► VIDEO | Privacy-Preserving Approximate k-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns

► VIDEO | March 26th, 2021 | 12:00pm – 1:00pm EDT

Tianxin Tang,
Ph.D. Candidate, Computer Science at Georgia Tech

Cybersecurity Virtual Lecture Series
Co-sponsored by the School of Cybersecurity and Privacy and the Institute for Information Security and Privacy


This talk examines the problem of privacy-preserving approximate kNN search in an outsourced environment — the client sends the encrypted data to an untrusted server and later can perform secure approximate kNN search and updates. We design a security model and propose a generic construction based on locality-sensitive hashing, symmetric encryption, and an oblivious man. The construction provides very strong security guarantees, not only hiding the information about the data, but also the access, query, and volume patterns. 

Speaker Bio:

Tianxin Tang is a Ph.D. candidate in Computer Science. She is interested in privacy-preserving techniques from the provable-security perspective, and her research primarily focuses on encrypted databases.