Opca: Enabling Optimistic Concurrent Access for Multiple Users in Oblivious Data Storage

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Yuezhi Che;Dazhao Cheng;Xiao Wang;Rujia Wang
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Abstract

The challenges of data privacy and security posed by data outsourcing are becoming increasingly prevalent. Oblivious RAM (ORAM)-based oblivious data storage guarantees data confidentiality through data encryption and access pattern obfuscation. However, it suffers from performance degradation and low throughput. To address these issues, the concurrency of ORAM in a multi-user scenario has been explored. We investigate several existing concurrent oblivious data storage solutions and discover that a trusted proxy is used to serve concurrent accesses between users and storage, with processing locks involved in the proxy to ensure correctness and prevent conflicts. The proxy-based system is inherently prone to pessimistic concurrency control, and as the number of users grows, a proxy might become a performance bottleneck, causing significant delays. In this study, we propose Opca, a novel oblivious data storage framework that enables optimistic concurrent access. Opca refines the proxy design by temporally storing multiple versions of modified data with labeled timestamps, committing only the latest version to the storage during a separate processing period. Opca is implemented and evaluated in different real-world storage backends with a scalable number of users, and its performance is compared to alternative schemes. Opca outperforms the state-of-the-art concurrent oblivious storage system TaoStore, which relies on a similar system setting. Our results show that Opca can improve 3.77x throughput and reduce 73.5% response time.
Opca:在遗忘数据存储中实现多用户优化并发访问
数据外包带来的数据隐私和安全挑战越来越普遍。基于遗忘内存(ORAM)的遗忘数据存储通过数据加密和访问模式混淆来保证数据的机密性。然而,它存在性能下降和吞吐量低的问题。为了解决这些问题,我们探索了多用户情况下遗忘内存的并发性。我们研究了几种现有的并发遗忘数据存储解决方案,发现用户和存储之间的并发访问使用可信代理服务,代理中涉及处理锁,以确保正确性并防止冲突。基于代理的系统在本质上容易造成并发控制的悲观,随着用户数量的增加,代理可能会成为性能瓶颈,造成严重的延迟。在本研究中,我们提出了一种新型遗忘式数据存储框架 Opca,它可以实现乐观的并发访问。Opca 改进了代理设计,在时间上存储了多个带时间戳的修改数据版本,在单独的处理期间只将最新版本提交到存储中。Opca 在用户数量可扩展的不同实际存储后端中进行了实施和评估,并将其性能与其他方案进行了比较。Opca 的性能优于最先进的并发遗忘存储系统 TaoStore,后者依赖于类似的系统设置。结果表明,Opca 的吞吐量提高了 3.77 倍,响应时间缩短了 73.5%。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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