Secure Crowdsourcing Classification with Privacy Protection against Servers

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This project aims to enable comprehensive quality data classification via secure crowdsourcing. The quality of a data-intensive process, such as a Machine Learning algorithm, depends on the input data quality. By using a crowdsourcing classification, the project expects to overcome the painstaking and costly process of humans correctly annotating extensive input data from diverse real information. The expected outcomes are innovative technologies, guaranteeing accuracy and confidentiality of annotation results whilst protecting the privacy of data classification results. It enhances data-intensive outputs quality, which will benefit large data-intensive applications, such as cybersecurity protections via intrusion detection.