ecosystem.

How to achieve distributed differential privacy (DP) without a trusted central party is of great interest in both theory and practice. Recently, the shuffle model has attracted much attention. Unlike the local DP model in which the users send randomized data directly to the data collector/analyzer, in the shuffle model an intermediate untrusted shuffler is introduced to randomly permute the data, which have already been randomized by the users, before they reach the analyzer. The most appealing aspect is that while shuffling does not explicitly add more noise to the data, it can make privacy better. The privacy amplification effect in consequence means the users need to add less noise to the data than in the local DP model, but can achieve the same level of differential privacy. Thus, protocols in the shuffle model can provide better accuracy than those in the local DP model. What looks interesting to us is that the architecture of the shuffle model is similar to private aggregation, which has been studied for more than a decade. In private aggregation, locally randomized user data are aggregated by an intermediate untrusted aggregator. Thus, our question is whether aggregation also exhibits some sort of privacy amplification effect? And if so, how good is this “aggregation model” in comparison with the shuffle model. We conducted the first comparative study between the two, covering privacy amplification, functionalities, protocol accuracy, and practicality. The results as yet suggest that the new shuffle model does not have obvious advantages over the old aggregation model. On the contrary, protocols in the aggregation model outperform those in the shuffle model, sometimes significantly, in many aspects.

Solving the large-scale linear system of equations is one of the most fundamental problems both in theory and practice. However this problem requires too much computational resource for most users to solve it. With the rapid development of cloud services, many users tend to outsource the expensive computing to the cloud server, which is regarded as an efficient way of solving such problem. Nevertheless, the cloud server can not protect the data privacy well, especially when the user's linear system of equations contain private and sensitive data. There are many previous research works on secure outsourcing of systems of linear equations. In this paper we first analyze a privacy preserving CGM (conjugate gradient method) algorithm for secure outsourcing of large-scale systems of linear equations proposed in [1] . We find that the cloud server can recover the protected coefficient matrix of the linear system of equations from the message it receives, which makes the security method in this scheme fails. This is a serious problem, which makes the private and sensitive data of the user leak to the cloud server, and privacy preserving does not exist. To overcome this problem, we modified this algorithm to protect the message from leaking, which can protect the users' privacy well. We also show the security of this new scheme and do experiments to show its efficiency.

Abstract There is an increasing trend for data owners to outsource their data to an untrusted cloud provider. Besides providing the storage for the data, the service provider could allow the data owner or authorized clients to search over the data. To guarantee the data secure, the owner must encrypt his or her data before sending to the cloud. However, traditional encryption does not allow searching without decrypting the data. Searchable symmetric encryption is one approach that allows users to search over the encrypted data. For data applications, various different functional search have been proposed, such as wildcard search, similarity keyword search and fuzzy keyword search. Moreover, dynamic addition and removal of files should be supported in practice. However, to our knowledge, there does not exist a searchable symmetric encryption scheme that can support many properties such as more than three functions in all the aforementioned operations. In this paper, we propose an efficient multi‐functional searchable symmetric encryption scheme that can support wildcard search, similarity search (including hamming distance and edit distance), fuzzy keyword search and disjunctive keyword search simultaneously. In the new scheme, the trapdoor changes with various search requests and it enumerates all possibilities of the keyword of the trapdoor. Moreover, we use an array instead of a matrix to reduce the storage, and the scheme can be constructed efficiently in terms of both computational and space complexity. Our scheme is based on the Bloom filter, and it is secure against non‐adaptive chosen keyword attack. With the dynamic technique for the inverted index, our scheme can support dynamic operation such as addition and removal of data files, which can also be secure against adaptive chosen keyword attack. Copyright © 2015 John Wiley & Sons, Ltd.