论文标题
分开:旨在规范工作的未来多平台的人群以保证的方式保证
SEPAR: Towards Regulating Future of Work Multi-Platform Crowdworking Environments with Privacy Guarantees
论文作者
论文摘要
拥挤的平台为不同的工人提供了为不同请求者执行任务的机会。 “演出”经济的受欢迎程度引起了独立平台,这些平台提供了竞争和互补的服务。工人以及具有特定任务的请求者可能需要为多个平台的服务工作或利用,从而导致多平台拥挤系统的上升。最近,政府,法律和社会机构在拥挤的平台上执行法规(例如最少和最大工作时间)的兴趣越来越大。因此,多平台人群工作系统中的平台需要协作以执行跨平台法规。在合作执行全球法规的同时,需要透明地共享有关任务及其参与者的信息,但需要保留所有参与者的隐私。在本文中,我们提出了一个整体愿景,探讨了工作多平台人群工作环境的未来的法规,隐私和架构维度。然后,我们提出了一个多平台的人群工作系统,该系统以隐私性的方式对一组分布式独立平台进行了实用的全球法规。使用轻量级和匿名代币强制执行隐私,而使用跨多个平台共享的容忍故障区块链实现透明度。分离秘密对手的隐私保证是正式的,并彻底证明了,而实验则揭示了绩效和可扩展性方面的分离效率。
Crowdworking platforms provide the opportunity for diverse workers to execute tasks for different requesters. The popularity of the "gig" economy has given rise to independent platforms that provide competing and complementary services. Workers as well as requesters with specific tasks may need to work for or avail from the services of multiple platforms resulting in the rise of multi-platform crowdworking systems. Recently, there has been increasing interest by governmental, legal and social institutions to enforce regulations, such as minimal and maximal work hours, on crowdworking platforms. Platforms within multi-platform crowdworking systems, therefore, need to collaborate to enforce cross-platform regulations. While collaborating to enforce global regulations requires the transparent sharing of information about tasks and their participants, the privacy of all participants needs to be preserved. In this paper, we propose an overall vision exploring the regulation, privacy, and architecture dimensions for the future of work multi-platform crowdworking environments. We then present SEPAR, a multi-platform crowdworking system that enforces a large sub-space of practical global regulations on a set of distributed independent platforms in a privacy-preserving manner. SEPAR, enforces privacy using lightweight and anonymous tokens, while transparency is achieved using fault-tolerant blockchains shared across multiple platforms. The privacy guarantees of SEPAR against covert adversaries are formalized and thoroughly demonstrated, while the experiments reveal the efficiency of SEPAR in terms of performance and scalability.