162 citations · 210 across the 3 of their papers we have counts for
4 papers
IDFace: Face Template Protection for Efficient and Secure Identification
Sunpill Kim, Seunghun Paik, Chanwoo Hwang +3
As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteri…
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
Seewoo Lee, Garam Lee, Jung Woo Kim +2
Transfer learning is a de facto standard method for efficiently training machine learning models for data-scarce problems by adding and fine-tuning new classification layers to a m…
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
Ning Wang, Xiaokui Xiao, Yin Yang +5
Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP,…
Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy
Thông T. Nguyên, Xiaokui Xiao, Yin Yang +3
Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data. However, doing so raises privacy concerns, and ris…