In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, while an untrusted central server aggregates the encrypted models to obtain an updated global model. Prior work has demonstrated how to verify the correctness of aggregation in such a setting. However, such verification relies on strong assumptions, such as a trusted setup among all users under unreliable network conditions, or it suffers from expensive cryptographic operations, such as bilinear pairing. In this paper, we scrutinize the verification mechanism of prior work and propose a model recovery attack, demonstrating that most local models can be leaked within a reasonable time (e.g., 98% of encrypted local models are recovered within 21 h). Then, we propose VerSA, a verifiable secure aggregation protocol for cross-device federated learning. VerSA does not require any trusted setup for verification between users while minimizing the verification cost by enabling both the central server and users to utilize only a lightweight pseudorandom generator to prove and verify the correctness of model aggregation. We experimentally confirm the efficiency of VerSA under diverse datasets, demonstrating that \textsc{VerSA} is orders of magnitude faster than verification in prior work.

In cloud storage services, deduplication technology is commonly used to reduce the space and bandwidth requirements of services by eliminating redundant data and storing only a single copy of them. Deduplication is most effective when multiple users outsource the same data to the cloud storage, but it raises issues relating to security and ownership. Proof-of-ownership schemes allow any owner of the same data to prove to the cloud storage server that he owns the data in a robust way. However, many users are likely to encrypt their data before outsourcing them to the cloud storage to preserve privacy, but this hampers deduplication because of the randomization property of encryption. Recently, several deduplication schemes have been proposed to solve this problem by allowing each owner to share the same encryption key for the same data. However, most of the schemes suffer from security flaws, since they do not consider the dynamic changes in the ownership of outsourced data that occur frequently in a practical cloud storage service. In this paper, we propose a novel server-side deduplication scheme for encrypted data. It allows the cloud server to control access to outsourced data even when the ownership changes dynamically by exploiting randomized convergent encryption and secure ownership group key distribution. This prevents data leakage not only to revoked users even though they previously owned that data, but also to an honest-but-curious cloud storage server. In addition, the proposed scheme guarantees data integrity against any tag inconsistency attack. Thus, security is enhanced in the proposed scheme. The efficiency analysis results demonstrate that the proposed scheme is almost as efficient as the previous schemes, while the additional computational overhead is negligible.

Cloud storage provides scalable and low cost resources featuring economies of scale based on multi-tenant architecture. As the amount of data outsourced grows explosively, data deduplication, a technique that eliminates data redundancy, becomes essential. However, deduplication leads to problems with data confidentiality, thereby necessitating secure deduplication solutions. Server-aided encryption schemes have been proposed to achieve the strongest confidentiality but with the cost of managing a key server (KS). Previous schemes, however, are based on a centralized KS that uses only a single secret key assuming a single KS in the system. In cloud storage where multi-tenancy and scalability are crucial, such schemes degrade not only the effectiveness of deduplication but also the scalability with increasing users. In this paper, we extend server-aided encryption to a decentralized setting that consists of multiple KSs. The key idea of our proposed scheme is to construct an inter-KS deduplication algorithm, by which a cloud storage service provider can perform deduplication over ciphertexts from different KSs within a tenant or across tenants. This way, our scheme simultaneously offers flexibility of KS management and cross-tenant deduplication over encrypted data. The novelty of the approach is using a decentralized architecture that does not require any centralized entities for the coordination or pre-sharing of secrets among KSs. Therefore, it allows cloud storage services to offer high deduplication efficiency and scalability while preserving strong data confidentiality. We show the result of performance analysis on the proposed scheme by conducting extensive experiments. In addition, our security analysis demonstrate that the proposed scheme satisfies all desired security properties.

With the successful proliferation of data outsourcing services, security and privacy issues have drawn significant attention. Data authentication in particular plays an essential role in the storage of outsourced digital content and keeping it safe from modifications by inside or outside adversaries. In this paper, we focus on online data authentication using a Merkle (hash) tree to guarantee data integrity. By conducting in-depth diagnostics of the side channels of the Merkle tree-based approach, we explore novel solutions to improve the security and reliability of the maintenance of outsourced data. Based on a thorough review of previous solutions, we present a new method of inserting auxiliary random sources into the integrity verification proof on the prover side. This prevents the exposure of partial information within the tree structure and consequently releases restrictions on the number of verification execution, while maintaining desirable security and reliability of authentication for the long run. Based on a rigorous proof, we show that the proposed scheme maintains consistent reliability without being affected by continuous information leakage caused by repetitions of the authentication process. In addition, experimental results comparing with the proposed scheme with other state-of-the-art studies demonstrate its efficiency and practicality.

Public auditing enables efficient integrity checks of data assigned to cloud servers. In this article, we revisit the public auditing for encrypted data, in which a major concern is how to effectively support data dynamics, i.e., data modification, insertion, and deletion. We first determine which factor in existing auditing schemes most limits data dynamics from a cost perspective. We then propose a novel public auditing scheme that provides data dynamics that are orders of magnitude faster than previous methods. Our auditing challenge-response protocol reduces the computation cost of the third-party auditor (TPA) significantly, thus increasing the verification speed for the auditing results. Performance and security analysis demonstrates that the proposed scheme generates minimal computation costs while guaranteeing data integrity and privacy against an untrusted cloud.