SVOC: Secure Aggregatable and Extractable System for Outsourced Cloud Computation

计算机科学 云计算 计算 计算机安全 操作系统 算法
作者
Willy Susilo,Yumei Li,Fuchun Guo,Zhen Zhao,Yannan Li,Chunpeng Ge
出处
期刊:IEEE Transactions on Dependable and Secure Computing [IEEE Computer Society]
卷期号:22 (6): 5876-5887
标识
DOI:10.1109/tdsc.2025.3576386
摘要

With the rapid advancement of technology, cloud computing has emerged as the most popular and promising service platform. A cloud user can delegate heavy computation tasks to cloud servers. To ensure the correctness of outsourced processing (e.g., machine learning and data mining), the cloud server must prove that the processing has been executed properly. However, even without malicious intent, it is possible for a cloud server to produce incorrect results. Consequently, clients may outsource the same task to multiple cloud servers and receive various results, aiding them in selecting the best outcome. To protect data privacy, the cloud server must encrypt the results before sending them back to the user. Yet, processing and verifying encrypted results remain significant challenges. To avoid the expensive computational overhead of decrypting ciphertexts from cloud servers one by one, clients prefer to use homomorphic encryption (HE) to obtain the combined output from a single server. However, existing schemes fall short of efficiently verifying the correctness of computations over encrypted data processed by multiple cloud servers, especially in extracting the results computed by each server. In this paper, we introduce a new framework for verifiable outsourced computing systems. In this system, each cloud server's computation result is protected by Paillier encryption, and the edge server can verify these results using zero-knowledge proofs and aggregate the verified ciphertexts. The client can extract the combined plaintext through the Base-3 conversion algorithm to identify each cloud server's results and any non-participating servers. We also prove the security of our scheme and analyze its performance from both theoretical and experimental aspects. Performance analysis shows that our system significantly reduces the client's workload and is userfriendly
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