Compacting privacy-preserving k-nearest neighbor search using logic synthesis

By Ebrahim M. Songhori, Shehzeen Samarah Hussain, Ahmad-Reza Sadeghi, Farinaz Koushanfar. 2015

40 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Nitin Agrawal
2. Ali Shahin Shamsabadi
3. Adrià Gascón
4. Matt J. Kusner
5. Borja Balle
6. Phillipp Schoppmann
7. Peter Kairouz
8. Badih Ghazi

Citations in the corpus

• QUOTIENT: Two-Party Secure Neural Network Training and Prediction (2019, methodology): “These trade-offs lead to a natural idea recently exploited in several works in the secure computation for ML: one should design protocols that are customized to full algorithms, such as the training of linear/logistic regressors and DNNs [14, 41, 44], matrix factorization [43], or k-nearest neighbor”
• Private Nearest Neighbors Classification in Federated Databases (2018, background): “Finally, another trend of related work is in privacypreserving nearest neighbors computation (Qi & Atallah, 2008; Songhori et al., 2015; Elmehdwi et al., 2014; Riazi et al., 2016; Li et al., 2015; Rong et al., 2016).”