Privacy-Preserving Computing and Secure Multi Party Computation

An increased awareness about privacy issues among individuals. In many countries, databases containing personal, medical or financial information about individuals are classified as sensitive and the corresponding laws specify who can collect and process sensitive information about a person. The financial services industry has rich sources of confidential financial datasets which are vital for gaining significant insights. However, the use of this data requires navigating a minefield of private client information as well as sharing data between independent financial institutions, to create a statistically significant dataset. A major challenge that many organizations faces, is how to address data privacy regulations such as CCPA, GDPR and other emerging regulations around the world, including data residency controls as well as enable data sharing in a secure and private fashion. We will present solutions that can reduce and remove the legal, risk and compliance processes normally associated with data sharing projects by allowing organizations to collaborate across divisions, with other organizations and across jurisdictions where data cannot be relocated or shared. We will review solutions that are driving faster time to insight by the use of different techniques for privacy-preserving computing including k-anonymity and differential privacy. We will discuss multi-party computation where the data donors want to securely aggregate data without revealing their private inputs. We will also review industry standards, implementations, key management and case studies for hybrid cloud (Amazon AWS, MS Azure and Google Cloud) and on-premises.