The valuable insights that can be inferred from the analytics of data generated and collected from various devices and applications are transforming businesses and are therefore one of the key motivations for organizations to adopt such technologies. Nevertheless, the data being analyzed and processed are often highly sensitive and put individuals’ privacy at risk. The current European General Data Protection Regulation (GDPR) represents a major challenge for companies (especially small-to-medium enterprises) as they are required to follow a privacy-by-design approach in their systems and adopt privacy-enhancing technologies. Such technologies protect data to ensure individuals’ privacy while allowing the processing of data in a meaningful and useful manner
The PAPAYA project aims to address privacy concerns when data analytics tasks are performed by untrusted, third-party data processors. Since these tasks may be performed obliviously on protected data (i.e., encrypted data), the PAPAYA team is striving to design and develop dedicated privacy-preserving data analytics modules that will enable data owners to extract valuable information from protected data, while being cost-effective and accurate.
Our contribution to the project:
- Privacy-preserving training and application of deep neural networks
- Privacy-preserving collaborative training of deep neural networks
- Platform for privacy-preserving analytics