Privacy-preserving Techniques with Applications in Biomedical Data and Other Areas

Data privacy becomes a major concern of the general public and governments. On the other hand, it is important for researchers and industries to collaborate by contributing their data. A critical question is how to integrate data from multiple parties for analysis while protecting the privacy and confidentiality of the data. A trivial solution is to "anonymize" the data before sharing. But there is no perfect solution for anonymization. In this talk, we try to tackle the problem from another perspective. That is, we encrypt the data and try to perform computation on encrypted data without decryption, i.e., without looking at the raw data, we try to compute useful information from encrypted data provided by multiple parties. We will provide an overview how this can be done and also show some applications, such as biomedical applications and blockchain, that can leverage these techniques.