John Robert Wernsing

Artificial Intelligence · 2016

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

Whom John Robert Wernsing trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Vinod Vaikuntanathan
  2. Craig Gentry
  3. Zvika Brakerski

Who trusts John Robert Wernsing overall

  1. M. Sadegh Riazi

Papers from John Robert Wernsing's perspective

Ordered by how strongly the researchers John Robert Wernsing trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Efficient Fully Homomorphic Encryption from (Standard) LWE
  2. ON DATA BANKS AND PRIVACY HOMOMORPHISMS
  3. Can homomorphic encryption be practical?
  4. A privacy-preserving protocol for neural-network-based computation
  5. On Private Scalar Product Computation for Privacy-Preserving Data Mining
  6. On the Computational Efficiency of Training Neural Networks
  7. On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption
  8. The mnist database of handwritten digits
  9. Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme
  10. Encrypted statistical machine learning: new privacy preserving methods

Their papers