SEPAR: Towards Regulating Future of Work Multi-Platform Crowdworking Environments with Privacy Guarantees
This addresses the need for regulatory compliance and privacy in the gig economy, but it is incremental as it builds on existing blockchain and privacy techniques for a specific domain.
The paper tackles the problem of enforcing cross-platform regulations like work hour limits in multi-platform crowdworking systems while preserving participant privacy, proposing SEPAR, a system that uses anonymous tokens and blockchains to achieve this with formal privacy guarantees and demonstrated efficiency in performance and scalability.
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.