Privacy-preserving and fine-grained data aggregation framework for crowdsourcing
Gaoqiang Zhuo · 2017
Crowdsourcing is a new way of finishing a task, especially for data aggregation, where the requester can out-sourcing her task to the workers and get the aggregated results. Despite the benefits, current crowdsourcing system has serious security and privacy issues impeding the wide acceptance of crowdsourcing. In particular, a requester cannot achieve privacy-preserving fine-grained access control to her task, and the workers' data privacy is not well preserved in existing crowdsourcing systems. The concerns on privacy make requester and workers lose incentive in using or participating in crowdsourcing. To address these issues, we propose a privacy-preserving and fine-grained data aggregation framework especially for crowdsourcing system. Security and performance analysis show the security, feasibility, and efficiency of our scheme.