Compressive sensing based secure multiparty privacy preserving framework for collaborative data-mining and signal processing
In many real-world applications, multiple parties who provide data need to collaboratively perform certain data-mining and signal processing tasks. Security and privacy protection is a critical issue in such application scenarios. In this paper, we propose a compressive sensing (CS) based privacy preserving framework for collaborative data-mining and signal processing using secure multiparty computation (MPC) in which the data-mining and the signal processing are performed in the compressive sensing domain. In our framework, the MPC protocols are used only for compressive sensing transformation and reconstruction while the data-mining/signal processing tasks are de-coupled from MPC operations. So our framework enjoys a great deal of flexibility and scalability when compared to the prior works because the decoupling allows CS transformed data to be reused and many data processing algorithms can be applied in such CS domain. Our framework also enables privacy preserving data storage in the cloud at the same time. Additionally, we develop a MPC based orthogonal matching pursuit algorithm and its corresponding MPC protocol for the CS reconstruction. Our analysis and experimental results demonstrate that the proposed framework is effective in enabling efficient privacy preserving data-mining/signal processing and storage.