Dae-Ho Kim

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Dae-Ho Kim trusts overall

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

  1. Ú. Erlingsson
  2. Arvind Narayanan
  3. Cynthia Dwork
  4. Giulia C. Fanti
  5. Frank McSherry
  6. Vasyl Pihur
  7. Zhan Qin

Papers from Dae-Ho Kim's perspective

Ordered by how strongly the researchers Dae-Ho Kim trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Calibrating Noise to Sensitivity in Private Data Analysis
  2. Differential Privacy
  3. Randomized response: a survey technique for eliminating evasive answer bias.
  4. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
  5. Privacy integrated queries: an extensible platform for privacy-preserving data analysis
  6. Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries
  7. Differential privacy via wavelet transforms
  8. Robust De-anonymization of Large Sparse Datasets
  9. Incognito: efficient full-domain K-anonymity (ACM SIGMOD Conference)
  10. Privacy: Theory meets Practice on the Map

Their papers